Journal of Applied Engineering and Technological Science (JAETS)

A New Strategy to Improve the Performance of Informed RRT* Algorithm in Solving the Global Path Planning of Mobile Robot

2024-11-10T09:58:49+00:00

Informed RRT* has been mentioned as a great method to find feasible and optimal solution of path planning. Technically, it uses the prolate hyper-spheroid and a centralized optimization strategy to gain the optimality of path. This optimization process is started when the initial feasible solution is found. Conventionally, the traditional procedure of RRT* is used to connecting starting point and goal point feasibly. Therefore, it is not suppressing if the optimization process begins later in large coverage of path planning problem. For this reason, a new strategy needs to propose with an objective to speed up the convergence rate by reducing the inefficiency of its blind sampling. Sequentially, it is conducted by integrating the bias technique and constraint sampling to replace the traditional sampling method. Next, the nearest node's ancestor is taken into consideration up until the first stage of choosing the parent is less expensive then RRT*. Regarding to these offers and the comparative results, the performance of the proposed method has shown better performance compared to its predecessor in terms of optimality, indicated by a decrease in finding the initial path by an average acceleration of 47.90% and a convergence rate indicated by an average path cost decrease value of 3.94%.

Comparison of Various Sky Model for Daylighting Availability Inside The Classroom with Bilateral Opening Typology in The Tropics

2025-01-23T18:57:32+00:00

This study compares daylighting performance under four sky models of a classroom in tropical climates to understand the differences in illuminance and uniformity values. This research is significant as it can inform the relevance of the widely used static metric, such as the daylight factor, for daylight performance evaluation in tropical climates in comparison with the climate-based sky model which is utilized for dynamic metric calculation. Computational simulation was employed to achieve the objective. Grasshopper-Rhinoceros was utilized for the classroom model, while Radiance was employed for sky modelling and daylight simulation. The results indicated that static sky models exhibited greater discrepancies in their average illuminance and uniformity values compared to climate-based or dynamic sky models. The pervasive utilization of static metrics, such as the daylight factor, for evaluating daylighting performance within a space may necessitate reconsideration in tropical climates, given the higher error rates observed in this study for a classroom with bilateral opening design.

Quality Control in Small and Medium Enterprises: A Study of Present Challenges and Future Opportunities

2025-03-06T11:07:34+00:00

Quality control is a critical foundation for establishing effective and efficient systems across industries, but small and medium enterprises (SMEs) face unique challenges in implementing effective quality control due to resource constraints. This study aims to identify the specific needs through demographic and operational analysis, and to develop a compact, adaptable Quality Management Framework to enhance their operational excellence. Survey responses from 50 SME managers were analyzed using cluster analysis, utilizing Ward's method and K-means to categorize businesses based on their quality control practices. Findings revealed three distinct SME clusters emerged, with systematic quality practices correlating to 71% higher customer satisfaction. These findings highlight significant variations in managerial perceptions, needs, and priorities. This research proposes an adaptable quality management framework tailored for SMEs and recommends the integration of technology to enhance scalability and effectiveness. Thus, this study addresses a theoretical gap in SME quality control system design while offering practical, actionable insights for enhancing quality management processes in diverse industrial contexts.

Enhanced CSMA/CA Protocol-Based Optimal Robust Dynamic Query-Driven Clustering for Improved QoS in Heterogeneous WSNs

2025-08-25T13:22:43+00:00

Heterogeneous Wireless Sensor Networks (HWSN) are basically decentralized and distributed systems that playing a crucial role in numerous Internet of Things (IoT) applications, enabling efficient monitoring and data collection. However, these networks often suffer from high latency, routing overheads, and energy consumption. To meet these challenges effectively, This article proposes an enhanced CSMA/CA protocol based on an Optimal Robust Dynamic Query-Driven Clustering Protocol (ECODQC) model. The enhanced model includes two key components: the improved CSMA/CA protocol, which reduces network collisions, lowering delay and overhead during communication, and the Optimal Robust Dynamic Query-Driven Clustering (ODQC) protocol, which efficiently reduces energy consumption among sensors. In the first phase, the modified CSMA/CA protocol focuses on analyzing communication delays, defining dynamic data transmission, and evaluating data delivery beyond predefined times. In the second phase, the ODQC protocol addresses optimal load balancing and the dynamic process of cluster head selection, aiming to reduce energy consumption during sensor communication. The proposed techniques demonstrate superiority over conventional protocols and are recommended for enhancing the overall quality of service in decentralized, distributed HWSN-based IoT networks. The ECODQC model is compared against existing methods using the NS2 simulation platform in two scenarios: the varying numbers of nodes and varying speeds. The performance parameters of this proposed model are analyzed in terms of energy efficiency, cluster head efficiency, data success rate, computational delay, and node throughput. The Results demonstrate that ECODQC proves to be superior compared to existing techniques in terms of energy efficiency of 432.23 J, low latency of 85.23 ms, and increased throughput of 813.77 Kbits/s. With these observations, the possibility of using ECODQC with a high level of applicability in real-time IoT scenarios is evident

Redesigning Passenger Distribution System of KRL Commuter Line: An Integrative Approach

2025-03-06T12:51:08+00:00

Commuter line electric rail (KRL) has become a critical mode of public transportation supporting urban mobility in densely populated areas. However, the rapid growth of urban populations and the corresponding increase in daily commuters have created significant challenges in delivering optimal and comfortable services due to overcrowding. This study addresses these challenges by enhancing passenger comfort on KRL commuter lines through the redesign of the passenger distribution system considering personal space along with passenger flow management. An integrative approach combining ergonomic approach in determining carriage capacity, passenger flow management, and simulation-based analysis was employed. Empirical data were collected through observation, empirical survey, and direct anthropometric measurement. Observations on passenger density were conducted on the Cikarang line during peak morning hours, focusing on mixed-gender carriages. Anthropometric measurements involving 238 subjects alongside carriage dimensions were analyzed to determine the capacity of carriage using ergonomic principles of personal space The results revealed that the carriage capacity of 150 passengers balancing comfort and efficiency. An innovative passenger distribution system deploying queuing system equipped with integrated sensors providing real-time number of passengers and innovative automatic door-closing mechanism at the carriages were proposed and tested under current passenger density and determined carriage capacity using discrete-event simulation implemented in Arena software. This study provides novel contribution both practically and theoretically demonstrating the application of ergonomics and sensor integration in public transportation system design for improving commuter comfort and safety in highly congested urban transport systems. Future researches are also discussed.

