Journal of Applied Engineering and Technological Science (JAETS)

Calorific Value of Palm Kernel Shell Charcoal (PKSC) Briquette as Solid Fuel

2025-03-03T11:46:16+07:00

The need and utilization of energy in society exceed available production. This condition requires acceleration and efforts to find solutions through the diversification of palm shell biomass into solid fuel briquettes. Palm shells have the potential as biomass and renewable energy sources that are selected based on strategic, technical, and environmental considerations. Its utilization so far has only been burned directly which causes air pollution or used as road paving in oil palm plantations. The environmental impact is the accumulation of solid waste, and global warming in the Crude Palm Oil processing industry. The research objective was to obtain the calorific value of palm kernel shell briquettes with carbonization process. The experimental research method carried out by innovating palm kernel shell briquette raw materials at various percentage variances (90%: 10%, 85%: 15%, 80%: 20%, 75%: 25%) using tapioca adhesive. The technical parameters of briquettes making are molding pressure of 10 MPa, particle grains of 60 mesh, carbonization temperature of 400^oC; 450^oC; 500^oC with a holding time of 1 hour. From this study, the calorific value of palm kernel shell charcoal (PKSC) briquettes at a concentration of 85%;15% at a temperature of 400^oC was 25.86 MJ/kg with tapioca adhesives as the highest calorific value parameters. The technology used to make palm kernel shell charcoal briquettes is a potential development that can be recommended as a precursor to solid fuels. The impact of developing PKSC biomass energy briquettes is an innovation in utilizing waste to create solid fuels. The implications of this research can be applied by home industries or households. This research is a contribution to solutions in overcoming energy needs and deficiencies as a form of sustainable energy..

A Cost-Effective Real-Time Human Activity Recognition System Using Supervised Learning Algorithms and Wearable Acceleration Sensors

2025-03-06T11:31:58+07:00

Human activity recognition (HAR) plays a vital role in health monitoring by providing detailed insights into daily movements. This study aims to enhance HAR by developing a lightweight and efficient machine learning model that balances accuracy, real-time performance, and affordability. Using acceleration data from a wearable inertial sensor, we extracted a novel feature set optimized for computational efficiency. The proposed model was evaluated on a benchmark dataset, achieving an accuracy of 98.9%, in classifying six essential daily activities: walking, walking upstairs, walking downstairs, laying, sitting, and standing. These results demonstrate the model’s potential for real-time health monitoring applications, offering a cost-effective and deployable solution for wearable-based activity recognition.

Bibliometric Analysis for Quality Management in The Manufacturing Sector: 2020 to 2024

2024-11-05T09:52:40+07:00

In today's dynamic manufacturing market, quality management is critical to competitiveness, efficiency, and customer happiness. This study includes a bibliometric analysis from 2020 to 2024 to illuminate trends, patterns, and developing quality management areas in the industrial sector. Using bibliometric methodologies, this study examines a wide range of scholarly outputs, including journal articles, conference proceedings, and other relevant material, to determine the intellectual structure of this field. This methodology finds crucial contributors, seminal works, research focal points, and emerging themes in the field by quantitatively examining publication outputs, citation networks, collaboration patterns, and keyword co-occurrences. By synthesizing and visualizing the bibliographic data, this study provides a comprehensive picture of the knowledge landscape, allowing researchers, practitioners, and policymakers to navigate the complexities of quality management in manufacturing and foster continuous improvement initiatives. Furthermore, this bibliometric research provides important insights into the evolution of quality management techniques, emphasising the adaptation of methodologies and frameworks in response to growing difficulties and technological advancements. This study provides a road map for future investigations to improve quality management techniques in the industrial industry, indicating significant research gaps and areas ripe for discovery. Furthermore, the analysis gives information on the global distribution of research efforts, allowing for international collaborations and knowledge exchange to propel collective growth in quality management. Finally, this thorough review of the scholarly literature broadens our understanding of manufacturing quality management. It informs strategic decision-making processes for businesses looking to optimise their operations and provide superior products and services to customers.

Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate

2024-10-26T08:28:18+07:00

An artificial neural network (ANN) is a network of a group of units to be processed which is modeled based on the behavior of human neural networks. ANN has one of its tasks, namely prediction. Multilayer perceptron (MLP) is one of the ANN methods that can be prediction all of data. Where the prediction needs to be reviewed because the prediction process does not always run normally. So, it takes a good measurement accuracy in order to get an accuracy sensitivity. The accuracy technique in this paper is carried out using Mean Absolute Percentage Error (MAPE) based on absolute error and detection rate. The results obtained with absolute error achieve an accuracy of 99.73% while the accuracy based on the detection rate achieves an accuracy of 99.49%. this can be seen in the case of the prediction of (Indonesian Rupiah) IDR exchange rate against United State Dollar (USD) with the MLP algorithm by testing using MAPE to achieve sensitivity with absolute error.

Provisioning of Live Container Migration in Edge/Cloud Environments: Techniques and Challenges

2025-04-29T11:37:05+07:00

Containers have become increasingly popular in the virtualization landscape. Their lightweight nature and fast deployment behavior make them an efficient alternative to traditional hypervisor-based virtual machines. In IoT applications and edge/cloud deployment, the live container migration can substantially reduce computing system overheads by minimizing the migration time and transmitting minimum memory pages from the source host without interrupting the service process. Until today, there has been a lack of comprehensive research discussing live container migration in the IoT domain and investigating the challenges of representing them in the edge/cloud environment. This survey presents cutting-edge articles that involve a live container migration approach. This survey aims to boost current knowledge, identify best practices, and highlight the challenges of live container migration in the IoT and edge/cloud environments, which will contribute to the advancement of container technology, as well as the optimization of deployment practices. The survey results indicate that selecting a suitable container engine relies heavily on the workload characteristics in the edge/cloud environment, particularly given the constraintions of live container migration. The survey highlights the direct and indirect challenges that influence container migration and proposes machine learning and blockchain as potential solutions.

