Journal of Applied Engineering and Technological Science (JAETS)

On The Assembly Line Balancing Problem: A Simplified Perspective With The Precedence Matrix

2024-09-07T13:58:11+07:00

The assembly line balancing problem (ALBP) has been an attractive research area for decades; however, the industrial application of the research findings remains limited. This can be attributed to the complexity of solution methods, restrictive assumptions, and the numerous variants of the problem in the real-world settings. This article describes using the precedence matrix as the basis for developing simplified, more practical analysis frameworks for the ALBP. We introduce algorithms to construct the precedence matrix from basic assembly problem data and implement it within a spreadsheet model, and to utilize this matrix in assigning the assembly tasks to workstations while ensuring assignment feasibility. The structure and potential uses of the precedence matrix-based framework are presented in detail. Furthermore, we compare selected line balancing priority rules using the proposed framework to demonstrate its effectiveness. The results suggest that the precedence matrix model is a straightforward, efficient tool for line balancing analysis, that can offer substantial support to both industry practitioners and academic researchers.

Mobile Tourism Research and Practices: A Comprehensive Bibliometric Exploration and Future Study Direction

2024-07-07T15:37:27+07:00

Even though mobile tourism analysis is developing, there is still a lack of literature regarding its formation patterns which makes the picture of its evolution incomplete. The evaluation in this study was carried out in general by emphasizing the evolution of exploratory analysis on the theme of "mobile tourism". This article aims to determine the increasing development of mobile tourism in terms of the number of publications, journals, authors, and the most relevant citations. Then analyze the itinerant tourism distribution map based on grouping, with merging, joint citation, collaboration network analysis, and joint word assessment, and interpret the conceptual structure of the country's co-occurrence and collaboration network regarding its visual representation. The method used is a descriptive approach through bibliometric analysis. An in-depth search of the Scopus database and RStudio software was conducted, screening, analyzing, and interpreting 752 relevant articles from 2014 to 2023. The findings show that an increase in the development of mobile tourism is identified from an increase in the number of publications, the most relevant journals, and authors, as well as the increasingly diverse origins of the authors even though the number of annual average citations has decreased slightly. Recommended six themes in mobile tourism studies, namely Tourism, Technology Acceptance Models (TAM), Mobile Applications, Recommendation Systems, Loyalty, and Cultural Tourism, and produced Technology Acceptance Models and Tourism as important studies with great prospects in the future.

Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers

2024-10-23T21:17:09+07:00

The banking sector faces significant challenges in effectively promoting its products and services. While direct marketing has proven to be a potent tool for customer acquisition, it often leads to customer dissatisfaction, thereby tarnishing the bank's reputation. Leveraging Business Intelligence (BI) technology offers a strategic advantage by enabling the classification and analysis of customer data, particularly for time deposit customers. This study presents the development and optimization of an Ensemble Least Squares (ELS) algorithm to enhance the classification of potential deposit customers. The proposed Ensemble Least Squares Support Vector Machine (ELS-SVM) algorithm demonstrated superior performance compared to traditional SVM and LS-SVM methods. Notably, the ELS-SVM achieved an average performance improvement of 10.04% over standard Support Vector Machine (SVM) techniques.

Pre-trained BERT Architecture Analysis for Indonesian Question Answer Model

2024-04-25T08:47:01+07:00

Developing a question-and-answer system in Natural Language Processing (NLP) has become a major concern in the Indonesian language context. One of the main challenges in developing a question-and-answer system is the limited dataset, which can cause instability in system performance. The limitations of the dataset make it difficult for the question-and-answer model to understand and answer questions well. The proposed solution uses Transfer Learning with pre-trained models such as BERT. This research aims to analyze the performance of the BERT model, which has been adapted for question-and-answer tasks in Indonesian. The BERT model uses an Indonesian language dataset adapted specifically for question-and-answer tasks. A customization approach tunes BERT parameters according to the given training data. The results obtained; the model is improved by minimizing the loss function. Evaluation of the trained model shows that the best validation loss is 0.00057 after 150 epochs. In addition, through in-depth evaluation of the similarity of question texts, the BERT model can answer questions measurably, according to existing knowledge in the dataset.

An Enhanced Image Segmentation Technique-Based on Motion Detection Algorithm

2024-10-26T08:07:50+07:00

This paper presents a prototype for an intelligent, self-contained theft detection system designed for small-scale applications. Utilizing a Raspberry Pi 3 as the core processing unit, the system employs a motion-detecting camera to monitor a defined area, recording and securely archiving video data on a cloud server upon detecting movement. This cloud-based repository supports real-time analysis, ensuring that data remains available for future reference. Battery-powered configuration enhances the system’s portability, making it adaptable across various environments, such as healthcare for patient monitoring or wildlife tracking for behavioural studies. The design aligns with IoT principles, featuring autonomous operation and cloud connectivity, offering a scalable, flexible solution capable of integration into larger IoT ecosystems for diverse surveillance applications.

Air-Gap Reduction and Antenna Positioning of an X-Band Bow Tie Slot Antenna on 2U CubeSats

2024-10-26T09:42:20+07:00

In this research work, a small size and wide-band Bow Tie slot antenna (BTSA) is proposed and optimized for use on an unlimited lifetime small-sized CubeSats at X-band. Interestingly, this paper introduces a graceful mechanism of integrating Bow Tie slot antennas on the bodies of small CubeSat configurations, which minimizes the antenna throwing from the satellite body and the whole CubeSat volume. The proposed approaches propose and analyze in detail how a small metallic part of a 2U CubeSat body improves the antenna performances around an operating frequency of 8.4 GHz. It maximizes the antenna gain simultaneously with the beamwidth angles at 8.4 GHz by suppressing the resulting back-lobes, which are re-directed outside the CubeSat box. These impacts are achieved by shifting a very small air-gap distance of only 1 mm between the back face of the BTSA dielectric and the CubeSat top face. The developed BTSA is lightweight and exhibits a unidirectional radiation pattern with a wide beamwidth angle of about 160° and a high gain of about 11.0 dBi at 8.4 GHz. The overall results with occupied size and volume are satisfactory for unlimited lifetime CubeSat missions at X-band such as UM5-Ribat and UM5-EOSAT of University Mohammed V in Rabat.

Scene Text Detection and Recognition Using Maximally Stable Extremal Region

2024-10-21T22:04:16+07:00

In recent years, scene text detection and recognition have become important research areas in computer vision and machine learning. Traditional text detection and recognition methods may struggle with detecting and recognizing text in images with low resolution, complex backgrounds, and varying font sizes. The proposed methodology addresses these challenges by combining multiple algorithms and using deep learning techniques. In this paper, we propose a method for scene text detection based on Maximally Stable Extremal Regions (MSER) combined with Stroke Width Transform (SWT) and recognition using Convolutional Recurrent Neural Networks (CRNN). Our method consists of two stages: text detection and text recognition. To detect text, we use MSER and SWT to extract candidate text regions from the input and then, we eradicate non-text regions using image to image translation. Finally, to recognize text, CRNN is used to recognize the text present in the detected regions. Our CRNN architecture consists of convolutional and recurrent layers, which enable us to capture both spatial and temporal features of the text. The methodology is evaluated on various benchmark datasets and has obtained good results with accuracy of 96% when compared to existing methods.