Fabrication and Measurement of a Circularly Polarized All-Metal Patch array and Single PRS Layer based Fabry-Perot Antenna for NASA Interplanetary CubeSat missions

2025-03-06T12:32:31+00:00

When it comes to pushing the limits of small-scale technologies for interplanetary space missions, NASA has demonstrated that the dream is achievable. NASA has launched a number of interplanetary CubeSat missions, including as MarCO A and B, INSPIRE, LOGIC, and LunaH-map CubeSats, successfully during the past 10 years. Since the antennas of these NASA-certified satellites are responsible for defining the range of communication with Earth, they are the main topic of this study. A high gain and circularly polarized all-metal patch array with PRS-based Fabry Perot antenna is proposed in this paper for NASA’s interplanetary CubeSat missions. The strength of this approach stems from its dependence on a wholly novel concept for developing a durable antenna that is harmoniously compatible with NASA's goals. The resulting all-metal patch array was well-fabricated and tested in an anechoic chamber and using a VNA, and showed measured realized gain of 35.84 dBi and AR of 1.629 dB at 11.5 GHz with 3dB-AR bandwidth higher than 3 GHz. Additionally, the Fabry-Perot design enhances the gains, achieves AR of 0.29 dB at 12.1 GHz, and exhibits RHCP and LHCP making this new technique as suitable candidates for NASA's interplanetary CubeSat missions.

Optimization of Convolutional Neural Network for Classification of Hydroponic Vegetable Cultivation Using Machine Learning

2025-07-07T21:02:02+00:00

In an effort to apply applied product innovation and support the improvement of hydroponic vegetable cultivation, it is based on several things. Among them are changes in the texture of the year, stems and vegetable quality. At this time the problems faced by hydroponic vegetable pickers, especially banyumas village youth organizations who have UMKM hydroponic vegetable cultivation. This situation will have an impact on problems and losses that result in a lack of yield and quality of harvested vegetables if not resolved quickly. The results of this study resulted in optimal accuracy performance in the classification of hydroponic vegetables with CNN, this study also successfully classified normal vegetables with vegetables affected by disease. This research produces accuracy in the first test 73% and the second test 92%.

From Exclusion to Empowerment: Redesigning E-Learning to Mitigate Risks for Disabled Students

2025-09-20T15:39:14+00:00

This study explores how e-learning platforms can be redesigned to empower disabled students by mitigating their educational risks, such as accessibility barriers, communication challenges, and unequal participation. Using a qualitative approach, the research combines literature reviews, case studies, and user feedback to analyze current e-learning environments. It evaluates how AI technologies like ChatGPT can facilitate content accessibility, improve interaction, and support personalized learning experiences for disabled students. This study identifies key risks: limited content adaptability, poor user interface design, and social isolation, which hinder disabled students' academic engagement. It proposes a framework integrating assistive AI tools to transform these platforms into inclusive, low-risk learning environments. Results indicate that AI-driven adaptations can enhance content comprehension, foster collaboration, and improve learning outcomes for disabled students. This study focuses on higher education contexts and may require further validation across diverse educational levels and technological infrastructures. Additionally, reliance on evolving AI technology presents challenges related to affordability and accessibility. This study contributes to the growing field of inclusive education by offering a novel, AI-supported framework to redesign e-learning platforms. It addresses the gap between technological advancement and practical accessibility solutions, emphasizing risk mitigation for disabled students. The findings provide valuable insights for educators, policymakers, and technology developers aiming to create equitable, empowering learning experiences for all learners.

Impact of SWCC Measurement Methods on the Numerical Investigations of Unsaturated Soils

2025-07-07T21:48:21+00:00

The soil-water characteristic curve SWCC, also known as the soil-water retention curve SWRC, describes the relationship between soil suction and water content in unsaturated soils. Many studies rely on specific laboratory testing methods without considering their impact on numerical simulations. This research investigates the effect of different SWCC measurement techniques on numerical modeling outcomes. Soil samples from the Tebessa region were tested using three methods: the filter paper method, the osmotic technique, and the axis translation technique. Numerical simulations were then conducted using FLAC software to analyze the influence of these methods on the hydromechanical properties of unsaturated soils and slope stability under two days of wetting conditions. The results indicate that the selected SWCC measurement method significantly affects the predicted vertical suction profile and influence the factor of safety (FOS) in slope stability analysis. This suggests that while accurate suction measurements are crucial for understanding soil behavior, their overall impact on slope stability predictions varies depending on the testing technique employed.

Magie Broom: Revolutionizing Cleaning with User-Centered Ergonomic Design

2025-08-25T13:16:08+00:00

Magie Broom is an innovative cleaning tool designed by considering several principles such as ergonomics and anthropometry. This study aims to determine the optimal ergonomics design that can minimize the risk of musculoskeletal disorders (MSDs) that often arise from the use of traditional cleaning tools. The development of this tool involves several stages: literature review, field observations, and detailed design using the Quality Function Deployment (QFD) method, followed by testing. Anthropometric principles, especially for determining the optimal length and grip, are carefully considered to ensure the design meets user needs for ease of use, comfort, and cleaning effectiveness. The design The Magie Broom prototype was tested by various user groups such as housewives and students to obtain input used in refining the design. The test results showed that this tool was able to increase cleaning efficiency by provide 3in1 function and modularity. Its also provide comfort for users and reduce the potential for MSDs by decrease REBA score from 11 to 3. The design is very easy to carry, modular, and equipped with a rechargeable battery. The Magie Broom serves as a promising model for ergonomic product development especially in houshold tools, illustrating how thoughtful design can minimize physical strain and injury risk. Magie Broom offers a practical solution for everyday cleaning needs and has great potential to be further developed and marketed widely.

Enhancing the Effectiveness of the YOLO Model Through Caladium Leaf Images Generated by Generative Adversarial Networks

2025-03-04T12:25:21+00:00

The need for ornamental caladium plants is very popular, but there are several obstacles to recognizing its type. Caladium species classification using AI is needed to overcome the problem of misidentification among enthusiasts. This study uses the Generative Adversarial Network (GAN) algorithm to generate new images from the Caladium dataset: Amazon Caladium, Bicolor Caladium, White Queen Caladium, and Skull Caladium. We combine GAN with YOLOv5 to detect Caladium in real time to improve accuracy. The quality of the generated images is evaluated using the Kernel Inception Distance (KID) method, with the highest scores of 0.2320 for Amazon Caladium, 0.1966 for Bicolor, 0.1713 for Skull, and 0.1857 for White Queen, indicating close similarity to the original images. We chose the best model to generate three datasets: Original Dataset, Mixed Dataset (original images plus GAN-generated images), and Dataset consisting mainly of GAN images. The Mixed Dataset achieved the best results, with a mean Average Precision (mAP) of 0.695 for an Intersection over Union (IoU) of 0.50:0.95 outperforming the GAN dataset and the original Dataset. This training used 50 epochs, a learning rate of 0.0003, and a batch size of 16, to obtain the best model and significantly improve Caladium detection. From this experiment, it was found that the GAN, combined with the original data, was able to support the accuracy of YOLOv5 for real-time caladium classification and was also able to create new images that resembled the original leaves. In the mobile application, this model allows real-time identification of Caladium types, making it easier for users to buy Caladium according to the desired type.