Large-Scale Periodic Gust Generation and Spectral Analysis Approach for Characterization and Evaluation

2024-05-06T22:42:00+07:00

Generating a periodic continuous gust in a controlled manner and at sufficiently large scales for the gust encounter studies on MAV applications is a challenge. In order to achieve it a pitching and plunging flat-plate is utilized with aggressive motion profiles. A range of periodic functions in pitch and plunge axes are investigated for the motion of the gust generator. Significant and distinct vortices are measured with PIV in its wake. An in-depth spectral analysis of the velocity vector field of the wake is performed to investigate the generated gust characteristics since the aggressive motion profiles can produce uniform and/or weak gust characteristics. To obtain the cases that simulate large-scale transverse wind gusts in a quasi-sinusoidal pattern, the PIV results are evaluated by using auto- and cross-spectral density plots of the entire flow field at the wake, ensuring the consistency of the gust characteristics for future gust wing encounter studies. Four cases in which the flat-plate moves with the quasi-feathering condition provide gusts that are useful to employ in MAV gust studies.

From Virtual to Reality: How Metaverse and VR Technologies Influence Travel Decisions

2025-03-03T13:10:49+07:00

This study investigates the impact of Virtual Reality (VR) and the Metaverse on travel decisions, specifically focusing on lesser-known tourist destinations. The purpose is to understand how immersive digital experiences can influence potential visitors' perceptions and travel intentions. A mixed-methods approach, combining qualitative interviews with tourism stakeholders and quantitative surveys with 500 participants, was used to collect data. The results show that VR and Metaverse experiences significantly enhance user engagement, emotional attachment, and the likelihood of visiting the destination in person. The study's findings offer practical insights for tourism marketing strategies, suggesting that VR and Metaverse platforms can complement traditional marketing approaches. Theoretical implications include contributing to the understanding of digital transformation in tourism, particularly in how immersive technologies shape travel behavior. This research contributes to both theory and practice by highlighting the potential for VR and Metaverse technologies to increase the appeal of lesser-known destinations like Lake Toba.

Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques

2025-03-02T14:45:19+07:00

This study aims to develop a predictive maintenance system for an aging vertical grinding machine, operational since 1978, by integrating machine learning techniques, vibration analysis, and fuzzy logic. The research addresses the challenges of increased wear and unexpected failures in older machinery, which can lead to costly downtime and reduced operational efficiency. Vibration and temperature data were collected over 12 days using an MPU-9250 accelerometer, with conditions categorized as good, fair, and faulty. Various machine learning models, including logistic regression, k-nearest neighbors, support vector machines, decision trees, random forest, and Naive Bayes, were trained to classify bearing states. The random forest model achieved the highest accuracy of 94.59%, demonstrating its effectiveness in predicting machine failures. The results highlight the potential of combining multi-dimensional sensor data with advanced analytics to enable early fault detection, minimize downtime, and improve operational efficiency. This approach provides a cost-effective solution for maintaining aging machinery and contributes to both theoretical advancements in machine learning applications and practical improvements in industrial maintenance practices. The study’s findings offer scalable insights for industries reliant on legacy equipment, promoting sustainable manufacturing through optimized resource use and enhanced reliability.

Liquefaction Potential Analysis Using Various Methods (Case Study of Railway Bridge in Sintuk Toboh Gadang District, Padang Pariaman Regency, West Sumatera)

2025-03-18T11:30:47+07:00

The earthquake that rocked West Sumatra with a magnitude of 7.9 SR, a depth of 71 km, and an epicenter of 0.84 LS - 99.65 BT around 57 km Southwest of Pariaman on 30 September 2009 has caused damage to infrastructure and buildings and caused 383 fatalities. One of the problems caused by the earthquake is the liquefaction phenomenon. Liquefaction was reported to have occurred in Padang in the form of sand ejection coming out of cracks in the ground after the 7.9 SR earthquake in 2009. This study aims to determine the liquefaction potential of the Sintuk Toboh Gadang railway, Pariaman, using various liquefaction potential analysis methods so that the most practical and convincing method is obtained among these methods. In this study, the methods used to predict liquefaction are the Tsuchida (1970), Seed & Idriss (1971), Shibata & Teparaksa (1988), and Hakam (2020) methods. Field testing was conducted at four CPT test points, four NSPT test points, and machine drilling tests. The results showed that using the Tsuchida (1970) method, soil deposits at the four points tended to have liquefaction potential. The Seed & Idriss (1971) method showed that points 3, with depths of 8m and 14m, and point 4, with a depth of 8m, had liquefaction potential, while the Shibata & Teparaksa (1988) method using CPT data showed that at depths <10 meters there was a tendency for liquefaction to occur at the four points reviewed. The study's results using the Hakam (2020) method resemble the method proposed by Seed & Idriss (1971). It can be concluded that among the four methods, the most practical and convincing method is the Hakam (2020) method.

Adsorption of Cu(II), Cd(II), and Pb(II) Using a Novel Adsorbent Based on Polyvinyl Alcohol Anchoring Citric Acid (PVA-CA)

2025-04-07T22:56:05+07:00

Heavy metals are widely used in industry. On the other hand, heavy metals cause environmental pollution. One technique for removing heavy metals is adsorption. By reacting polyvinyl alcohol with triethyl citrate, a novel adsorbent based on polyvinyl alcohol attaching citric acid (PVA-CA) has been successfully formed, as shown by FTIR spectra. The adsorption test was carried out under acidic conditions for metal ions, namely Cu(II), Cd(II), and Pb(II). Experimental results on metal ions Cu(II), Cd(II), and Pb(II) obtained that the optimum pH for the adsorption of each metal were 4.64, 3.92, and 4.65, respectively. The adsorption mechanism was ion exchange and was supported by coordination bonds between metal ions and C=O oxygen atom of carboxylic group; the slope value and FTIR measurements confirmed this. SEM-EDX was used to confirm the morphology of the adsorbent and metals adsorbed on the PVA-CA surface. The maximum loading capacity of Cd(II) was higher than Pb(II) and Cu(II), for each metal were 1.33, 0.69, and 0.67 mol/ kg for Cu(II), Cd(II), and Pb(II), respectively. From the capacity value, which was relatively high compared to other adsorbents, PVA-CA has excellent potential as an adsorbent to overcome environmental pollution problems caused by heavy metals such as Cu(II), Cd(II), and Pb(II).