Multimodal Analysis of Augmented Reality in Basic Programming Course: Innovation Learning in Modern Classes

2024-10-08T09:26:43+07:00

The purpose of this research was to examine the effectiveness of Augmented Reality when implemented as a modern approach in a basic programming course, to solve problems in learning abstract concepts of programming. This vision places AR as a rich solution to programming education in the digital era, given the potential of the medium in delivering a deeper understanding of programming constructs in the context of complex and engaging visualizations. In this experimental study, a multimodal analysis and the ASSURE model were used in incorporating AR in a contemporary classroom. There were 61 students who took the basic programming course for students and all of them were included in the study. Self-completed questionnaires were used to record the impact of AR on the students, specifically on the understanding of the concept, motivation to learn and learning engagement. The study brought out the following key discovery; Ar enhanced students’ comprehension of the basic concepts in programming, enhanced their interest and encouraged them through the creative design of the learning process. AR enhanced some aspects of programming elucidated by student, particularly turning the complex programming strategy into an easily manageable model as well as providing for a real-time simulation and graphical rendering, all of which were difficult. Still, and importantly, AR cut down on mistakes and provided a clear understanding of the coding patterns Iraq required. Applying AR into the learning of programming education has practical implications in improving students learning experiences as well as simplifying existing concepts. In theory, this work entails the effectiveness of Augmented Reality in programming education and the exploration of opportunities for application of new ideas in technology-enabled teaching in the information age.

Software Design for Inventory Management Improvement in a Peruvian National University

2024-11-06T11:52:15+07:00

The purpose of this study was to design a software to optimize inventory management in the Office of Information Technology and Communications (OTIC) of the Faculty of Industrial Engineering and Systems of the Universidad Nacional del Callao, Peru. The research arose due to the lack of an efficient system that minimizes asset losses and reduces operational errors in the university context, where resource constraints demand sustainable and adaptable solutions. To address this problem, an agile methodological approach was used, implementing the Scrum methodology in the software design, as well as an architecture based on MVC (Model-View-Controller) with technologies such as MySQL, PHP and CodeIgniter. The evaluation of the system included usability surveys to users and an expert judgment, focused on three dimensions: Reliability, Usability and Design. The results of the study showed a high acceptance of the system in terms of ease of use and user satisfaction, highlighting the effectiveness of the modular design for future extensibility and performance improvements. This study provides theoretical value by highlighting the application of agile methodologies in educational environments and highlights the importance of a scalable design to improve inventory management in academic institutions. In practical terms, the implementation of this software offers an adaptable and scalable solution, with potential for replication in other universities, optimizing resource management and strengthening operational efficiency in the educational sector. The main contribution of the study lies in the combination of a flexible and low-cost design, aimed at meeting the specific needs of inventory management in educational environments with budgetary constraints.

Environmental Quality Impact Analysis of Settlements Bontang Kuala, East Kalimantan Province

2024-10-26T12:23:46+07:00

The present study seeks to elucidate the multifaceted impacts of resettlement programs on various dimensions of social and economic life within the community. The primary objectives of this investigation are threefold: 1) to assess the influence of resettlement programs on the intensity and quality of social relations; 2) to conduct a comprehensive analysis of the socioeconomic ramifications of resettlement programs; and 3) to evaluate the behavioral patterns associated with the utilization of bathing, washing, and defecation facilities. The research methodology employed in this study is based on secondary data sourced from “Bontang Dalam Angka 2023” and primary data collected through questionnaires administered to local fishermen. A random sampling technique was employed to select a sample of 50 families. The data collected, encompassing various research variables, were analyzed using descriptive analytical methods, t-tests, and Product Moment Correlation. The findings of the study indicate a significant positive impact of resettlement programs on social relations within the community. Families that promptly moved into their new residences reported enhanced levels of social interaction and engagement. Additionally, the resettlement programs demonstrated a positive influence on the socioeconomic conditions of the community, particularly in terms of the expansion of educational opportunities and infrastructural improvements. Notable improvements in learning outcomes and increased access to educational facilities emerged as key positive outcomes. Furthermore, the study reveals a marked improvement in social behaviors related to sanitation practices, particularly in the use of bathing, washing, and defecation facilities, among individuals participating in the resettlement programs. A heightened adherence to hygienic sanitation practices was observed, signifying the positive behavioral shifts prompted by the resettlement initiatives. In conclusion, the evidence suggests that resettlement programs exert a favorable influence on both the social and economic aspects of community life, while also fostering improved sanitation practices. This research contributes meaningfully to the broader understanding of the complex impacts of resettlement programs on individual and communal life.

Does Social Presence on Social Commerce Platform Attract Buying Intention of Indonesian Local Food?

2024-10-15T09:16:51+07:00

Studying social commerce and consumer behavior in Indonesia is crucial due to the country's rapid digital transformation and unique cultural landscape. This study aims to investigate the effects of social commerce on consumer trust and buying intentions in Indonesia’s local food sector. This study uses a quantitative approach through hypothesis testing. Data was collected using questionnaires and purposive sampling techniques. A survey of 240 respondents was conducted, and data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM). Results indicated that social commerce significantly influences trust in sellers and buying intention, while social support was not significantly affected. Social support and trust significantly influence purchase intention. However, the effect of social commerce on social support and trust in sellers through social presence moderation is not proven. This study highlights the role of social commerce in building consumer trust, though its effects on social support remain inconclusive. This research is interesting for developing social commerce to attract buyers' interest in local Indonesian food

Interactive Geographic Visualization and Unsupervised Learning for Optimal Assignment of Preachers to Appropriate Congregations

2024-09-21T18:13:04+07:00

Riau Province has a population of 6,642,874 and a diverse geography, which poses significant challenges in optimizing Islamic preaching activities. Traditional assignment methods often lead to inefficiencies due to misalignment between the preacher’s expertise and congregational needs, as well as logistical issues. This study integrates K-Means clustering and DBSCAN algorithms with interactive geographic visualization to optimize the assignment of preachers to mosques. We collected 435 data points, including 185 mosques and 250 preachers. K-Means was evaluated using the Elbow Method and Silhouette Score, identifying 10 clusters as optimal with a Silhouette Score of 0.435654. However, K-Means does not handle outliers effectively, as indicated by zero outliers in all configurations. DBSCAN was tested with various epsilon (eps) and minimum sample values. The optimal configuration with eps of 1.5 and 5 minimum samples resulted in 10 clusters with a Silhouette Score of 0.381108 and 60 outliers. DBSCAN effectively manages outliers and varying densities. Although K-Means is advantageous for its simplicity and higher Silhouette Scores, it is unable to handle outliers effectively. DBSCAN provides robust clustering for noisy data. Therefore, it can be concluded that hybridizing unsupervised learning algorithms with geographic visualization can potentially improve the effectiveness of preaching activities in Riau Province and enhance preacher assignment.