Enhancing Intrusion Detection System Performance Using Reinforcement Learning : A Fairness-Aware Comparative Study on NSL-KDD and CICIDS2017

2025-11-17T14:26:07+00:00

Conventional Intrusion Detection Systems (IDS) often fail to generalize in dynamic network environments, facing challenges with evolving attack patterns and class imbalance. This study aims to evaluate and compare the effectiveness of three Reinforcement Learning (RL) paradigms to enhance IDS adaptability and accuracy against these challenges. This research employs a comparative experimental design, implementing Q-Learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO). These algorithms were systematically evaluated using the NSL-KDD and CICIDS2017 benchmark datasets to represent both legacy and modern network traffic. A fairness-aware evaluation framework was applied, prioritizing the Matthews Correlation Coefficient (MCC) as a primary metric alongside accuracy to ensure robust performance assessment against skewed class distributions. Experimental results demonstrate that PPO significantly outperforms value-based algorithms such as Q-Learning and DQN. On the high-dimensional CICIDS2017 dataset, PPO achieved the highest detection accuracy (96.3%) and MCC (0.913). Confusion matrix analyses confirmed PPO’s capability to simultaneously minimize false positives and false negatives. Conversely, Q-Learning exhibited poor generalization on complex data, while DQN showed improved performance due to deep value approximation but remained less stable than PPO. These findings imply that policy-gradient methods like PPO are superior for real-world IDS deployments where scalability, adaptability, and low error rates are critical. Theoretically, the results suggest that stochastic policy optimization handles complex, continuous state spaces more effectively than traditional value-estimation approaches. This study contributes a rigorous head-to-head comparative analysis of RL algorithms across multiple standard datasets using fairness-aware metrics. It bridges the research gap found in previous studies that often evaluated algorithms in isolation or relied on accuracy metrics that can be misleading in imbalanced security contexts.

Swarm Intelligence Optimisation Vs Deep Learning: Energy-Aware Strategy for Disaster Communication Networks

2025-10-01T14:05:03+00:00

In disaster-prone environments, communication networks must sustain operation under severe constraints such as limited energy, damaged infrastructure, and uncertain topology. This study compares Deep Learning (DL) and Swarm Intelligence Optimisation (SIO) as energy-aware strategies for disaster communication. While DL excels in data-rich prediction and situational analysis, its reliance on high-performance hardware and stable connectivity restricts its feasibility during real-time emergencies. In contrast, SIO provides decentralised coordination, lightweight computation, and adaptive routing, making it better suited to infrastructure-independent device-to-device (D2D) networks when central control collapses. A comparative conceptual framework was developed to evaluate both paradigms across five criteria: energy efficiency, adaptability, computational demand, response time, and scalability, based on recent literature between 2023 and 2025. Findings show that SIO demonstrates superior suitability for energy-limited and time-critical operations, while DL remains valuable for pre-disaster prediction and post-event analysis. Hybrid DL–SIO frameworks bridge both paradigms, enabling predictive–adaptive synergy across the disaster lifecycle. The study contributes a context-aware guideline for algorithm selection, shifting the focus from technology-centric performance toward environment-centric deployment in future energy-efficient, resilient, and adaptive disaster communication systems.

CNN-Based SIBI Sign Language Recognition Alphabet: Exploring the Impact of Hardware on Model Training

2025-03-09T13:38:50+00:00

The recognition of Sign Language Alphabets (SLA) plays a vital role in human-computer interaction, especially for individuals with auditory disabilities. This study aims to evaluate the impact of different hardware configurations—specifically CPU, GPU, and memory setups—on the training efficiency and recognition performance of a Convolutional Neural Network (CNN)-based model for SLA using the SIBI dataset. The novelty of this research lies in its focus on hardware-aware deep learning optimization for Indonesian sign language (SIBI), an underexplored area. The model was trained on 3,468 labeled hand gesture images representing 24 SIBI alphabet signs. Experiments were conducted on CPU (Intel Xeon 2.00 GHz) and GPU (Nvidia Tesla T4) platforms using a consistent CNN architecture. The training time was significantly reduced by 45.5%, from 1 hour 39 minutes to just 54 minutes, while the accuracy remained consistent at 96.7%, showing no significant change between the two setups. These results demonstrate the significance of parallel processing and memory bandwidth in enhancing model convergence and generalization. The findings are relevant for real-time SLA deployment with hardware constraints on embedded or mobile platforms. Overall, the study underscores the importance of hardware optimization in accelerating CNN training and improving performance in sign language recognition systems.

Modern Language Techniques from Emerging Trends, Evaluation, Obstacles, to Prospects: Review

2025-10-07T03:55:22+00:00

Languages designed for a particular application domain are known as domain-specific languages (DSLs). When compared to general-purpose programming languages (GPPLs) in their field of usage, they provide significant improvements in expressiveness and usability. In order to handle the concurrent expansion of areas like cloud-native, distributed, and modular architectures, this discipline has been undergoing enormous evolution. Finding current trends, gaps, and new prospects in the field of DSLs is the aim of this review. We use a novel approach in this review by grouping the state-of-the-art studies into several groups. Furthermore, the three primary implementation issues of DSLs abstract syntax, concrete syntax, and semantics were highlighted. In particular, they are distinguished by the functions they prioritize (modeling, visualizing), the mapping outcomes (textual/graphical symbols), and their parsing and mapping approach (external/internal) between the abstract and concrete languages. Focus on the development lifecycle while keeping up with contemporary trends, obstacles, and the assessment metrics that are employed to evaluate the DSLs. We concluded by summarizing the research overview of DSLs after integrating it with the literature.

Fine-Tuning Whisper Model for Mandar Speech Recognition: Approach and Performance Evaluation

2025-07-07T21:01:29+00:00

This research focuses on the development of speech recognition technology for the Mandar language, a regional language in Indonesia with limited digital resources. The main challenge lies in the lack of local datasets and the minimal representation of the Mandar language in existing multilingual speech recognition models. This study aims to enhance the performance of Automatic Speech Recognition (ASR) systems by fine-tuning the Whisper model using a Mandar-specific dataset. The dataset consists of 1,000 audio recordings with various dialects and recording qualities, which underwent preprocessing steps such as segmentation, normalization, and data augmentation. Fine-tuning was conducted using supervised learning methods with hyperparameter optimization, resulting in a reduction of Word Error Rate (WER) from 73.7% in the pretrained model to 37.4% after fine-tuning, and an increase in accuracy from 26.3% to 62.6%. The optimized model was also compared with other ASR models, such as DeepSpeech and wav2vec 2.0, demonstrating superior performance in terms of accuracy and time efficiency. Further analysis revealed that recording quality and dialect variations significantly impacted model performance, with high-quality recordings and standard dialects yielding the best results. The model was implemented as a web application prototype, enabling efficient and near real-time transcription of Mandar speech. This research not only contributes to the development of ASR technology for low-resource languages but also opens new opportunities for preserving and utilizing the Mandar language through digital technology. For future improvements, larger datasets, more advanced augmentation techniques, and the exploration of additional language model integration are recommended.