A Framework of Hybrid System Dynamics and Agent Based Model With Cooperative Game Theory for Sustainable Coffee Supply Chain

2025-03-06T13:11:34+07:00

The global coffee supply chain is a complex network involving diverse stakeholders such as farmers, traders, exporters, and consumers, each with unique incentives and constraints. This study introduces a conceptual framework that integrates System Dynamics (SD), Agent-Based Modelling (ABM), and Cooperative Game Theory (CGT) to address challenges in profit allocation, coalition stability, and sustainability. SD provides macro-level insights into global trends such as price fluctuations and production dynamics, while ABM models individual decision-making processes. CGT complements these methods by facilitating fair payoff distribution and stable coalition formation. The framework is structured into problem identification, model development and mapping, and interaction mode selection, offering a comprehensive approach to understanding material, information, and decision flows. Using illustrative scenarios, the study demonstrates the framework’s potential to analyses trade-offs and long-term impacts on supply chain stability. Its practical implications could support policymakers and industry leaders in designing fair profit-sharing mechanisms, promoting stable cooperation among stakeholders, and enhancing the overall sustainability of coffee and other agri-food supply chains. Thus, the framework highlights its applicability as a conceptual tool for supporting decision-making and sustainability in coffee supply chains and beyond.

Analysis of Performance and Energy Efficiency for Multi-User Mimo 6G Network Using Beamforming Methods

2025-03-06T11:20:07+07:00

This paper addresses the challenge of high energy consumption in sixth-generation (6G) multi-user multiple-input multiple-output (MU-MIMO) networks by analyzing how different beamforming techniques impact network performance and energy efficiency. Simulations were performed in a sub-THz communication environment using analog, digital, and hybrid beamforming across various base station antenna configurations (64, 128, and 256 elements) and user densities to evaluate performance metrics (such as received power and signal-to-interference-plus-noise ratio) alongside energy consumption. The results show that larger antenna arrays (e.g., 256 elements) provide significantly higher signal quality and received power but require more energy. In contrast, smaller arrays (e.g., 64 elements) use less power at the cost of performance. Digital beamforming with a moderate array size (128 antennas) yields the highest energy efficiency, while hybrid beamforming with the largest array results in the lowest energy efficiency. These findings imply that carefully selecting the beamforming method and antenna array size can balance performance with energy efficiency, guiding the design of more sustainable 6G networks. The novel contribution of this research is a comprehensive comparative analysis of analog, digital, and hybrid beamforming in a 6G MU-MIMO context, providing new insights for optimizing future 6G network deployments.

The Improvement of Aircraft Carbon Dioxide Emissions Through The Use of Enhanced Gas is Carried Out According to The Requirements of The Standard

2025-03-09T13:54:16+07:00

The current study aims to see that optimizing CO? emissions on aircraft through effective exhaust gas management is essential to achieving environmental sustainability in aviation. Sequentially, the literature review has enabled the study to identify current standards and regulations for aircraft CO? emissions, technologies for reducing aircraft CO? emissions, challenges in meeting CO? emission standards, environmental and economic benefits of optimized emission strategies and future research opportunities in aircraft emission reduction. This study uses literature data related to carbon dioxide emissions from aircraft. The findings are key strategies include optimizing fuel consumption, increasing sustainable aviation fuel (saf) production, and adopting novel technologies like turbine propulsion. Immediate actions and effective policies are essential for decarbonization, as the sector's emissions continue to rise. future efforts should focus on electric propulsion and sustainable biofuels to mitigate climate change impacts. Immediate actions are important if carbon neutrality by 2020 and net-zero emissions by 2050 are to be possible; so this needs effective policies and increased funding for research. It is for this reason that the aviation sector should be among those going through urgent decarbonization processes due to the huge contribution it makes to CO? global emissions

Analytical Hierarchy Process (AHP) : A Strategy to develop Disaster Resilient Tourism Priority in Indonesia

2025-04-29T11:40:52+07:00

Majene Regency in Sulawesi Barat, Indonesia, ranks as one of the regions most susceptible to natural disasters, with its tourism sector highly exposed to these risks. Given that nearly all tourism destinations in the region lie within hazard-prone zones, the economic vulnerability of this sector is critical. This research aims to formulate a disaster-resilient tourism strategy for Majene by employing the Analytical Hierarchy Process (AHP), a decision-making framework that enables structured prioritization based on stakeholder input. The study involved twelve experts from government, academia, the private sector, and the local community who conducted pairwise comparisons of five strategic categories derived from the World Bank’s disaster-resilient tourism framework: understanding risk, planning and prioritization, mitigation and preparedness, response and recovery, and long-term resilience actions. The results revealed that long-term resilience actions (22.7%), understanding risk (22.3%), and mitigation and preparedness (21.4%) were the top priorities. Key programs within these strategies include integrating tourism into national risk assessments, embedding tourism into disaster management planning, and establishing early warning systems. These findings offer actionable insights for local governments and tourism planners, highlighting strategic priorities that can guide policy development and foster sustainable, disaster-resilient tourism in vulnerable areas like Majene.

Cocoa Ripeness Classification Using Vision Transformer

2025-04-28T19:40:26+07:00

The quality of manual methods for assessing the ripeness of cocoa pods is subjective and varies from one person to another because of the intense labor required and variation of light and background conditions within the field. This research implemented an automated classification approach for cocoa ripeness classification utilizing Vision Transformer (ViT) with Shifted Patch Tokenization (SPT) and Locality Self Attention (LSA) to improve classification accuracy. The model proposed in this research achieved an accuracy of 82.65% and a macro F1 score of 82.71 on the exam with 1,559 images captured under varying illumination backgrounds and complex scenes. The model also proved better than baseline CNN architectures such as VGG, MobileNet, and ResNet in identifying visually progressive stages of ripeness and demonstrated greater generalization in cocoa ripeness classification. The findings of this research indicate the benefits of reducing manual intervention with careful inspection without compromising quality assurance standards in cocoa production. This work demonstrates new ways of applying transformer models to address computer vision problems in agriculture which is a step towards precision and smart farming.