Investigating Tensile Strength in SLA 3D Printing Enhancement Through Experimentation and Finite Element Analysis

2024-08-11T08:47:51+07:00

This study aims to optimize tensile strength in Stereolithography (SLA) 3D printing by investigating the effects of print orientation and layer orientation on mechanical properties. Employing a multilevel factorial design, we systematically analyzed various print andlayer orientations using both experimental and computational methods. The experimental component, performed with an Instron 5566 machine, identified a print orientation of 22.5 degrees and side orientations as optimal, achieving a tensile strength of 72.01 MPa. Computational simulations using ANSYS software supported these findings, showing a close correlation between experimental and simulated results. This research not only advances theoretical understanding of SLA 3D printing processes but also offers practical insights for optimizing tensile strength in manufacturing applications. By integrating experimental and finite element analysis (FEA) results, which predicted a maximum stress of 71.97 MPa under a 686 N load, the study contributes valuable knowledge for enhancing additive manufacturing practices and informs future research on parameter optimization

Sara Detection on Social Media Using Deep Learning Algorithm Development

2024-09-16T22:40:13+07:00

Social media has become a key platform for disseminating information and opinions, particularly in Indonesia, where SARA (Ethnicity, Religion, Race, and Intergroup) issues can fuel social tensions. To address this, developing an automated system to detect and classify harmful content is essential. This study develops a deep learning model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to detect SARA-related comments on Twitter. The method involves data collection through web scraping, followed by cleaning, manual labeling, and text preprocessing. To address data imbalance, SMOTE (Synthetic Minority Over-sampling Technique) is applied, while early stopping prevents overfitting. Model performance is evaluated using precision, recall, and F1-score. The results demonstrate that SMOTE significantly improves model performance, particularly in detecting minority-class SARA comments. CNN+SMOTE achieves a accuracy of 93%, and BiLSTM+SMOTE records a recall of 88%, effectively capturing patterns in SARA and non-SARA data. With SMOTE and early stopping, the model successfully manages class imbalance and reduces overfitting. This research supports efforts to curtail hate speech on social media, especially in the Indonesian context, where SARA-related issues often dominate public discourse.

Design of Comprehensive Monitoring on Hybrid Photovoltaic and Thermoelectric Generator Using IoT

2024-09-19T09:16:30+07:00

The study aims to design and develop a more efficient measurement monitoring system based on influential parameters for the performance of hybrid PV and TEG modules using IoT with the Thingspeak application. The parameters measured include the PV top and bottom surface temperatures and output current and voltage, the surface temperatures of the TEG's hot and cold sides, their respective current and voltage outputs, air humidity, and solar intensity. This IoT-based monitoring system experimental method utilizes two types of PV, polycrystalline and monocrystalline, each rated at 50 Wp, in a hybrid configuration with 5 TEG modules attached to the back of the solar panel. The monitoring design results indicate that sensor measurements were accurate and data readings were reliable. The temperature difference between the two sides of the TEG was measured up to 21.7°C, and the hybrid efficiency of the monocrystalline PV with TEG showed better results compared to the polycrystalline setup, achieving optimum efficiency above 6%, with the PV surface temperature maintained at an average of 50°C. Additionally, IoT monitoring revealed the effect of air humidity on TEG performance: lower air humidity resulted in a larger ?T, peaking at 21.7°C with humidity at 39.1%. Therefore, this study recommends using IoT technology for observational data collection on system performance, particularly for PV and TEG, with sensor component modifications tailored to the characteristics of the targeted sensor object.

Examining Factory Process Safety Management Systems And Studying Lean Improvement Methods

2024-07-25T17:23:55+07:00

Many factories have set up a safety management system (PSM), and there is an increasing rate of safety management systems. This is increasingly in line with the development of industrial plants. External and internal inspection of security management processes are the core of Process Safety Management (PSM) and are an essential opportunity to improve operational solutions. The problems of security inspection arise due to unclear and unplanned inspection issues, resulting in unsystematic monitoring of factual information and opinions and loss of opportunities to achieve goals throughout the organization. This research aims to improve the inspection process by using computer software technology in auditing. After improving the process according to the Lean Process Management (ECRS) concept to reduce waste, the workflow was reduced by 40.74%, and 60% efficiency was increased. In addition, the assessment results from a sample of PSM auditors showed 93.4% satisfaction as the most appropriate. Therefore, this study demonstrated the potential and benefits of implementing the Process Safety Management System (PSM) audit program and applying Lean theory to improve auditing, which can be used as an essential reference for future research and program development.

Waste Analysis of the International Delivery Service Process Using Lean Six Sigma in Logistics Service Provider Companies

2024-09-19T15:52:33+07:00

International shipping poses challenges such as customs clearance delays, slow delivery speeds, and process bottlenecks, leading to time wasstage, increased costs, and potential profit losses. These issues impact specific processes and customer satisfaction. Lean Six Sigma offers a robust framework for identifying and eliminating waste in international shipping processes. Applying the lean service approach, we identified four key waste types in international shipping: delay, duplication, unnecessary movement, and error. However, a Pareto diagram highlighted delay and error as critical culprits, with sigma values of 1.85 for delay and 2.92, 3.46, 3.77 for the three error types, respectively. These low sigma values indicate significant room for improvement After conducting an impact analysis and failure mode analysis (FMEA), four improvement recommendations were proposed: the implementation of an online shipping form filling system, providing customs training to all counter employees, and creating SOPs for inspection processes at the warehouse. These recommendations form a roadmap for international shipping companies to embrace Lean Six Sigma, enhancing efficiency, reducing costs, and improving customer satisfaction.

Optimizing Low-Pressure Die Casting of A356 Aluminum Alloy Wheel Rims: A Novel Side Core With Integrated Cooling System

2024-08-13T21:43:50+07:00

This study explores the development of a novel side core design with an integrated cooling system for the low-pressure die casting (LPDC) of A356 aluminum alloy wheel rims in Indonesia. The objective is to reduce cycle time and minimize casting defects. The methodology involves 3D modeling using SolidWorks and simulations performed with MAGMAsoft to evaluate filling and solidification behavior, air pressure, and air entrapment. Notably, this is the first application of a cooling system in Side cores within the Indonesian automotive industry, motivated by the need to improve production efficiency and product quality, as current manufacturing processes suffer from long cycle times and high defect rates. Simulation results show a significant reduction in cycle time by 72 seconds, improving both efficiency and product quality. This study demonstrates the effectiveness of integrating cooling systems into side cores to enhance the LPDC process.

Big Data and Netnography Analysis of Mental Health on Twitter/X: Evidence From Indonesia

2024-10-08T09:24:14+07:00

Digital traces of mental health conversations on social media X have become a new culture, especially for the young generation. This study aimed to explain the network, themes and comment of social media discussion posts about mental health in Indonesia. This study has focused on each conversation in the form of tweets using big data analysis from posts with containing keyword related and specific criteria on the theme of mental health. This research was collected 15,200 mental health-themed discussions on X. Further, the researcher conducted a selection based on engagement and specific criteria through the NoLimit tool in 2021. A total of 4969 threads were analyzed further. As a result, researchers found a network that formed 3 clusters, namely the Stress cluster, Depression cluster, and Bipolar cluster. Apart from that, three big themes were found that represent every conversation on social media X in Indonesia, namely, (1) The Story of Survivors, (2) Mental Health as a Trend and Fashion, (3) Stress, Anxiety, or just a Gerd? Through the study of netnography, it is revealed that social media serves as a community's backbone while also perpetuating stigma related to mental health issues. Mental health in Indonesia is still an issue that does not get serious attention, even though the number of cases of mental disorders continues to increase.