Performance Improvement of Quality Monitoring Systems in Imbalanced Data Conditions for Fat-Filled Powder Quality in The Dairy Industry

2025-03-06T13:11:08+00:00

Fat-filled powder has the potential to substitute milk in meeting the nutritional needs of the community, but its product quality remains unstable during continuous production processes. A key challenge in fat-filled powder (FFP) production is the difficulty in quality monitoring, which is influenced by various uncertainty factors that affect product quality. Machine learning can be implemented for quality monitoring system, but the imbalanced data conditions require the development of algorithms with optimal performance. This study aims to design a quality monitoring system for FFP using a machine learning model under imbalanced dataset conditions and the influence of other uncertainty factors. A Random Forest (RF) machine learning model was developed for monitoring FFP quality. In the context of imbalanced datasets, the model was optimized through various scenarios, including data splitting for training and testing, as well as the Synthetic Minority Oversampling Technique (SMOTE) and Distribution Optimally Balanced – Stratified Cross Validation (DOB-SCV) schemes. The results showed that the SMOTE model achieved the best performance in terms of accuracy, precision, and recall with scores of 99.67%, 99.79%, and 99.24%, respectively, on the testing data. Statistically, the RF model with the SMOTE data manipulation scenario also showed significant differences compared to the DOB-SCV model and the traditional data splitting approach. The quality monitoring model for FFP developed in this study can be implemented in the dairy industry, offering more stable, accurate quality monitoring predictions that align with real conditions, helping to avoid quality uncertainties during the production process. The implementation of this model in the industry has the potential to facilitate a broader, more transparent, and optimized product quality evaluation process, which can also be conducted in real time under continuous production conditions.

Developing Industry-Relevant Soft Skills Through Peer-Engaged Project Approach (PePA): A Vocational Education Perspective

2025-03-09T14:10:25+00:00

The development of soft skills remains a critical challenge in vocational education, as the curriculum often prioritises technical competencies while neglecting systematic integration of interpersonal skills. This study aims to develop and evaluate the Peer-Engaged Project Application Model (PePA), a project-based learning framework designed to enhance communication, teamwork, and problem-solving through structured peer engagement. Employing a Research and Development (R&D) approach based on the ADDIE model, the research was conducted in the D4 Mechanical Engineering Program at Yogyakarta State University. The PePA model was tested on a group of 15 students in the Fabrication Construction Practices course. Data were collected through Likert-scale questionnaires, structured observations, and semi-structured interviews, and analysed using descriptive and inferential statistics, along with qualitative techniques. The implementation of PePA resulted in improved performance in soft skills, with average scores increasing from 3.03 to 3.65 (communication), 3.20 to 3.78 (teamwork), and 3.13 to 3.70 (problem-solving). The model was also rated as “very valid” by expert evaluators, with an average validation score ranging from 3.58 to 3.85. These findings suggest that PePA is a feasible and effective learning model for strengthening vocational students' soft skills in alignment with industry expectations. The model has potential applicability beyond engineering education and supports policy recommendations for integrating soft skills into vocational curricula.

Hybrid Optimization Model for Integrated Image Data Extraction Expert System in Rice Plant Disease Classification

2025-03-06T11:07:23+00:00

The purpose of this study is to increase the accuracy for rice plant disease classification by developing a hybrid optimization model using Convolutional Neural Network (CNN) in combination with Extreme Learning Machine (ELM), followed by Support Vector Machine. A key issue is to overcome with traditional expert systems that difficult, due the variation differences and complex among rice plant image data set. For feature extraction, plant images are passed through CNN and for classification ELM & SVM used. Experimental results show the best accuracy of 98.63% is attained using CNN+ELM model on images resized to 100x100 pixels and has precision, recall, F1-Score all at value=0.99 By comparison, the CNN+SVM model achieves an accuracy of 91.92% using that same image size. Top AbstractIntroductionMethodsResultsDiscussionConclusionReferencesOverall, the proposed CNN+ELM combination can classify rice plant diseases better than using only a conventional approach (CNN) through various results from devices with limited computing power. The study presents a novel plant disease detection system that can be utilized for the development of precise tools to help improve agricultural management practices.

Leveraging Intranet Quality for University Financial Sustainability: The Mediating Role of Enterprise Risk Management

2025-11-14T10:40:25+00:00

This study examines how intranet quality affects the financial performance of Malaysian public universities, filling a crucial gap in understanding how internal digital infrastructure supports institutional sustainability. It highlights Enterprise Risk Management (ERM) as a mediating factor translating intranet quality into measurable performance results. A cross-sectional survey was conducted with 210 participants, including risk committees, internal auditors, and top management from 20 public universities in Malaysia. This study used purposive, stratified, and census sampling methods. Intranet quality was evaluated across six key areas: collaboration tools, risk management application, access to proper risk data, interaction in risk problem-solving, communication among the risk committee, and risk management controls. ERM implementation was measured using ISO 31000-aligned standards, while university financial performance was assessed through five income sources: research projects, consultancies, public and private funding, commercialisation, and program offerings. Covariance-based structural equation modelling (CB-SEM) was employed for analysis. Findings reveal that intranet quality significantly improves ERM implementation, positively impacting financial performance. ERM partially mediates this relationship, with more substantial indirect effects than direct ones. This study emphasises the strategic importance of digital infrastructure and risk governance in boosting institutional effectiveness. It proposes a socio-technical model that helps university leaders leverage intranet systems to enhance risk resilience and long-term financial sustainability.

Clustering Analysis of Patchouli Plantations for Sustainable Patchouli Oil Supply Chain Using K-Means Algorithm

2025-07-07T21:31:12+00:00

Growing demand for patchouli oil has undoubtedly become an opportunity for the patchouli industry, particularly in Aceh, which supplies about 80% of Indonesia’s patchouli oil in the global supply chain system. However, the opportunity is often misguided by farmers and even the government, which implements various programs related to patchouli cultivation without identifying the potential land that is suitable to be used for it. The condition indicated that not every land is suitable for patchouli cultivation. Thus, it is necessary to cluster the distribution of existing patchouli plantations. The clustering aims to identify the existing patchouli plantations that have the potential for replication. This study uses the K-Means method that combines variables (the planting land, the harvesting land, and total production) to provide information on the plantation’s potential scale in each region. The clustering measurement pointed out that the plantation in South Aceh Regency has the most potential land for sustainable cultivation, followed by several other areas included in Cluster 2 and Cluster 3. The study’s result is essential in contributing significantly to optimizing patchouli cultivation management sustainability to fulfill Aceh Province’s role as the best quality patchouli oil supplier.