Secure E-Voting System Utilizing Fingerprint Authentication, AES-GCM Encryption and Hybrid Blind Watermarking

2024-11-06T11:42:09+07:00

Ensuring security, integrity, and reliability of the election process consider as the main challenges in the electronic voting system. This paper describes the e-voting system by integrating the biometric authentication, advanced encryption, and watermarking techniques towards meeting such challenges. The system employs the fingerprint authentication by utilizing the Scale-Invariant Feature Transform (SIFT) for verifying the identity of the voter to ensure genuineness and non-repudiation of the service. The vote will be encrypted with the AES-GCM technique to be employed in securing the voting process, thus ensuring both data privacy and integrity. Hybrid Blind Watermarking employs the technique of Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for embedding the encrypted vote into the colored watermark cover image. Increased robustness against attacks with maintained vote confidentiality is achieved through this approach. The experimental results proved its imperceptibility to reach a Peak Signal-to-Noise Ratio (PSNR) of 40.7 and Normalized Correlation (NC) of 1. Thus, the proposed system enhances the theoretical foundation of secure e-voting and provides an implementation strategy for a reliable and tamper-proof voting system.

A Heuristic Enhancing Artificial Immune System for Three-dimensional Loading Capacitated Vehicle Routing Problem

2025-03-02T14:20:22+07:00

This study addresses the Three-Dimensional Loading Capacitated Vehicle Routing Problem (3L-CVRP), a highly complex NP-hard problem that combines vehicle routing with spatially constrained three-dimensional bin packing. To tackle this challenge, we propose an enhanced Artificial Immune System (En-AIS) that integrates a novel local search heuristic called “Bring-i-to-j,” designed to improve routing feasibility and loading efficiency. The En-AIS algorithm is further refined through rigorous parameter tuning using a full factorial design and ANOVA analysis. Comparative experiments were conducted against conventional AIS and the Firefly Algorithm (FA) across 27 benchmark instances. Results demonstrate that En-AIS consistently outperforms both baseline methods in terms of solution quality, achieving an average improvement of 15–20% while maintaining competitive computational times. These findings highlight the algorithm’s robustness and its practical potential for application in logistics and supply chain optimization tasks involving joint routing and loading decisions.

Weather-Baglog Parameters Monitoring System Based IoT-MQTT-Nodered For Mushroom Cultivation Room: A Precision Agriculture

2024-11-09T06:00:44+07:00

Mushroom cultivation methods are continually being refined to meet increasing demands for quantity and quality. However, frequent weather fluctuations often pose challenges. They can influence the optimal growth of mushrooms and the baglog's nutrient-chemical. This study aims to implement precision agriculture by developing a weather-baglog parameters monitoring system based on IoT-MQTT-Nodered technology. It seeks to analyze and evaluate the dominant parameters influencing ideal oyster mushroom cultivation room conditions using machine learning classification models and capability process analysis. Sample data was collected from an oyster mushroom cultivation room using a 24-hour monitoring system over seven days. The monitoring tool's system design comprises three parts: multi-sensor data acquisition, communication protocol to the server, and smartphone-based data monitoring. The results demonstrate the system's effectiveness, mobile-access, and durability in monitoring and acquiring weather-baglog parameters data. The best model shows that light, temperature, and humidity are the dominant parameters influencing the ideal oyster cultivation room. Capability process analysis reveals that the dominant parameters in the cultivation room are currently less than ideal. The implications for improvement are needed an IoT-based control system to regulate them and make them ideal. This finding has been tested as an effective, mobile-access, durable, and data-centering monitoring system.

Bottle Design of Onion Chili Sauce using Kansei Engineering for SMEs in Indonesia

2024-11-14T17:49:21+07:00

The SMEs industry in Indonesia keep growing significantly in the last 10 years, driven by digital channels such as e-commerce. One of Indonesia's popular food products is onion chili. However, the packaging design of the product is generic and may not be attractive or meet consumer expectations. This study aims to design the product packaging for onion chili using Kansei Engineering (KE) to increase its value. To aim for the objectives, this research was conducted in two stages, the first stage was to assess customer satisfaction and collected Kansei words that related to packaged onion chili. The satisfaction level was measured based on color, packaging durability, and packaging style. In the second stage, a questionnaire consisting of a packaged design checklist and Kansei words was developed. Then, the Kansei words were reduced using Principal Component Analysis (PCA) and obtained 3 main factors which are convenience, durability, and hygiene & attractiveness. The result was then followed by determining the elements of the new design based on Partial Least Square Regression (PLSR) and followed by design requirement analysis. Three proposed bottle designs were developed based on those 3 main factors as an alternative design for SMEs who sell this kind of product

IoT Enabled Real Time Load Height Monitoring and Control System Using PLC and HMI for Smart Industrial

2025-03-06T21:45:30+07:00

This study develops a laboratory-scale prototype of an IoT-enabled, real-time load height monitoring and control system that integrates Programmable Logic Controllers (PLCs), Human–Machine Interfaces (HMIs), and cloud-based MQTT communication. Developed and validated under controlled conditions, the prototype consistently demonstrates sub-2-second latency (0.66–1.58 seconds) across varying network speeds, confirming its technical feasibility for future industrial applications. Proximity sensors and PLCs enable precise load height measurement, while the Haiwell C7S HMI provides real-time visualization and multi-platform control via web and mobile interfaces. Experimental results highlight the prototype’s robustness, scalability, and alignment with Industry 4.0 frameworks, offering a foundational architecture for subsequent industrial deployment. This work bridges the gap between theoretical Cyber-Physical Systems (CPS) principles and practical implementation, emphasizing adaptability and low-latency performance for smart manufacturing ecosystems.