Multimedia Transmission Technique for Smart Ambulance with multi-carrier OFDM in a V2V and V2I Channel model using Software Defined Radio Technology

2024-10-04T21:14:21+07:00

This research explores the implementation of a cutting-edge Software Defined Radio (SDR) framework to transmit multimedia files that can be assumed to be medical data in smart ambulances. The system utilizes multi-carrier Orthogonal Frequency-Division Multiplexing (OFDM) across V2V and V2I channels. The research is based on the notion that adaptive real-time communication is essential for the uninterrupted supply of key patient data to medical facilities and vehicles in transit, in order to address the problems posed by high mobility and dynamic environmental conditions. A comprehensive SDR system has been constructed and assessed in comparison to conventional communication mechanisms, demonstrating notable advancements in data accuracy and uninterrupted transmission. Our system successfully established stable connections in V2I channels, even in the presence of environmental obstacles. It maintained average power levels of approximately 32.074 dBm and a Peak-to-Average Power Ratio (PAPR) of 1.037 dB. These results indicate a constant signal envelope that promotes optimal signal transmission with excellent fidelity. In V2V scenarios, we successfully maintained data integrity with a low Peak-to-Average Power Ratio (PAPR) of 3.316 dB, even while vehicles were moving at a speed of 20 km/h. Additionally, we secured a high likelihood (94.5%) that the signal power remained close to the average, showing the robustness of our system against Doppler effects and signal dispersion. Text transmissions experienced errors when subjected to a Doppler shift of 20 km/h, which impacted the decoding of the received text. Similarly, image transmissions revealed limitations in bandwidth, as a transmitted image of 3640 KB was received with a degraded 4 KB. This emphasizes the importance of implementing effective error handling and recovery mechanisms. The results illustrate the efficacy of the suggested system in maintaining a high Quality of Service (QoS), offering proof of the effectiveness of contemporary wireless communication technologies in improving emergency medical services and setting new standards in smart ambulance capabilities.

Performance of Corrosion Behavior of Commercial Magnesium Alloy Anode Electrode in Seawater-Powered Lamps

2024-09-07T13:01:31+07:00

The development of environmentally friendly alternative technologies, such as seawater batteries, is increasingly important in addressing energy needs. This study aims to analyze the corrosion behavior of a magnesium alloy anode electrode used in seawater batteries with varying salinity levels. Five commercial anode electrode samples were prepared, each immersed in a salt solution with different concentrations: 14g, 16g, 18g, 20g, and 22g in 380 ml of distilled water. Potentiodynamic testing was conducted to measure the corrosion potential (Ecorr), corrosion current (Icorr), and corrosion rate. The results showed that an increase in salt concentration significantly increased the corrosion rate. The sample with the highest salt concentration (22g) produced a corrosion potential of 1.5419 Volts, a corrosion current of 0.0010 Amps/cm², and a corrosion rate of 12.5850 mm/year. These findings indicate that the corrosion rate increases with higher salinity. The study concludes that there is a positive correlation between salinity and corrosion rate, with future research expected to focus on additional treatments to improve electrode performance.

Crowd Speaker Identification Methodologies, Datasets And Features: Review

2024-07-29T18:49:49+07:00

Crowded speech or Overlapping speech, occurs when multiple individuals speak simultaneously, which is a common occurrence in real-life scenarios such as telephone conversations, meetings, and debates. The critical task in these situations is to identify all the speakers rather than just one. Overlapping speech identification is a significant research domain with applications in human-machine interaction, criminal detection in airports, trains, and public spaces. Our work examines crowd speech identification from four perspectives, including the most commonly used datasets, the most effective features for crowed speaker identification, and the best methodologies employed, and the highest results gained. This study proposes a comprehensive survey of research on crowd speech identification, covering the period from 2016 to present. The survey includes ninety research papers, fifty of which, are empirical studies. Initially, statistical methods were predominant, but the current trend leans towards artificial intelligence, particularly deep learning, which has demonstrated considerable efficacy in this field.

Post-Disaster Engineering Strategy for Anai River Debris Flow Management on Anai Valley National Road West Sumatra Indonesia

2024-09-16T22:59:10+07:00

One of the causes of flash floods is eruption material from Mount Marapi that is carried downstream, disrupting transportation access and the local economy. This study aimed to design and implement an effective post-disaster engineering strategy for debris flow management in the Anai River and evaluate its long-term success. Data were collected through field observations to measure river profiles, photogrammetry, and sediment sampling. Secondary data were used to calculate rainfall intensity and flood discharge in the Anai River to plan debris flow control. The study results showed that the large catchment area and high rainfall contributed significantly to the high peak discharge. Disturbed soil samples taken from the river surface were saturated, indicating the influence of sediment from the debris flow from the eruption of Mount Marapi. The removal of material from the riverbed needs to be controlled to avoid overexploitation that could exacerbate erosion of the riverbanks, ultimately threatening bridge structures and other infrastructure along the Anai River. To overcome this problem, it is necessary to build sediment control structures such as check dams and groundsills, as well as secure riverbanks in the management of debris flows in the Anai River.

Account Login and Database Access Control System with Time Attendance Through Facial Recognition

2024-08-11T08:37:00+07:00

As digital technology accelerates at an unprecedented pace, educational institutions and businesses are looking for more efficient and accurate ways to record attendance. Traditional attendance methods, such as manual signing or manual data entry, can face several challenges, including human error, inaccuracy, and time consumption, ultimately leading to decreased efficiency and productivity. With the increasing demand for smarter and more efficient solutions, it has become imperative to explore new technologies that can improve this vital process. The main problem is that traditional attendance systems are unable to keep up with the speed, accuracy, and security demands of the modern era. These systems are often prone to errors, which can result in incorrect or inaccurate attendance, and the attendance process can be time-consuming, especially in large groups, reducing time allocated for educational or productive activities. This study aims to develop an automated attendance system using real-time face detection technology, with the aim of improving efficiency, accuracy, and security compared to traditional attendance methods. Haar Cascades technology was used to recognize and classify faces, with attendance data stored in a MySQL database managed via SQL. The results of the study showed an accuracy rate of up to 97.44% in detecting faces and recording attendance under different conditions. It is proposed to enhance this system in the future by integrating anti-impersonation and mask detection technologies and using deep learning principles to increase security and effectiveness in different scenarios.

Feasibility Assessment of a Sustainable Building Applied Photovoltaic (BAPV) System With Solar Tracking Features Based on Techno-Economic Criteria: Malaysia Case Study

2024-08-15T10:22:37+07:00

In line with the increase attention regarding building applied photovoltaic system, the research in enhancing its performance is also critical to be explored, one of them is the utilization of solar tracking system. The goal of this paper is to analyze the feasibility of using a tracking system for increasing the PV output power and analyzing the energy sustainability aspects on a building applied photovoltaic system. A feasibility study considering three scenarios of PV capacities and propose two options in either renewing or reusing the PV panels to extend its life time. Based on the results, the tracking system contributes to the improvement of 18% in the energy generation. On the technical aspects, renewing the PV panels with a large capacity of up to 30 kW-peak is the most feasible option to meet the electricity demand on the building. In the meantime, the reuse option with large capacity is the best with the net present value is about USD 24,639.22 or 34% more than the option of renewal the PV panels. In summary, reusing the panel is beneficial from the economic and environment standpoints as it represents sustainable development of power generation. Since the realistic feasibility cases and analyses used, the novelty in terms of the approach is beneficial for the future investigations on adding a tracking system, keeping or replacing the PV system for sustainable aspect while improving its output power.