Acceptance of Culture Mobile Tourism Among Tourist: A Model Development Study

2025-09-20T15:36:46+00:00

This study is a preliminary study on the development of a new model by combining and integrating existing models, namely the Technology Acceptance Model (TAM) and Hofstede's Culture Dimension. The development of this model was carried out using IPO logic (input-process-output) and a causal model by combining, adopting, and adapting previous models. The influence of the formed path consists of 17 links and produces 10 variables. The existing variables will be formed into 40 indicators. This study examines the impact of cultural values on tourists' perceptions of usefulness, ease of use, trust, and conditions that support mobile tourism technology. This study also investigates the mediating role of cultural dimensions on behavioral intentions to use mobile tourism. This study intends to provide important information in developing and implementing more effective and sensitive mobile tourism by incorporating cultural dimensions into the TAM framework. These findings will provide extensive information to stakeholders about tourism improvement and the importance of cultural adaptation in adopting mobile tourism.

Social Media Marketing and Business Performance: Analyzing The TOE Framework and Relational Capability

2025-07-07T19:00:55+00:00

The rapid growth of social media is presenting micro and small enterprises (MSEs) with new opportunities to expand the market reach, strengthen customer engagement, and enhance competitiveness. In Indonesia where MSEs dominate the business landscape, government initiatives strongly motivate digital adoption while the practical and strategic use of social media marketing (SMM) remains limited. Therefore, this study aimed to investigate the impact of SMM adoption on business performance by extending the Technology–Organization–Environment (TOE) framework with relational capability (RC) as a mediating construct. A quantitative design was adopted using Structural Equation Modeling (SEM) with Smart PLS 4 on survey data from 300 food-based MSEs in Central Java. The results confirmed that organizational and environmental factors significantly influenced behavioral intention and strongly predicted SMM usage. SMM was also found to have a positive impact on both RC and business performance, while RC served as a significant mediator between SMM and performance. This study contributed theoretically by integrating TOE and RC to explain digital adoption outcomes in resource-constrained contexts, and practically by outlining the need for digital upskilling and relationship-building strategies to maximize the performance benefits of SMM adoption.

Artificial Intelligence and Optimization of Eeverse Logistics: An Analysis in the Aquatic Industry of The Mekong Delta

2025-09-20T15:32:44+00:00

In recent years, artificial intelligence (AI) has become an important technology that enhances the competitive advantages for businesses. This study investigates the application of artificial intelligence and how it can optimize reverse logistics for the aquatic industry in the Mekong Delta. It also explores the current situation in applying AI, its benefits, and challenges when using AI in reverse logistics for aquatic enterprises. The research uses qualitative and quantitative methods to collect data from interviewing managers, logistics staff, and technicians to deliver a survey to 41 seafood businesses. Results show AI applications in forecasting, storage, and recycling can cut operational costs by over 10% for 46.3% of firms and improve recovery time by over 10% for 56.1%. Benefits also include higher operational efficiency and better environmental performance. However, challenges persist in system integration, data access, and workforce readiness. The study provides practical recommendations, including enhancing AI workforce training, system integration, and collaboration with technology providers, to help seafood companies overcome barriers and maximize the benefits of AI in reverse logistics.

The Impact of Virtual Laboratories on Student Motivation and Academic Performance: An Integrated Fuzzy-Sem and Machine Learning Study

2025-11-17T12:17:53+00:00

This study explored the impact of virtual laboratories (VLs) on university learning and seeks to fill a gap in the literature: most VL research reports positive outcomes, but rarely explains why they occur or whether psychological mechanisms generalize predictively. The solution comes from a synthetic model combining Fuzzy-SEM, which is great for modelling uncertainty within Likert-based motivation and engagement constructs, with supervised machine learning models that provide causal explanation combined with predictive validation. We analyzed data from 432 undergraduates combining VL usage logs, motivation–engagement surveys, and official academic records. Fuzzy-SEM confirmed a mediated motivation–engagement–performance pathway, which confirms that VLs significantly boost performance primarily by converting motivational activation to sustained engagement. Predictively, the 1D CNN better fitted the classical ML models (AUC-ROC = 0.94) suggesting the possibility of early identification of at-risk students through behavioural and affective proxies. Practical implications should be to apply VLs as complementary motivational approaches to training practice and to monitor prediction weekly for intervention. In theory, the study bolsters engagement frameworks by elucidating how VLs exert their effect. Methodologically, it presents an integrated Fuzzy-SEM + ML pipeline that facilitates both explanatory context and potentially deployable prediction, although it recognizes the limitation of single-institution and self-report.

Evaluation of Water Needs and Availability in The Design of The Water Needs Masterplan on The IPDN West Sumatera Campus

2025-10-07T02:06:49+00:00

The sustainable availability of clean water is crucial for supporting academic, residential, and institutional operational activities in higher education settings. In general, previous research has primarily focused on clean water planning at the city or regional level, without examining the need and availability of water on boarding campuses that have distinct consumption patterns and distribution systems. This research aims to fill the gap by conducting an integrated evaluation of water needs and availability, serving as the basis for preparing a water management master plan at the West Sumatra IPDN Campus. A mixed-methods approach is employed, combining quantitative analysis based on SNI 03-7065-2005 to estimate water needs with qualitative analysis of infrastructure conditions and water quality through field surveys, interviews, and laboratory tests. The results showed that the water demand reached 92,150 liters/day (1.1 liters per second), while the source capacity was 5.03 liters per second, resulting in a surplus of 1,839%. Although the water supply was quantitatively adequate, quality tests revealed turbidity and bacteriological contamination at some distribution points, attributed to the corrosion of galvanized pipes and the absence of secondary filtration. This confirms that sufficient quantity does not guarantee the quality and efficiency of distribution. Therefore, network modernization, the implementation of layered filtration, and periodic monitoring are necessary. This research contributes to the development of a sustainable campus water management model in Indonesia.

Enhancing Cybersecurity Threat Detection Using Machine Learning: A Comprehensive Review

2025-10-15T13:44:48+00:00

Cybersecurity forms the backbone of digital infrastructure that protects overstretched payment systems, governmental operations, and business continuity today. With machine learning (ML) techniques, it can help analyze a large amount of data and improve cyber-security. It’s tough to quantify how effective the ML-based cybersecurity system is, especially when we theorize it. This review paper talks about the significant role of ML in security, threat detection and security measures. Using machine learning algorithms helps in cybersecurity as they make the system automatic and fast. We can implement a threat detection security model using widely used ML algorithms. For classification purposes, we have Support Vector Machines (SVM), Decision Trees (DT), Random forests (RF), and Adaptive and Extreme gradient boosting (XGBoost). This review paper proposes ML algorithms for the implementation of cybersecurity with some practical application demonstrations. Machine learning algorithms can provide valuable analytics to help bolster security and reduce threats. We assess the accuracy of threat detection in network security by utilizing a set of formulas based on confusion, recall, F1-score, time complexity, accuracy and precision. This review synthesizes algorithmic performance across benchmark datasets (CICIDS2017 NSL-KDD UNSW-NB15) to identify significant gaps in previous ML-based cybersecurity frameworks. The results demonstrate the superior precision (90. 8 percent) and scalability of XGBoost.