Solving Simulated Imbalanced Body Performance Data using A-SUWO and Tomek Link Algorithm

2024-04-23T13:14:01+07:00

This research examines the impact of various sampling techniques on the performance of classification models in the context of imbalanced datasets, employing the body performance dataset as a case study. Many studies in this field analyze the effect of sampling techniques on a model performance, however they often begin with imbalance datasets, lacking a balanced baseline for comparison. This research addresses that gap by simulating an imbalanced dataset from an originally balanced dataset, obtaining a target reference point for evaluating the effectiveness of the sampling methods. The dataset is categorized into three versions: (1) a normal distribution, (2) a simulated imbalanced distribution, and (3) a synthesized dataset achieved through various data sampling techniques, including oversampling with Adaptive Semi-Unsupervised Weighted Oversampling (A-SUWO), undersampling with Tomek Link, and hybrid sampling combining both techniques. The primary objective of this research is to identify sampling techniques, when combined with model performance, closely match the performance observed in the original balanced dataset. Based on all experiments using Decision Tree, Random Forest, and K-Nearest Neighbors (KNN) as classifiers, both A-SUWO and Tomek Link led to overfitting due to discernible gap between the training and testing accuracy, averaging 0.21304. Despite overftting and general performance issue, the undersampling with Tomek Link obtained highest test accuracy (0.65023), outperforming A-SUWO (0.62883) and the hybrid approach (0.63568) on average. These findings highlight the importance of appropriate sampling techniques and optimizing model performance in imbalanced datasets.

University Students' Intentions Toward Entrepreneurial Careers in The Hospitality and Tourism Sector: Empirical Insights From The Techno-Savvy Generation in Higher Education

2024-10-24T20:39:36+07:00

This study investigates the impact of family support, entrepreneurial passion, entrepreneurial motivation, and techno-savvy culture on the entrepreneurial career intentions of university students in the hospitality and tourism sector, with entrepreneurship education as a moderating variable. Data were collected from 277 students at Universitas Negeri Padang’s Faculty of Tourism and Hospitality who had completed entrepreneurial courses. Partial least squares structural equation modeling was employed to analyze the data. The findings reveal that family support, entrepreneurial passion, and entrepreneurial motivation significantly influence students’ entrepreneurial career intentions, while techno-savvy culture showed no direct impact. However, entrepreneurship education significantly moderated the relationships between these factors and entrepreneurial intentions. These findings provide actionable insights for enhancing entrepreneurship education to foster innovation and career readiness in the hospitality and tourism industry. The study contributes to existing knowledge by elucidating the interplay of support systems, intrinsic motivations, and education in shaping entrepreneurial aspirations, offering a foundation for educational and policy reforms to boost entrepreneurship in the sector.

Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children

2025-01-18T12:12:05+07:00

Stunting in children remains a significant global health challenge, particularly in low- and middle-income countries. Addressing this issue requires an effective approach to predicting and preventing inadequate nutritional fulfillment. This study uses the Naïve Bayes approach to forecast nutritional needs for children's growth and development, providing practical information for stunting prevention efforts. The data used were sourced from 174 infant and toddler examinations at the Puskesmas Lawang, involving eight key attributes: gender, age, weight, height, head circumference, pre-screening, vision tests, and nutritional status. Key performance metrics were evaluated to validate the model's predictive capabilities, including accuracy, precision, recall, and F1-score. Six test scenarios were conducted using different percentages of training data (90%, 80%, 70%, 60%, 50%, and 40%) to evaluate the reliability of the Naïve Bayes method. Results indicated that the highest accuracy of 78.84% was achieved in the sixth test scenario. The third test scenario produced the highest precision at 97.5%, while the highest recall (100%) was observed in the first three scenarios. The highest F-measure of 90.3% occurred in the fourth scenario. These results suggest the algorithm's potential for early detection to decrease the number of stunting children. The study’s implications are twofold: practically, the model can be integrated into health monitoring systems to assist healthcare professionals and policymakers in designing more effective nutrition programs; theoretically, it highlights the adaptability of Naive Bayes for handling complex, multi-dimensional health data.

Executive Carriages Toilet of Joglosemarkerto Indonesian`S Train Using Universal Design Approach

2025-03-03T13:30:50+07:00

This study aims to assess the accessibility of toilets on the Joglosemarkerto executive train for people with disabilities, especially wheelchair users, using the Universal Design approach. The research method is a mixed method, through direct measurement of toilet dimensions, accessibility checklists based on universal design principles, and online user perception surveys involving 64 respondents, including disabled groups. The Accessibility Index is calculated based on weighting the door, sink, and main toilet room. The study results indicate that the accessibility index category is low, with a score of 22 for the toilet door, 17.5 for the sink, and 24.4 for the main toilet room. The main obstacles are narrow circulation space, insufficient door width, information in Braille, and emergency buttons not being available. Practically, this study encourages operators and policymakers to improve the design of accessible train toilets by involving disabled groups in the design. Theoretically, this study enriches the literature on evaluating public transportation accessibility in Indonesia through the presentation of a replicable universal design evaluation model. The contribution of this study is to map the infrastructure gaps and develop recommendations to improve the inclusiveness of railways in Indonesia and promote equality of access.