Effects of Movement Restrictions on Consumer Consumption

2024-05-01T13:20:23+07:00

This study addresses the impact of movement restrictions, particularly during pandemics, on global consumer behavior, with a focus on grocery shopping. Despite the widespread effects, research on these changes remains limited, prompting the need to investigate hidden purchase patterns contributing to economic growth. Employing secondary data analysis, the study explores the effects of movement restrictions on customer consumption patterns, using K-means cluster analysis to identify distinct consumer segments. The findings highlight significant impacts on purchasing power, item prices, and consumption behavior, with a notable increase in item prices during the post-restriction period followed by a subsequent decline influenced by economic factors such as financial uncertainty and shifting priorities. This research contributes by shedding light on the dynamic nature of consumer behavior during and after movement restrictions, offering valuable insights for policymakers and businesses navigating the post-restriction economic landscape and informing strategies for economic recovery and growth.

Proposed Framework for Halal Value Chain Analysis in Batik Industry

2024-09-06T18:07:29+07:00

This paper aims to apply the halal value chain to Tanah Liek Batik textiles to become halal batik products, using a framework designed to analyze the halal value chain. The method used in dealing with this problem is to apply HCCP (Halal Critical Control Point) to the company's activity value chain to become a halal value chain. The initial data used is a flow diagram of the production process of four types of Tanah Liek Batik products: Tanah Liek dyed batik, chemically dyed batik, stamped batik with soil-like dye, and chemically dyed batik. Based on the results of the identification of haram materials in each process in the activity, the Halal Control Point (HCP) is obtained. The application of HCCP to the Tanah Liek Batik value chain has 13 critical points of haram products, which come from the process, facilities, and tools and materials used during the production process. This study also produces a Halal Value Chain framework in the Textile Industry. Applying this framework in the textile industry is expected to increase the number of halal-certified textile industries.

A Sustainable Hybrid Off-Grid System Design for Isolated Island Considering Techno-Economic and Frequency Stability Analysis

2024-07-25T17:09:45+07:00

Electrifying remote islands presents complex challenges. Currently, most remote areas in Indonesia rely on diesel fuel for their electricity supplies, contributing to escalating generation costs and environmental degradation. Aligned with the global net-zero emission goal, this study proposes the design of a hybrid off-grid system for Kabare Village in the Raja Ampat Islands, integrating techno-economic and frequency stability analyses. HOMER Pro was employed to identify the most optimal system configuration, while DIgSILENT PowerFactory was utilized to assess the frequency stability performance of the system. This study unveils that the optimal system combines existing generators, solar panels, and batteries, with a net present cost of $1.37 million. The optimal system delivers an 11.8% reduction in levelized cost of energy to $0.269/kWh, alongside a 25.6% decrease in both fuel consumption and greenhouse gas emissions compared to the existing system. Moreover, the system meets frequency stability metrics, even under extreme operational conditions. This study demonstrates that implementing a hybrid off-grid system in Kabare Village is not only technically and economically feasible but also a practical option. These findings are anticipated to assist the government in promoting the utilization of renewable energy sources, particularly in remote areas such as the islands of eastern Indonesia.

Effect of Zinc Addition in Copper to Structure, Hardness, Corrosion, and Antibacterial Activity

2024-11-06T14:19:21+07:00

Brass (CuZn) is widely used today due to better mechanical, thermal, and chemical properties. The present research fabricated CuZn alloy by adding various Zn (6, 9, and 12 wt.%) to the Cu using gravity casting. Casts CuZn alloy by adding various Zn to the Cu to investigate optimum composition were resulting highest inhibited of bacterial activity. In addition, the structure, hardness, and electrochemical behavior of the alloy were also investigated using XRD, Vickers hardness, and potentiostat equipment. XRD confirmed that CuZn alloy has an alpha phase, and a FCC crystal structure. The rise of the Zn content in the alloy led to an increase in crystallite size, a decrease in the hardness and a shift to a more negative OCP potential at 1200 s measurement. Enhancing the Zn content to 9 wt.% in the alloy lead to decrease the corrosion rate. Moreover, 24-hour post-contact observation found that the sample places removed remained clear of bacteria. The Cu6Zn sample successfully inhibited the growth of Escherichia coli in the 3^rd hour, while Staphylococcus aureus was 100 % reduced in the 7^th hour. The Cu6Zn sample could be used as an alternative material for medical equipment in ambulances.

Proximity Index Value for Supplier Selection Using Compromise Weighting of Stepwise Weight Assessment Ratio Analysis and The Method of Removal Effects Of Criteria: A Case Study in Indonesian Leather Industry

2024-10-24T09:41:36+07:00

Procurement of new raw materials is needed when product demand increases, and raw material suppliers must be determined to meet the company's needs. This research examines what criteria a company needs when selecting criteria using Delphi. The weighting of criteria cannot be separated from the element of the decision maker's subjectivity; therefore, it is necessary to compromise between subjective and objective criteria. Therefore, the study used The Method of Removal Effects of Criteria (objective weighting of criteria) and Stepwise Weight Assessment Ratio Analysis (subjective weighting of criteria) in weighting criteria. Then, considering the weight of the criteria, the Proximity Index Value (PIV) is used to evaluate and rate the suppliers. The offered methodology is applied to a real case study from a leather manufacturing company in Indonesia to verify its applicability with a sensitivity analysis performed on different scenarios. The findings indicated that the proposed model is dependable and that the rankings are resilient to fluctuations in the criterion weights.

Development of A Low-Cost Analyzer for Misalignment Identification Based on Vibration and Current Analysis

2024-09-18T20:25:39+07:00

This paper proposes a low-cost analyzer based on vibration and motor current signature analysis (MCSA) using the Arduino microcontroller. Misalignment identification on induction motors with disc coupling is considered a case study. Several methods for misalignment identification have already been conducted in previous research. However, there are still issues with making the identification more reliable and well-known for further investigation. This paper describes the design and development of an analyzer for misalignment identification that is easy to use and fast utilizing a low-cost Arduino microcontroller. Furthermore, this experimental rig also consists of several additional components such as; an induction motor, shaft, and bearing dan disk coupling. In this paper, misalignment distance variables were set in 0 mm, 0.5 mm, 1 mm, and 1.5 mm, and the motor speed was varied from 500 rpm to 1500 rpm with an increment of 100 rpm. The misalignment characteristic was experimentally achieved using an analyzer with two sensors: an accelerometer for vibration analysis and a current sensor for MCSA. As a result, a low-cost analyzer for misalignment has been successfully developed. The results show that misalignment was explicitly defined by dominant peak frequencies at 3X rpm for vibration analysis and side bands around the main frequency for MCSA. Moreover, side-band frequency increases by increasing the misalignment distance.