Ergonomic Study in Developing The Organizational Culture to Improve SME's Performance

2025-11-17T13:14:02+00:00

In industrial companies, both large and small-scale, performance is the main indicator of organizational success. Optimal performance can be achieved if the organization implements a healthy and effective organizational culture. On the contrary, an improper organizational culture can lead to conflicts between employees and stakeholders. Conflicts often arise that can cause work discomfort. The impact is to reduce work morale, negatively impacting performance and leading to a decline. Therefore, organizations need to build a culture that considers the human factor. One approach that can be applied is the concept of ergonomics, which integrates physical, cognitive, and organizational aspects to create a comfortable work environment so that employees can work healthily and effectively. This study aims to analyze the role of ergonomics on organizational culture and its implications in improving worker performance in the SME environment. This study uses a quantitative approach, supported by field observations and structured interviews with workers and managers of SMEs. Data analysis was carried out using the Structural Equation Modeling (SEM) method. The results of the study show that ergonomic aspects that have a significant effect on the formation of organizational culture with a loading factor value greater than 0.7 include physical ergonomics through workload variables (with work duration indicators), work environment variables (with temperature, humidity, lighting, and noise indicators), and equipment and equipment variables (with indicators of tool and machine design conditions, cognitive ergonomics (with indicators of perception, logical reasoning, and working relationships); and organizational ergonomics through work system design variables (with communication indicators and task variations) and work organization variables (with organizational structure indicators). These findings confirm that improving ergonomics not only creates a healthier and more efficient work environment but also strengthens organizational cultural values, which ultimately positively impacts the overall performance of SMEs

Design of A Digitalization System for Machine Scheduling and Allocation in Flexible Job Shop Heavy Equipment Manufacturing Industry

2024-08-12T20:36:47+00:00

This study aims to develop a digitalized scheduling system based on the Flexible Job Shop (FJS) model to optimize production efficiency in the heavy equipment manufacturing industry. The heavy equipment manufacturing industry faces significant challenges in achieving production efficiency due to its high-mix, low-volume (HMLV) nature and the complexity of production processes. The research follows a structured approach, beginning with Focus Group Discussions (FGDs) to gather stakeholder requirements. These requirements are translated into a House of Quality (HoQ) matrix to prioritize features for the dashboard. A literature review identifies optimal scheduling methods, with a focus on FJS and heuristic scheduling rules. The dashboard is developed using JavaScript, PHP, Node.js, and PostgreSQL, and deployed on Amazon Web Services (AWS). The system undergoes black-box testing to ensure functionality and reliability before implementation. The study identifies the Earliest Due Date (EDD) method as the most effective scheduling approach, with an average delay of 3.2 days, utilization of 29%, and completion time of 14.33 days. The implementation of the digitalized scheduling system increased on-time production from 70.56% to 92.8% and improved production achievement from 92.78% to 97.4%. The dashboard application successfully integrates real-time data, adaptive scheduling, and operational features, such as a start-stop system and machine load recommendations. The findings highlight the importance of digital transformation in manufacturing, particularly in optimizing resource allocation, reducing delays, and improving production efficiency. This research contributes to the field of digitalized scheduling and real-time production management by providing a practical, data-driven solution tailored to the HMLV characteristics of heavy equipment manufacturing.

The Paradox of Smart Tourism: Does Technology Empower or Discourage Experience Sharing

2025-07-28T02:34:12+00:00

This study aimed to develop a model for showing the process of sharing memorable experiences created at tourists destinations or digital tourism supported by smart tourism technology (STT). A survey was conducted on 136 tourists who stayed in home-based accommodations and shared experiences on Instagram, which were later reposted by the destination's official Instagram account. In this study, a quantitative method was used, and the questions were addressed using the SMART PLS 4.1.0.9 tool. Furthermore, data were collected using a purposive sampling method from 136 visitors in West Sumatra who had completed their stay, shared experiences in the form of photos or videos on social media, and had posts reposted by the destination’s official account. The results showed that tourists tended to place greater importance on the emotions evoked by travel experiences compared to those arising from interaction with smart tourism technology (STT) services when evaluating overall happiness. Both theoretical insights and practical applications were further discussed.

Assessment of Cyber Security Awareness Using Developed Game From H5P on Users Aged at Elementary and First Secondary School in Madiun City

2025-04-23T18:14:52+00:00

The increasing cyber threats among children using digital devices without supervision highlight the importance of early cybersecurity awareness. This study aims to develop and evaluate an H5P-based educational game to enhance cybersecurity awareness among elementary and junior high school students in Madiun City. Using a Research and Development (R&D) approach, the game was developed through four stages: needs analysis, design and development, expert validation, and limited field testing. The game incorporates gamification elements such as points, badges, and instant feedback to engage students in learning topics such as password management, data protection, and phishing recognition. The results indicate that 70% of 68 elementary students and 74% of 92 junior high school students showed improved awareness after playing the game. These findings suggest that interactive educational games can effectively enhance cybersecurity awareness among children. The study recommends broader implementation of gamified approaches in educational curricula to foster safe online behavior from an early age.

Developing a Cost-Effective Air Quality Monitoring Solution Using IoT Technology: Addressing Long-Distance Transmission Challenge

2025-03-01T16:20:17+00:00

This research explores the integration of Internet of Things (IoT) technology and LoRa repeaters to enhance air quality monitoring. IoT enables low-cost, real-time sensors for continuous air quality assessment, while repeaters address the limitations of traditional wireless communication over long distances. Our study demonstrates the effectiveness of a LoRa repeater system, with signal strengths between monitoring stations and repeaters ranging from -84 dBm to -92 dBm, achieving a practical operational range of 850 meters. The highest Packet Delivery Ratio (PDR) recorded was 65% using a Spreading Factor (SF) of 10, while SF 7 resulted in a PDR of 25%. Environmental factors and antenna gain were identified as critical for optimizing transmission power and communication reliability. This research underscores the potential of advanced IoT applications in extending internet connectivity and improving air quality management across various sectors, paving the way for smarter urban environments and public health initiatives.

Implementation of Software For Efficient Inventory Management at A National Peruvian University

2025-03-09T14:07:42+00:00

This study presents the design and implementation of a multiplatform inventory management system developed for a public university in Peru, aiming to improve process efficiency and user satisfaction. Following an agile development methodology (SCRUM), the system was designed using modular architecture and responsive interfaces to ensure compatibility across devices and browsers. The usability evaluation was carried out using the CSUQ questionnaire, and the results were transformed to the SUS scale to assess the overall experience. A descriptive quantitative methodology was used, supported by surveys and technical compatibility testing. The findings reveal high user satisfaction, a SUS score of 93.75 ("Best imaginable"), and strong performance across all functionalities, particularly in navigation and inventory tracking. These results confirm the effectiveness of agile development in higher education contexts and highlight the importance of user-centered design in administrative systems.