Enhancing Electricity Consumption Forecasting in The Republic of Kazakhstan Using Machine Learning

2025-03-22T22:05:04+07:00

Accurate electricity consumption forecasting is critical for optimizing energy management and ensuring grid stability. This study uses advanced machine learning techniques to enhance electricity consumption forecasting in the Republic of Kazakhstan. The research analyzes historical electricity consumption data from 2002 to 2022. Considering seasonal and temporal dependencies. Various forecasting models, including Holt-Winters, Seasonal ARIMA (SARIMA), and Long Short-Term Memory (LSTM) networks, are applied and compared in terms of accuracy and reliability. The results indicate that while traditional statistical models effectively capture seasonal patterns, machine learning-based approaches, particularly LSTM, demonstrate superior performance in identifying complex nonlinear trends. The study discusses the practical implications of accurate electricity consumption forecasting for energy management, demand-side optimization, and policymaking. The findings contribute to developing intelligent analytical frameworks for improving energy efficiency and sustainability in Kazakhstan’s power sector. This study enhances electricity consumption forecasting in Kazakhstan using machine learning models, improving accuracy and energy management. Scientifically, it advances predictive analytics in power systems. Practically, it aids grid stability and demand planning. And sustainability. Internationally, the findings contribute to global forecasting methodologies, benefiting energy sectors worldwide. LSTM outperforms traditional models, offering robust solutions for dynamic electricity demand. This study uses advanced machine learning techniques to improve electricity consumption forecasting in the Republic of Kazakhstan. Historical monthly data from 2002 to 2022 were collected from the National Statistics Bureau. We compared statistical models (Holt-Winters, SARIMA) with a Long Short-Term Memory (LSTM) neural network. Results show that while classical methods effectively capture seasonal trends, LSTM more accurately models nonlinearities and longer-term dependencies. The implications include enhanced planning for energy providers and policymakers, leading to better demand-side management and grid stability. Our findings contribute to developing intelligent forecasting systems in Kazakhstan’s power sector and provide an example for other regions with similar energy challenges.

Determination of Covid-19 Pulmonary Infection Rate From X-Ray Images Using U-Net Model

2025-03-06T11:33:50+07:00

Global health has suffered by millions of COVID-19 infections and fatalities. Pneumonia and ARDS are major consequences of this viral infection. Patient treatment and resource allocation depend on accurate lung infection rates. Reverse transcription polymerase chain reaction (RT-PCR) is highly specific but lacks sensitivity, especially in early infection. Thus, imaging, particularly chest X-rays, is crucial for detecting and monitoring COVID-19-related pulmonary problems. Among various image processing techniques, deep learning methods, especially U-net models, have shown promising results in segmenting and analyzing X-ray images to determine the extent of lung infection. This article explores the importance of imaging techniques in diagnosing COVID-19 lung infection, provides an overview of the U-Net model in medical imaging, and describes in detail the method of using this complex model to determine infection rates from radiographs. This study discusses the diagnosis of coronavirus infection, i.e. whether a person is infected or not, and determines the infection rate (severity) that mean the percentage of virus in the lungs was calculated based on a global radiograph (X-ray) dataset to study the infection, infection rate, and diagnosis by specialists using a pre-trained model (U-Net model). The results obtained were 97% accurate in diagnosing whether a patient was infected with the virus or not.

Advancements In Blockchain Cryptography: Self-Signed Key Applications For Digital Record Protection

2025-03-02T14:34:20+07:00

The significant deployment of Electronic Health Records (EHRs) has introduced serious issues with data security and confidentiality. The proposed study addresses such issues by investigating innovation in blockchain cryptography, with a special focus on the application of self-signed keys for secure digital record management. The research combines the use of Elliptic Curve Cryptography (ECC) with a blockchain framework to suggest a decentralized and efficient solution for the management and authentication of digital records. The experimental evaluation of the proposed solution indicates the efficiency of the system with 1626.03 seconds of execution time, 0.0018 tps of throughput, and 3.1790 seconds of the average latency for 1000 transactions. Furthermore, the proposed solution reduces the encryption time to 3650 ms and the decryption time to 3968 ms as compared to the traditional implementation of the blockchain, with ensured data integrity. The outcome attests to the practicability of the employment of the application of the self-signed keys for the improvement of security, confidentiality, and integrity of data for healthcare systems. Furthermore, the proposed solution strengthens decentralized systems with the introduction of the optimized mechanism of cryptography that maintains efficiency with the guarantee of security, introducing a practical mechanism for the protection of confidential medical data for real-world systems.

SiAkif-Bots: Gemini AI for Academic Service Chatbots

2025-03-06T11:58:16+07:00

Academic services are an important element in education, as they provide students with access to information and support. At Telkom University Purwokerto, there are obstacles to the efficiency of academic services, especially due to information delays and the high burden of onsite services. To overcome this challenge, a Telegram-based chatbot, "SiAkif," was developed using the Large Language Model (LLM) model from Gemini AI. Gemini AI's selection is based on its ability to understand complex conversational contexts and generate accurate and relevant responses. This research aims to implement the Telegram chatbot that utilizes Gemini AI for Indonesian-language academic services. The implementation showed satisfactory results, with the chatbot "SiAkif" recording an average BLEU score of 0.88, which reflects good performance and response. This chatbot effectively reduces information delays, expands service accessibility, and improves student experience in interacting with institutions. Through "SiAkif," the institution is expected to strengthen the interaction between students and academic services, making it a potential solution for digital transformation in education.

The Comparison of Activation Functions in Feature Extraction Layer using Sharpen Filter

2024-10-19T12:22:55+07:00

Activation functions are a critical component in the feature extraction layer of deep learning models, influencing their ability to identify patterns and extract meaningful features from input data. This study investigates the impact of five widely used activation functions—ReLU, SELU, ELU, sigmoid, and tanh—on convolutional neural network (CNN) performance when combined with sharpening filters for feature extraction. Using a custom-built CNN program module within the researchers’ machine learning library, Analytical Libraries for Intelligent-computing (ALI), the performance of each activation function was evaluated by analyzing mean squared error (MSE) values obtained during the training process. The findings revealed that ReLU consistently outperformed other activation functions by achieving the lowest MSE values, making it the most effective choice for feature extraction tasks using sharpening filters. This study provides practical and theoretical insights, highlighting the significance of selecting suitable activation functions to enhance CNN performance. These findings contribute to optimizing CNN architectures, offering a valuable reference for future work in image processing and other machine-learning applications that rely on feature extraction layers. Additionally, this research underscores the importance of activation function selection as a fundamental consideration in deep learning model design.