Development of Potato Nano Carbon as Electrode for Supercapacitors Achieves Green-Sustainable Development Goals

2024-10-19T11:49:42+07:00

The development of potato nanocarbon as an electrode from potatoes for supercapacitors will answer the energy needs that are needed and are continuously researched. This research was conducted in the laboratory by giving marinade treatment to potatoes and measured repeatedly and testing was carried out to determine the extent of the carbon content so that potatoes are very good to be used as electrodes with electroplating method. The results obtained were 0.8 grams of potatoes with a very high conductivity increase to 0.97 m S. The result TEM 160 nm after treatment an average size of 100 nm successfully coated on the surface of the particles. FTIR shows a wavelength of 3282.48 cm due to the width of potato absorption and overlapping with the C-H-O group. AAS shows the adsorption capacity of modified potato on metals occurs in the pH range 4.5-5.5. Analysis of potatoes to be made as electrodes with two positive and negative pieces showed better dispersion stability. The electrodes are made and have been tested using a smartphone. The acid in the potato forms a chemical reaction with zinc and copper and when electrons flow from one material to another energy is released.

Supply Chain Conceptual Model to Optimize a Local Food Agroindustry from the Coconut Milk Processing

2024-10-21T19:50:36+07:00

This study uses a soft system methodology to examine the coconut milk supply chain, which supports the local food agro-industry, particularly for rendang products. It aims to develop a fundamental concept to optimize this supply chain. The proposed method is effective for addressing complex, multi-stakeholder problems. Using systems thinking, real-world system behaviour is converted into a conceptual model, facilitating decision-making through customer, actor, transformation, worldview, owner, and environment components. The findings include a conceptual model translated into a causal loop diagram, part of a dynamic systems approach, which shows the interconnections and feedbacks among variables in the supply chain, such as raw material supply, logistics costs, production volume, and distribution costs. This stage identifies stakeholders and systematically illustrates the relationships in managing the flow of goods. It provides a basis for designing a supply chain network aligned with local government policies for developing the coconut milk agro-industry. It aims to optimize the local food industry, particularly rendang, in West Sumatra province. After creating the conceptual model, gap analysis and validation were conducted, comparing the model with real-world insights from stakeholders, including coconut farmers, producers, rendang manufacturers, academic institutions, and local governments. Validation focused on efficiency, efficacy, and effectiveness. The results demonstrate the foundational idea for designing the coconut milk agro-industry supply chain network to support local food products, as depicted in the causal loop diagram.

Innovative Tech-Savvy Education: Designing a Smart Assessment System

2024-10-08T09:25:38+07:00

The purpose of this research is to create a smart assessment system linked to a project-based learning model through a website. It is anticipated that this system will raise the proficiency and productivity of learning evaluation through the implementation of digital technology. The approach applied in this research includes the identification of needs, and the designing of the system, followed by software development and testing. According to the findings of the research, it is evident that the system developed for smart assessment can offer several complex and combined assessment facilities within the system, which would help the educators for easier examination of the student’s performance on a real-time basis. In addition to that, this system also enhances the common work of the students with the educators on completing the project assigned by the latter. Hence, the incorporation of website-based online assessments tries to extend the validity of the PBL implementation approach as well as contribute positively towards embracing positive enhancement in education.

Corrosion Rate of ASTM A53 Steel in Seawater Influenced by Variation in Concentration of Mangifera Indica L. Peel Extract

2024-08-15T22:34:24+07:00

This study investigates the effectiveness of mango peel extract as a corrosion inhibitor for ASTM A53 steel, which is widely used in the oil and gas industry. The research aims to evaluate how different concentrations of mango peel extract can mitigate corrosion in seawater from Pangandaran, thereby extending the lifespan of steel components in marine environments. Corrosion tests were conducted through immersion experiments over durations of 1, 4, 9, 16, and 25 days with mango peel extract concentrations of 0 ppm, 20 ppm, 40 ppm, 60 ppm, and 80 ppm. Analytical methods including X-ray diffraction (XRD), Optical Microscopy (OM), energy dispersive spectroscopy (EDS), and scanning electron microscopy (SEM) were used to examine the steel's surface morphology and chemical composition. The results demonstrate that mango peel extract significantly reduces the corrosion rate of ASTM A53 steel, with the highest efficiency achieved at 40 ppm (58.15%) and a notable reduction at 60 ppm (56.4%). The inhibition is attributed to chemical absorption, which lowers the steel's corrosion potential. These findings suggest that mango peel extract is an effective, eco-friendly corrosion inhibitor, offering practical and theoretical benefits for corrosion management. This research supports the use of bio-based inhibitors and may inform future industrial corrosion protection strategies.

A Combined MobileNetV2 and CBAM Model to Improve Classifying the Breast Cancer Ultrasound Images

2024-05-01T09:43:57+07:00

Breast cancer is the main cause of death in women throughout the world. Early detection using ultrasound is very necessary to reduce cases of breast cancer. However, the ultrasound analysis process requires a lot of time and medical personnel because classification is difficult due to noise, complex texture, and subjective assessment. Previous studies were successful in ultrasound classification of breast cancer but required large computations and complex models. This research aims to overcome these shortcomings by using a lighter but more accurate model. We integrated the CBAM attention module into the MobileNetV2 model to improve breast cancer detection accuracy, speed up diagnosis, and reduce computational requirements. Gradient Weighted Class Activation Mapping (Grad-CAM) is used to improve classification explanations. Ultrasound images from two databases were combined to train, validate, and test this model. The test results show that MobileNetV2-CBAM achieves a test accuracy of 93%, higher than the complex models VGG-16 (80%), VGG-19 (82%), InceptionV3 (80%), and ResNet-50 (84%). CBAM is proven to improve MobileNetV2 performance with an 11% increase in accuracy. Grad-CAM visualization shows that MobileNetV2-CBAM can better focus on localizing important regions in breast cancer images, providing clearer explanations and assisting medical personnel in diagnosis.

DCT and SVD Sparsity-Based Compressive Learning on Lettuces Classification

2024-08-18T15:36:59+07:00

Compressive Sensing (CS) technique in image compression represents efficient signal which offering solutions in image classification where the resources are constrained especially on a large image processing, storage resource, and computing performance. Compressive learning (CL) is a framework that integrates signal acquisition via compressed sensing (CS) and machine/deep learning for inference tasks directly on a small number of measurements, On the other hand, in real-world high-resolution (HR) data, where the image dataset is very limited CL, has the drawback of reduced accuracy under conditions of aggressive compression ratio. Here, a reconstruction method is necessary to maintain high levels of accuracy. To address this, we proposed a framework Deep Learning (DL) and Compressive Sensing that processing a small dataset of 92 images maintaining high accuracy. The framework developed in this paper employs processing sensing matrix A in compressive sensing with two transformation methods: DCT CL with Multi Neural Networks and the SVD method with GoogleNet framework. To maintain the same computation efficiency as DCT Compressive learning, SVD with GoogleNet framework provides a solution for object recognition, achieving accuracy values ranging from 89.47% to 63.15% for compression ratios of 3.97 to 31.75. This performance shows a linear tendency concerning the PSNR level, an index of signal reconstruction quality, and demonstrates an efficient process in the S matrix.