Multipurpose Incubator Based on Microcontroller System With Distance Monitoring Feature Case Study For Crackers Drying

2025-09-20T15:39:27+00:00

Most available commercial incubators are considered not appropriate for certain food processing such as for crackers drying and also considerably expensive. Besides, they do not have distance monitoring and control features. The objective of this article is to present the development process of an incubator prototype which has distance monitoring and control feature built based on the components commercially available in the market involving traditional stove-based oven, electric heater, DHT22 temperature sensor, ESP8266 microcontroller, buzzer, and LCD OLED display. The incubator was equipped with the Blynk platform of a smartphone for distance control and monitoring. The functional test results showed that the developed incubator works well as expected. The calibration testing results suggested that temperature measurement by using the developed system has inaccuracy of 0.34% when compared to measurement by using a commercial thermometer. To validate its function in actual condition, the incubator was used for drying rice crackers. The rice crackers were dried at the temperature of 70 with a duration of 5 hours. It can be observed that the drying result by using the developed incubator was comparable to that of drying by direct sunlight on a sunny day with a duration of 16 hours.

Using Fuzzy Cognitive Maps For Modeling Environmental Aspect of Sustainable Development in Construction Projects

2025-03-06T21:44:38+00:00

The pillars of sustainable development are representing the interface between environmental, economic, and social sustainability. Sustainable development is a method of planning and managing construction projects to reduce the effect of the construction process on the environment so that there is a balance between environmental capabilities and the human needs of present and future generations. Usually, Environmental sustainability is most important and effective in construction projects. The environment suffers from significant negative impacts as a result of the implementation of construction projects; therefore, this study aims to identify the effecting factors on environmentally sustainable development. The methodology of this study used fuzzy cognitive maps (FCMs) because of adopted simulation approach, after selecting the factors that have RII more than 65% and determine causal relationship between factors by applying fuzzy logic using MATLAB program. Then the effecting factors were analyzed and ranked by static and dynamic analysis. The results showed the static analysis of effecting factors on ESD in first quarter are characterized by influential and affected by other factors of (ESD), were include (C_2.4, C_4.6, C_1.6, C_2.1, C_3.3, C_3.7, C_3.6, C_6.2), When comparing between dynamic analysis and RII of the factors, it has been noticed a difference in the importance. This is an essential finding in the understanding that dynamic analysis considers the interactions between factors, while the RII takes the reasons independently and neglects interactions between them. The study has provided recommendations for the application of (FCM) model that was proposed depend on these factors in building projects to improve the environment and reduce its negative effects.

Generating Accurate Topographic Map by Integrating Drone Imagery and GNSS Data

2025-09-20T15:34:35+00:00

When operating in big or hard-to-reach areas, traditional topographic survey methods can be costly, difficult to organize, and time-consuming. Some of these technologies use total stations and GPS on the ground and aerial photogrammetry done by planes or helicopters. We need a better and cheaper approach to collect geographic data fast. This article discusses employing unmanned aerial vehicles (UAVs) for topographic surveying, mapping, and updating data as one option. A DJI Mavic 2 Pro quadcopter drone with a 20-megapixel digital camera took photographs of the Wasit University campus from 125 meters above the ground. The pictures indicated a space of around 0.43 km², with 80% of the front and 70% of the sides overlapping. The research area was turned into an orthomosaic by Agisoft PhotoScan Professional. This was then loaded into ArcMap so that features may be taken out. By comparing the coordinates of fourteen Ground Control Points (GCPs) that we got using the Real Time Kinematic Global Navigation Satellite System (RTK-GNSS) mechanism, we were able to get a reference positional precision of 0.050 m RMSE. The results of this study demonstrate that geospatial data obtained from UAVs, when augmented by GCPs, can produce and update comprehensive maps with accuracy comparable to RTK GNSS and Total Station methodologies. Many individuals use these methods for surveys of land, buildings, and engineering.

Blockchain Framework for Secure IoT Operations in Military Applications: Integrating LoRaWAN and Helium Network

2025-08-16T02:14:09+00:00

The traditional IoT is typically based on centralized systems that are susceptible to multiple cyberattacks and a single point of failure. Modern industries regularly embrace block chain technology due to its decentralization and security. This study suggests a block chain-based system that guarantees reliable and secure operations. They suggest a secure compact block chain for handling access to precious information through instruments and controllers. Based on realistic military applications, the current investigation makes evidence for the benefits of merging LoRaWAN and Helium Network technology, and also demonstrates how deliberate research and analysis can bridge the block chain gap for military cyber defense. To improve the proposed system's computing efficiency, the block chain network has devised a rapid and power-saving decision technique for proof of authentication. The suggested framework for smart industrial environments has survived extensive testing and study to be sustainable. Use the suggested configuration to convert a standard processing system into an intelligent and secure industrial platform. This article aims towards assessing the practicality of Proof of Authority in the block chains network as a consensus algorithm. There are numerous techniques available for creating a consensus among the nodes.

Hand Gesture Recognition Using Optimized Hyperparameters of CNN for Real-Time Control of a Multi-Servo Hand

2025-10-15T13:22:01+00:00

Gesture recognition has emerged as a promising approach to enhance human-machine interaction, especially in robotics and assistive devices. This study presents a real-time gesture-controlled robotic system that combines deep learning and machine learning techniques to recognize hand gestures and map them to servo motor movements. A convolutional neural network (CNN) was used to classify six predefined hand gestures: a closed fist and five individual finger extensions. To enhance classification accuracy and generalization, CNNs are tuned using hyperparameter tuning techniques, such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Brown Bear Optimization (BBO). These methods efficiently explore the hyperparameter space—such as learning rate, filter size, and batch size—reducing manual trial and error in control. Among these proposed models tested, the BBO-CNN has been achieved the highest performance with a classification accuracy of 99.98%, outperforming both PSO-CNN (99.89%) and GWO-CNN (99.44%). The model CNN without optimization achieved an accuracy of 97.50%. The combination of advanced deep learning models and embedded control demonstrates the feasibility and effectiveness of gesture-based robotics applications.