Integrating Fibonacci Retracement To Improve Accuracy of Time Series Prediction of Gold Prices

2025-01-18T12:06:17+07:00

The prediction of gold prices is crucial for investors and policymakers due to its significant impact on global financial markets. Machine learning and deep learning have been used for predicting gold prices on time series data. This study employs MLR, SVM and CNN LSTM with Fibonacci retracement levels to forecast gold prices based on time series data. The experiment results demonstrate that combining Fibonacci retracement with model prediction significantly enhances predictive performance compared to prediction without Fibonacci. The use of Fibonacci levels has resulted in a higher R² score and lower RMSE score showing that Fibonacci levels influence the accuracy of gold price predictions and strengthen the overall reliability of gold price forecasts. The findings underscore the potential of combining machine learning models with technical analysis tools in financial forecasting. Integrating the Fibonacci retracement level offers valuable insights for market participants, enabling more informed investment decisions and effective risk management strategies.

Flexible Job Shop Scheduling Optimization Using Genetic Algorithm For Handling Dynamic Factors

2024-09-19T15:50:09+07:00

This research introduces the Genetic Adaptive Scheduling System (GASS), a novel framework designed to optimize scheduling in Flexible Job Shop Scheduling Problems (FJSP). Due to its complexity, FJSP presents significant challenges stemming from machine flexibility, dynamic routing, and operation precedence constraints. GASS addresses these challenges by incorporating real-time, dynamic data, enabling the system to adapt to machine downtimes, fluctuating job priorities, and process variability. Leveraging advanced genetic algorithm techniques, GASS integrates enhanced mutation and selection processes that dynamically adjust setup times, prioritize urgent tasks, and balance machine workloads to minimize makespan effectively. Empirical results demonstrate that GASS achieves up to a 45.3% reduction in makespan within the flexible packaging industry, showcasing its ability to enhance scheduling efficiency and adaptability. The research highlights the system’s scalability and potential applicability across diverse industries, including printing, electronics, pharmaceuticals, and food manufacturing, where operational flexibility and efficiency are critical. By bridging existing gaps and integrating real-time constraints into scheduling models, GASS provides practical solutions for modern manufacturing environments. The findings contribute to the advancement of optimization techniques in FJSP, offering valuable insights for researchers and practitioners seeking efficient, scalable, and adaptive scheduling systems.

Design and Implementation of HMI For Monitoring The Imbalance of Current and Voltage Based on Calculation of Maximum Deviation Mean Value Method

2025-03-02T13:51:08+07:00

Voltage and current (V-I) imbalance in a three-phase power system can cause decreased efficiency, increased power losses, equipment heating, induction machine faults, and neutral currents. The main causes of this problem are uneven load distribution, phase failure, or network disturbances. Therefore, monitoring imbalance is critical to determine the right corrective steps. This study aims to design and implement a Human-Machine Interface (HMI) as a tool to monitor voltage and current imbalance using the Calculation Maximum Deviation Mean Value (CMDMV) method. This method calculates the maximum deviation of V-I from each phase to obtain an accurate imbalance value. Current and voltage sensors are used to collect real-time data, which are then processed using CMDMV in the HMI software. The results are displayed in the form of graphs, status indicators, and percentage figures, then compared with a power quality analyzer for accuracy validation. The results show that this HMI system can display V-I imbalance in real-time with a reading error rate when the imbalance condition is below 5%, and when it detects an imbalance in V-I, the indicator turns yellow (WARNING). With the creation of this device, it can help identify V-I imbalances in each phase.

Exploring the Effectiveness of VR-Based Educational Games in Improving Student Engagement and Comprehension in Computer Architecture

2025-04-29T13:18:46+07:00

This study examines the effectiveness of integrating VR-based educational games into the Computer Architecture Organization course to enhance students’ understanding, motivation, and overall learning experience. VR-based learning offers an immersive environment that allows students to engage interactively with complex computer architecture concepts, potentially bridging the gap between theoretical knowledge and practical application. Data were collected from 100 students through a post-test survey, focusing on three primary variables: student understanding, motivation, and effectiveness of VR games. Using Structural Equation Modeling (SEM) with SmartPLS, we analyzed the relationships among these variables. The findings indicate a significant positive effect of motivation and understanding on the perceived effectiveness of VR-based educational games, with motivation having a stronger impact. These results underscore the value of VR technology in enhancing student engagement and comprehension in technical education. This study suggests that VR gamification can be a valuable tool in modernizing educational practices, particularly in areas requiring high engagement and complex conceptual understanding.

Sentiment Analysis of Emoji and Latinized Arabic in Indonesian Youtube Comments: A LABERT-LSTM Model

2025-03-02T14:16:56+07:00

This study addresses the challenges of sentiment analysis on Indonesian-language YouTube comments, which are complex due to the use of dialects, slang words, emojis, and Latinized Arabic text. The proposed LABERT-LSTM model integrates BERT for deep feature extraction and Bi-LSTM to capture word sequence context effectively. The dataset comprises 24,593 YouTube comments from five renowned Islamic preachers discussing the topic of “tahlilan”. After data preprocessing, the model was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrate that LABERT-LSTM achieved an accuracy of 0.95756, precision of 0.94014, recall of 0.91815, and an F1-score of 0.92868, outperforming standalone BERT and Bi-LSTM models by reducing misclassification and improving predictions for negative, positive, and neutral sentiment classes. Future research recommendations include expanding the dataset to other social media platforms, adopting advanced NLP techniques, conducting studies in other languages, and optimizing the model for enhanced performance and computational efficiency.

Magic Boom Chemical: A Tracking Marker-Based Approach in Developing Chemical Molecule Textbook

2025-01-18T11:52:50+07:00

In Traditional teaching methods for chemical molecules, which rely on textbooks and physical modeling tools, face challenges in providing an engaging and comprehensive learning experience. This study introduces "Magic Boom Chemical," an augmented reality (AR)-based educational tool utilizing marker tracking to display three-dimensional molecular structures. A quasi-experimental method was employed, involving two groups of high school students: an experimental group using the AR application and a control group using conventional teaching methods. Pre-tests and post-tests were conducted to assess the effectiveness of the tool. Results indicated a significant improvement in students’ understanding and engagement when using the AR-based media, with the experimental group achieving higher average post-test scores. The study concludes that integrating AR into educational materials can enhance learning outcomes and increase students’ motivation. These findings highlight the potential of AR technology as an innovative solution for improving science education and suggest further research to refine and expand its applications.