Deep Learning Techniques for MRI Image-Based Performance Analysis of Brain Tumor Classification

2024-11-07T19:34:19+07:00

Brain tumors can produce symptoms and indicators due to direct tissue death, localized invasion of the brain, or aftereffects from increased intracranial pressure. In order to identify images from the publicly available image dataset, this work combined multiple image feature sources using deep learning algorithms. The architecture of most classic convolutional neural networks (CNNs) consists of convolution modification and max-pooling of layers connected with several completely linked layers. The steps used in this system are pre-processing, segmentation, feature extraction, and classification. The preprocessing procedures of this investigation were used by the modified trimmed median filtering approach. U-Net segmentation is used to carry out the segmentation process. Features are then extracted using the wavelet transform method. In this study, MRI images of brain tumors, including meningnant and benign tumors, were detected and classified using the proposed CNN-based VGG16 model. The convolutional neural network (CNN) architectures employed in this investigation were guided by the VGG-16. The outcomes are assessed in terms of accuracy, precision, recall, and F1-score after the suggested model has been simulated. According on the test findings, the recommended approach may lead to 96.9% maximum recall, 97.4% maximum F1-score, 98.45% maximum accuracy, and 98.1% maximum precision.

Unlocking User Satisfaction: A Delone & Mclean Is Success Model Approach To It Helpdesk Ticketing System Adoption

2024-04-06T11:12:40+07:00

The IT Helpdesk Ticketing System application is used to submit requests for IT services and handle technical problems related to these services. This research aims to understand the factors that influence user satisfaction with this application, which is triggered by user dissatisfaction. The research method used is qualitative using the DeLone & McLean IS Success Model, which is implemented through the Partial Least Squares Structural Equation Modeling (SEM) framework. The research was conducted on 231 respondents who were surveyed online. The results showed that system quality, service quality, and level of system usage significantly affect user satisfaction. System and service quality contribute positively to user satisfaction, while the level of system usage also has a key role in determining the level of satisfaction. The implications of the findings emphasize the importance of paying attention to these aspects in system development and improvement, while increasing the level of usage to meet the needs and increase user satisfaction more effectively.

The Art of The Gacha Lure: Determinants of The Continuous Use of Mobile Role-Playing Games (RPG) With Gacha System in Indonesia

2024-08-18T15:44:29+07:00

The expansion of the mobile games industry should be attributed to the convenience of in-game purchases that are adopted by most mobile games in the market. In this study, we aim to investigate the relationship between determinants of continuous use of mobile RPG games with gacha system in Indonesia. We disseminated a questionnaire that was then filled in by 257 respondents. From a total of 257 results, we excluded 38 questionnaires due to non-completion as well as 8 questionnaires due to the unreliability of questionnaire answers. There were 211 questionnaires remaining for the model analysis. We proposed a total of 13 hypotheses which resulted in flow experience and satisfaction being positively associated with intention of continuous use. The findings of this study highlight how massive the effect of different type of RPG games affect the determinants of players in Indonesia.

Empowering Higher Education: The Tech Advantage in Work Coordination and Risk Management for Financial Growth

2024-11-02T21:53:10+07:00

This study examined the relationship of work coordination and Enterprise Risk Management on financial performance in Malaysian public higher education (PHEs). It addresses the gaps on how work coordination and ERM could mitigate financial challenges such as dependence on tuition fees and limited government funding. The responses from 350 key informants across 20 Malaysian PHEs were analyzed using SEM analysis. The findings reveal that work coordination significantly enhances ERM implementation, and ERM implementation positively influence the financial performance, ERM also act as a mediator variable, which has a greater effect on financial performance through work coordination. The findings support the strategic role of ERM in facilitating links between organizational capabilities and financial sustainability. It thus practically recommends increasing capacity through governance structures, investing in digital tools for risk management, and engaging leaders to improve ERM effectiveness. Theoretically, this study extends the Resource-Based View, placing ERM as that critical resource that would turn coordinated efforts into measurable financial outcomes. This study closes the literature gap in risk management in higher education by integrating work coordination and ERM within a comprehensive model of financial sustainability that has pragmatic implications for policymakers and leaders of institutions in strengthening resilience within resource-constrained educational contexts.

Oil Palm Smallholders Entrepreneurs And Financial Literacy: Technology Adoption

2024-11-04T17:20:48+07:00

This study explores the relationship between financial literacy and business performance among oil palm smallholder entrepreneurs in Malaysia, emphasizing the role of technology in improving financial practices. Many smallholders earn less than RM2000 per month, largely due to low financial literacy, which hinders effective financial management and business sustainability. Financial literacy is crucial for accessing financing and making informed financial decisions. Data were collected from 1,500 smallholders across six territories in Malaysia, with a 14.4% response rate. Structural Equation Modeling (SEM) revealed a positive correlation between financial literacy and improved financial practices, leading to better business outcomes, including enhanced access to bank loans and government grants. The study also highlights the transformative potential of digital technologies, such as mobile banking and financial education apps, in bridging financial literacy gaps. The findings suggest that targeted financial literacy programs and the adoption of digital tools can significantly enhance smallholders' financial management and business performance. This has important implications for policymakers and financial institutions in promoting rural economic development and sustainable entrepreneurship within the agricultural sector.

Advanced Smart Bracelet for Elderly: Combining Temperature Monitoring and GPS Tracking

2024-11-10T09:43:59+07:00

Indonesia is entering an aging population period, marked by an increase in the number of elderly individuals, accompanied by a rise in dementia cases. This situation leads to higher dependency among the elderly on others for assistance or long-term care. Dementia can cause elderly people to lose their sense of direction, often wandering aimlessly, making them difficult to track. To address this issue, a wearable smart bracelet is proposed to monitor the location and a vital body parameter such as body temperature. The system is equipped with a tracking application that can send an alert if the user is outside a designated area. It automatically sends a warning message to the caregiver's or family member's smartphone when abnormal signs are detected. The bracelet is designed like a wristwatch, to be worn on the wrist. It is small, lightweight, and battery-operated. Temperature and location data can be transmitted in real-time using an internet network to mobile devices. The device can notify when the user is outside the specified area. Test results indicate that the device has high accuracy and reliability in monitoring location and body temperature with accuracy around 98.5%, as well as sending notifications through a Telegram bot when certain thresholds are exceeded. This device can work properly for up to 5 hours on a single battery charge. With this device, it is expected to help monitor and support the care of the elderly so that they can improve their quality of life. This device can also provide an emergency alarm if the elderly are outside the area.