Investigating The Influence of Aquatic Algorithms on Underwater Wireless Routing Beyond Surface Constraints

2025-07-07T22:16:03+00:00

Underwater Wireless Sensor Networks (UWSNs) play a vital role in ocean monitoring, seismic activity tracking, and environmental observation, yet their performance is hindered by high propagation delay, limited bandwidth, and dynamic topological variations. This study conducts a comprehensive comparative analysis of aquatic algorithm on three prominent UWSN routing protocols—Vector-Based Forwarding (VBF), Depth-Based Routing (DBR), and Flood Routing—to evaluate their Quality of Service (QoS) performance in terms of delay, throughput, and packet loss under varying conditions of node density (20–100 nodes), communication distance (50–200 m), and deployment depth (0–200 m). Simulation data were generated using the Aqua-Sim framework in NS-3, ensuring a controlled and reproducible evaluation environment. The results indicate that VBF consistently achieves the lowest delay (0.0676 s at 20 nodes; 0.0769 s at 100 nodes) and the highest throughput (1772.80 bps at a depth of 51–100 m), making it the most efficient protocol for real-time and latency-sensitive applications. However, VBF experiences moderate packet loss (up to 57.34% in dense networks) due to its constrained forwarding region. DBR exhibits higher delay (0.1420 s at 151–200 m) and the greatest packet loss (85% at a 50 m distance), reflecting the limitations of depth-only forwarding in dynamic environments. In contrast, Flood Routing achieves the lowest packet loss (10.24% at 50 m) but suffers from increased delay (0.1063 s at 200 m) and inefficiency due to redundant transmissions. Overall, this study provides a unified performance benchmark for UWSN routing protocols, highlighting the fundamental trade-offs between efficiency, reliability, and scalability. These insights offer practical guidance for protocol selection and serve as a foundation for future adaptive and AI-driven routing strategies in underwater sensor networks.

Predicting Bentonite Plastic Concrete Performance Using Machine Learning

2025-10-07T02:41:27+00:00

This study develops an interpretable machine learning framework to predict the mechanical properties of bentonite plastic concrete (BPC), an essential material for low-permeability geotechnical structures. Traditional testing of BPC is time- and cost-intensive, while empirical equations often fail to capture the nonlinear effects of bentonite and curing conditions. To address these limitations, four ensemble learning models were optimized using the Forensic-Based Investigation Optimization (FBIO) algorithm, a parameter-free metaheuristic inspired by investigative search processes. The models were trained on three curated experimental datasets to predict slump, tensile strength, and elastic modules. Among all, XGB–FBIO achieved the highest accuracy for slump (R² = 0.98) and tensile strength (R² = 0.99), while GBRT–FBIO performed best for elastic modulus (R² = 0.97). SHapley Additive exPlanations (SHAP) analysis revealed curing time, cement, and water content as the most influential variables. The results demonstrate that the proposed framework can replace repetitive laboratory trials with data-driven insights, providing engineers with a reliable, explainable, and resource-efficient tool for optimizing BPC mix designs in environmental and geotechnical applications.

Blockchain-Enhanced Framework for Ensuring Data Consistency, Transparency and Privacy in Cloud Computing

2025-10-15T13:48:18+00:00

Signal Characteristics of Land Mobile Satellites in Urban and Suburban Equatorial Regions: A Study of S/N Ratios in Fixed and Mobile Conditions

2025-10-14T07:44:44+00:00

The increasing demand for mobile communication services in Indonesia underscores the necessity for reliable satellite mapping systems, particularly in equatorial regions where empirical data is scarce. This study aims to fill this research gap by evaluating the signal strength and quality for land mobile satellites in Pare-Pare City and Sidrap Regency. Utilizing a cost-effective laptop-based system alongside a handheld GPS receiver, we conducted measurements under both fixed and mobile conditions at various locations. Our analysis, performed using Matlab R2023b, identified notable variations in Signal-to-Noise Ratio (SNR), primarily ranging from 20 to 49 dBHz, with peak values of around 50 dBHz recorded in suburban areas. These findings indicate that local obstructions significantly affect GPS accuracy. The implications of this research are twofold: theoretically, it enriches the existing literature on GPS performance in equatorial environments, and practically, it offers actionable insights for optimizing satellite deployments to enhance communication reliability. By providing essential empirical evidence, this study represents a valuable contribution to the understanding of satellite communication dynamics in Indonesia, paving the way for more effective navigation and communication solutions in challenging equatorial settings.

A Comprehensive Review of Deep Learning Techniques for Intrusion Detection in the Internet of Medical Things

2025-03-03T11:29:37+00:00

The work revisits the security issues of Internet of Medical Things (IoMT) platforms and provides a list of deep learning models used for intrusion detection. The study fills the salient gap in early detection of actual IoMT system intrusions for enhanced medical device and data security. A wide-ranging and systematic investigation of deep learning models, such as CNNs, LSTMs, and hybrid ones (GNNs and BiLSTMs) recently introduced was carried out. These were then analyzed against well-known benchmark datasets, such as ToN-IoT and IoT-Healthcare Security and WUSTL-EHMS-2020, to consider the quality of their detection work on cybersecurity threats for IoMT systems. The results indicated high accuracy in cyber threat detection, reaching even 100% accuracy. But the challenges are still how to decrease false positives and improve the real-time performance of the model on robustness and generalization when making real-world applications. The research is literature-based and aimed to provide some further updates on a secure IoMT framework by identifying recent studies in the security of the IoMT ecosystem and shedding light on future work using hybrid methods, blockchain technology, or federated learning approaches that can contribute to the detection of IDSs. And all can help pave the way for a more secure, privacy-protecting IoMT that safeguards extremely sensitive medical data. The research also enhances the model: utilizing 15+ deep-learning models to propose an IoMT-resistant architecture. This can promote participation in the theoretical research and practical security protocols in the IoMT context, thus drawing attention and comprehension from researchers and practitioners to enhance security protocols.

Decision-Making in the Digitalization of Library Reference Services through Social Media: A Case Study of the National Library of Indonesia

2025-09-02T12:14:45+00:00

The digitalization of library reference services through social media remains under-researched, particularly regarding how socio-technical factors and institutional policy processes intersect in national libraries. This study addresses this gap by examining the case of the National Library of Indonesia (Perpusnas), where virtual reference services (VRS) have evolved amid infrastructural reforms and the COVID-19 pandemic. Adopting a qualitative case study within a constructivist paradigm, the research combines semi-structured interviews with librarians and managers (n=5) and a Social Network Analysis (SNA) of @perpusnas1 interactions on X (formerly Twitter) during 2023. The analytical framework integrates the Policy Cycle with the Social Construction of Technology (SCOT), enabling a multi-layered exploration of agenda-setting, policy formulation, interpretive flexibility, and network structures. Findings show that VRS development was shaped by problem recognition (inefficiencies in email services), adaptive policy formulation (iterative SOP revisions and platform selection), and improvisational implementation constrained by staff capacity and infrastructure. SCOT analysis revealed competing interpretations of social media—promotion, reference tool, or user shortcut—eventually stabilised through closure. SNA results confirmed a centralised hub-and-spoke model dominated by @perpusnas1, enhancing responsiveness but limiting distributed participation. This study contributes theoretically by linking SCOT, policy process models, and SNA in library science; practically by highlighting training, evaluation, and integration needs for managers; and for policy by illustrating adaptive pathways to digitalisation in developing-country contexts.