Introducing B-Sweep: An Innovative Bird-Repelling Device Powered by Solar Cells and Sound Waves, Efficiently Protecting Against Bird Strikes in Airport Airsides

2024-04-06T12:00:00+07:00

The study aims to create a bird-repelling device called Bird-repelling with solar cell and sound wave energy efficient protection (B-SWEEP) to reduce the likelihood of bird strikes. The study employs a Research and Development (R&D) methodology, whereby data is gathered through a site survey to identify bird-highlighted places, including documentation of the quantity and variety of birds observed during a specific timeframe at the Politeknik Penerbangan Palembang—analysis conducted by measuring the range and power consumption effectiveness. The findings indicate that B-SWEEP generated sound waves at a distance of 100 meters with a maximum frequency of 500 Hz. The B-SWEEP test field is used six times, with a five-meter buffer between each experiment and the user. The purpose is to evaluate how well B-SWEEP can receive Wi-Fi. This test enables us to determine the distance at which B-SWEEP began to perform poorly when the user gave commands, and it helps improve B-SWEEP even further as a result of this research. It demonstrates how B-SWEEP deters birds in a field. Using solar panels for charging, B-SWEEP can function without electricity for one hour. Using an ESP32-CAM microcontroller to implement Internet of Things (IoT) technologies. Airport managers can recognize the kinds of birds that fly into the airside area with the help of this micro camera, including sparrows that are immediately linked to a telephone. Due to its sound and wave, the sweep audio signal might deter birds from nesting and visiting the airport airside. Integrating IoT-based automation technologies with renewable energy sources can facilitate the execution of the eco-airport initiative. Based on testing results and feedback from 51 respondents, improvements to B-SWEEP include enhancing usability by simplifying interface complexity and adjusting visual elements for greater appeal. Furthermore, expanding its capabilities to repel a wider range of animals through tailored ultrasonic technology would enhance its effectiveness and user satisfaction, making B-SWEEP a more versatile solution.

The Role of Communities of Practice in Increasing Organizational Maturity Levels towards Digital Transformation: Case Study of Employee Cooperative

2024-09-18T19:10:49+07:00

Organizations looking to improve operational efficiency, customer service and innovation begin by embarking on digital transformation journey. Such transformation necessitates a radical shift in the way organizations execute and behave. Communities of Practice (CoPs) are central to this wavy journey towards agility. This paper presents the role of CoP to assist in advancing organizational maturity quality for successful digital transformation, focusing from a social learning perspective. This paper conducted a case study with an employee cooperative of one state-owned enterprise based in Jakarta, Indonesia using literature reviews and semi-structured interviews involving the perspective from managers, IT staffs as well general staff. This paper utilized the Deloitte Digital Maturity Model (DMM) to evaluate digital maturity levels in five areas: Strategy, Culture, Organization, Technology and Customer. Results suggest that CoPs encourage a learning, innovative culture supported by management strong support and flexible structures with digital collaboration tools embedded in this comprehensive structure as well purposeful continuous training. The cooperative's digital maturity is in the initial stages, with five areas identified for improvement in strategy integration, cultural openness, organizational structure, technological adoption, and customer engagement. This research underlines that CoPs play an important role in enabling collaborative learning as well as digital maturity. Future research could elaborate on the long-term consequences of CoP and extend comparisons across various industries as well as consider how emerging technology influences a variety of factors related to effectiveness of CoPs.

An Empirical Study for Estimating Ultimate Bearing Capacity of Concrete Small-Pile Cluster in Soft Clays

2024-04-26T20:03:02+07:00

Soft subgrades with shear strength, c_u of 10 kPa to 25 kPa are given low bearing capacity and high settlement. Local government has been built infrastructure of embankment such unpaved road on the soft subgrade by using timber and bamboo materials (called “Cerucuk”) are very familiar for Indonesian local people. By developing load capacity of floating pile type, bamboo pile cluster was applied for Tol road in North the Java Island. However, bamboo material limited of life construction inside the soft ground, and exploitation of timber pile was violated environmental issue. Therefore, this research method is developed a new geometrical small - cluster pile by using concrete from local material or Igneus stone such as gravel and sand produced in quarry Ternate Island. Ultimate bearing capacity of single small - cluster pile installation modelled in soft subgrade tank. Then, observed by several block concretes and it is presented by empirical formulae. Finally, the ultimate load capacity of small-pile cluster in soft subgrade is obtained Q_u of 15.92 kN and 19.79 kN for observation and calculation, respectively. Rearrange of piles can be increased load capacity of small-cluster pile in soft subgrade as spacing single pile by spacing 5D_eq to 3D_eq. Mostly load capacity of small-pile clusters should be calculated by using empirical method to provide bearing capacity based on geotechnical rule with laboratory and field soil investigation data and load standard for unpaved roads.

Development of Rammed Earth Material Technology by Utilizing Plastic Waste as Reinforcement on The Partition Walls of The Building Room

2025-01-18T12:02:52+07:00

In order to improve the compressive and bending strength of rammed earth materials for use in partition walls, this study investigates the incorporation of plastic trash. The goal of the research is to enhance the performance of sustainable construction materials while addressing the environmental problem of plastic waste. Using a Universal Testing Machine (UTM), compressive and bending strength tests were performed after 30 days for rammed earth mixtures containing four different amounts of plastic trash (0%, 1%, 3%, and 5%). According to the findings, adding plastic trash can increase compressive strength by up to 3%, reaching a maximum strength of 5.17 MPa. However, compressive and bending strength significantly decreased when the plastic percentage was increased over 3%, with the 5% plastic showing the worst performance. According to these results, plastic trash can enhance material performance, but its use requires careful optimization. By putting forth a novel technique for recycling plastic trash, the study supports sustainable building practices and provides a workable substitute for non-load-bearing applications such as partition walls. This study advances our understanding of green building technologies and offers workable ways to cut down on plastic waste in the building industry.