Attacks Detection in Internet of Things Using Machine Learning Techniques: A Review

2024-07-25T17:05:27+07:00

The proliferation of IoT devices across sectors such as home automation, business, healthcare, and transportation has led to the generation of vast amounts of sensitive data. This widespread adoption has introduced significant security challenges and vulnerabilities. This study aims to analyze and evaluate machine learning (ML) and deep learning (DL) models for detecting malicious activities in IoT networks, with a focus on improving cybersecurity measures. We conducted a comprehensive review of various ML and DL models, including Random Forest, Decision Tree, HTA-GAN, Hybrid CNN-LSTM, and SVM. The study also includes an evaluation of the datasets used for identifying harmful data, ensuring effective detection of large-scale attacks in IoT ecosystems. Our findings indicate that these models enhance IoT security by deploying efficient intrusion detection systems (IDS) using reliable, large-scale datasets. The study highlights the performance of these models in balancing security and resource management, given the constraints of IoT devices.ML and DL approaches offer significant security benefits for IoT networks, despite the challenges associated with their implementation. The study underscores the importance of future research to address these challenges and further improve IoT security. The results provide valuable insights into the application of ML/DL models in IoT security, contributing to both theoretical knowledge and practical solutions for enhancing cybersecurity in IoT ecosystems.

Exploring The Metaverse: A Comprehensive Bibliometric Review Using Scopus Database

2024-02-02T12:10:38+07:00

Metaverse is fast growing and emerging technology domain which has been adopted all around the world by organizations in different facets of business. The penetration of the internet and social media all around the world has paced the adoption where Gaming and social media companies are torch-bearers of the metaverse. As the metaverse becomes popular among various domains which include education, health, marketing, fashion, finance, and many more, the field of research has also seen the adoption of metaverse as a field of study by academicians around the world. Despite the growing domain of research, there is a dearth of review studies in the field of the metaverse, to fill the void authors conducted a bibliometric analysis. The Scopus database is taken into consideration to perform bibliometric analysis using the Vosviewer tool. The study identified the top publication and growth trends, domains, geographical distribution of documents and keyword analysis. Key results depict that as metaverse is related to technological advancements, the maximum research has also been done in the field of computer science and engineering. The contribution in the field from technologically advanced countries is highest, whereas from less developed countries the contribution in the field is negligible.

Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model

2024-10-17T10:17:14+07:00

The estimation of the electromagnet relay remaining useful life is highly crucial to maintain reliability and avoid unscheduled breakdowns in various applications. The objective of this research work will be to design a model with much higher precision and efficiency utilizing PCA coupled with a hybrid deep learning architecture of Bi-LSTM along with Bi-GRU. The C-MAPSS dataset was of reduced dimensionality, since PCA has been applied to eliminate data redundancy while retaining crucial characteristics, and then K-means clustering is applied to classify the data; afterwards, the Bi-LSTM and Bi-GRU models are implemented for RUL relay prediction. The proposed method in comparison with typical deep learning models has a Mean Absolute Error of 0.021 and an R² of 0.996. Results developed reflect how the model can produce some very powerful prediction, however; what it really shows is great potential for this approach with respect to predictive maintenance of electromagnet relays. PCA may well amalgamate with Bi-LSTM and Bi-GRU models to achieve great scalability according to the maintenance engineering, which offers practical applications in improving the lifetime of the electromagnet relays.

Breakthrough in Brain Tumor Diagnosis: A Cutting-Edge Hybrid Depthwise-Direct Acyclic Graph Network for MRI Image Classification

2024-10-20T08:34:39+07:00

Both adults and children are at risk of dying from brain tumors. On the other hand, prompt and precise detection can save lives. Early detection is necessary for a proper diagnosis of a brain tumor, and magnetic resonance imaging (MRI) is often used in this context. To assist in the early diagnosis of sickness, neuro-oncologists have used Computer-Aided Diagnosis (CAD) in a number of ways. In this study, proposed a hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET)-based deep learning was developed to distinguish between cancers and non-tumors. Three processes make up this method: pre-processing, classification, and feature extraction. Pre-processing methods used in this study included contrast enhancement and image shrinking. The MRI picture is processed to get the wavelet and texture properties and used to build a classifier. Using MRI scans, the proposed hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model classifies two types of brain tumors: tumor and non-tumor. Performance criteria such as accuracy (ACC), specificity (SP), and sensitivity (SE) are used to assess the suggested hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model. Based on 850 images, the studies yielded a 99.54% categorization accuracy demonstrate that the suggested hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model beats the most advanced methods.

Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning

2024-10-26T08:42:32+07:00

Due to COVID-19, the need for online education has increased worldwide, prompting students to shift from traditional learning methods to online platforms as guided by higher education departments. Higher learning institutes are focused on developing constructive online learning platforms. This research aims to measure students’ academic performance on an online learning platform – Moodle Learning Management System (LMS) – using machine learning techniques. Moodle LMS, a popular free and open-source system, has seen significant growth since the COVID-19 lockdown. Many researchers have analyzed student performance in online learning, yet there remains a need to predict academic outcomes effectively. In this study, data were collected from a higher learning institute in Tamil Nadu, and linear regression was applied to predict students' final course outcomes. The analysis, based on students' activity in Moodle LMS across both theory and laboratory courses, helps faculty identify students at risk of failing and adjust instructional methods and assignments accordingly. This approach aims to reduce failure rates by providing timely warnings and encouraging students to improve their engagement with LMS resources.

Fleet Control With IoT Using TLS Certificates And SIM7000G GPS Device

2024-09-07T22:09:31+07:00

Companies that apply technology to monitor mobile units face several challenges such as obtaining the best precision in geolocation coordinates, having good communication quality and security in data transmission, detecting route deviations, avoiding delays in their journeys, among others. This article proposes a real-time transport fleet monitoring system that uses the Internet of Things (IoT) to contribute to some of these challenges and improve the overall user experience of the collected data. A model is presented that includes a proposal to reinforce security in data transit with MQTT using Transport Layer Security (TLS) and a study carried out with the purpose of detecting which GPS coordinate acquisition device is more precise. 368 tests were carried out in five different coordinates with SIM800, ESP32, SIM7000G considering altitude, latitude, longitude, speed, number of satellites and sending time. The device that provided the best results in coordinate accuracy was the SIM7000G, even meeting the requirements for battery life and storage capacity due to the demand required to execute TLS security certificates.

The Geographic Factors-Based Optimization of National Flight Hub Airport Locations for Enhances Aviation Safety Standard

2024-11-05T21:21:48+07:00

Indonesia's strategic geographical location and conditions are a result of its archipelagic status. Indeed, this significantly impacts the livelihoods of Indonesians, particularly in the area of transportation. In order to facilitate the mobility of individuals between islands, the government constructed transportation infrastructure and facilities. This will serve as a reference for government regulations that aim to reduce inequality, specifically in the development and distribution of products and services outside Java, particularly in Papua. The primary objective of this research is to conduct a connectivity analysis as an initial step towards optimizing the transportation network and determining the level of connectivity between Indonesian provinces through air transportation. Next, the researchers will study the existing air transportation network's structure to understand its characteristics clearly. It will be helpful material for formulating optimization and network management scenarios. The structuring pattern in question is the application of the hub-and-spoke network concept. The hub-and-spoke network pattern focuses on optimization problems from the perspective of economies of scale. The researchers optimized the provincial capital airport's location to establish a hub airport and subsequently refined the analysis results to enhance the efficiency of the new flight service network. The above steps will compare air travel performance before and after the network arrangement scenario. Indicators for the network connectivity level will also be present, indicating that the final product optimization is now ready for verification. The target of this research is to produce a concept or optimize the location of airport hubs through a comprehensive study taking into account geographic factors and conducting an analysis of the location of National Aviation Hub airports, which is the novelty of this research.