Journal of Applied Engineering and Technological Science (JAETS)
Journal of Applied Engineering and Technological Science (JAETS) is published by Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI), Pekanbaru, Indonesia. It is academic, online, open access, peer reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. Journal of Applied Engineering and Technological Science (JAETS) is published annually 2 times every June and Desember. E-ISSN : 2715-6079, P-ISSN : 2715-6087. https://www.scopus.com/sourceid/21101138522 Journal title Initials Frequency DOI Print ISSN Online ISSN Editor-in-chief Publisher Language Indexing Citation Analysis : Journal of Applied Engineering and Technological Science : (JAETS) : JAETS : 2 issues per year (December and June) : by with Prefix 10.37385(https://doi.org/10.37385/jaets) : 2715-6087 : 2715-6079 : Assoc. Prof. Dr. Muhammad Luthfi Hamzah, B.IT., M.Kom : Yayasan Riset dan Publikasi Intelektual (YRPI) : English (preffered) : : Scopus | <a href="https://doaj.org/toc/2715-6079?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22terms%22%3A%7B%22index.issn.exact%22%3A%5B%222715-6087%22%2C%222715-6079%22%5D%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22match_all%22%3A%7B%7D%7D%7D%7D%2C%22size%22%3A100%2C%22sort%22%3A%5B%7B%22created_date%22%3A%7B%22order%22%3A%22desc%22%7D%7D%5D%2C%22_source%22%3A%7B%7D%7D" target="_blank" rel="noopener">DOAJ | Garuda | Moraref | IndexCopernicus | WorldCat | DRJI | SCILIT | Dimensions| SINTA 1 | : Scopus | Web of Science | Google Scholar | Dimensions
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Enhancing Intrusion Detection System Performance Using Reinforcement Learning : A Fairness-Aware Comparative Study on NSL-KDD and CICIDS2017
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.
Copyright (c) 2025 Yudhi Arta, Suzani Mohamad Samuri, Nesi Syafitri
Ergonomic Study in Developing The Organizational Culture to Improve SME's Performance
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
Copyright (c) 2025 Mohammad Fajar Nurwildani, Hari Purnomo, Hartomo Soewardi, Elisa Kusrini
The Impact of Virtual Laboratories on Student Motivation and Academic Performance: An Integrated Fuzzy-Sem and Machine Learning Study
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.
Leveraging Intranet Quality for University Financial Sustainability: The Mediating Role of Enterprise Risk Management
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.
Copyright (c) 2025 Ummu Ajirah Abdul Rauf, Ragnar Löfstedt, Paul Bracken, Astri Ayu Purwati
Blockchain-Enhanced Framework for Ensuring Data Consistency, Transparency and Privacy in Cloud Computing
Enhancing Cybersecurity Threat Detection Using Machine Learning: A Comprehensive Review
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.
Copyright (c) 2025 Somasundari P, Kavitha V
Hand Gesture Recognition Using Optimized Hyperparameters of CNN for Real-Time Control of a Multi-Servo Hand
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.
Copyright (c) 2025 Noor M. Naser, Kian R. Qasim, Zinah J. Jabbar
Signal Characteristics of Land Mobile Satellites in Urban and Suburban Equatorial Regions: A Study of S/N Ratios in Fixed and Mobile Conditions
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.
Copyright (c) 2025 Zulfajri Basri Hasanuddin, Kiyotaka Fujisaki, Limbran Sampebatu
Modern Language Techniques from Emerging Trends, Evaluation, Obstacles, to Prospects: Review
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.
Copyright (c) 2025 Samira Abdul-kader Hussain , Dina Riadh Alshibani, Noor A. Yousif
Predicting Bentonite Plastic Concrete Performance Using Machine Learning
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.
Copyright (c) 2025 Sameh Fuqaha, Ahmad zaki
Evaluation of Water Needs and Availability in The Design of The Water Needs Masterplan on The IPDN West Sumatera Campus
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.
Copyright (c) 2025 Fauzi Fauzi, Bambang Istijono, Benny Hidayat
Swarm Intelligence Optimisation Vs Deep Learning: Energy-Aware Strategy for Disaster Communication Networks
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.
Copyright (c) 2025 Norhisham Mansor, Wahidah Md Shah, Najwan Khambari
Multipurpose Incubator Based on Microcontroller System With Distance Monitoring Feature Case Study For Crackers Drying
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.
Copyright (c) 2025 Heru Supriyono, Anik Juliana, Agus Supardi, Pramudya Kurnia, Muhammad Satria Ananta, Mohammad Dwiki Aji Nugroho, Helmi Hidayatullah
From Exclusion to Empowerment: Redesigning E-Learning to Mitigate Risks for Disabled Students
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.
Copyright (c) 2025 Ummu Ajirah Abdul Rauf, Mazzlida Mat Deli, Nanang Husin
Acceptance of Culture Mobile Tourism Among Tourist: A Model Development Study
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.
Copyright (c) 2025 Syaifullah Syaifullah, Shamsul Arrieya Ariffin, Norhisham Mohamad Nordin
Generating Accurate Topographic Map by Integrating Drone Imagery and GNSS Data
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.
Copyright (c) 2025 Aqeel A. Abdulhassan, Noor A. Alwan, Marwaa K. Azeez, Doaa T. Yaseen
Artificial Intelligence and Optimization of Reverse Logistics: An Analysis in the Aquatic Industry of The Mekong Delta
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.
Copyright (c) 2025 Chuyen Tran Trung, Phan Tran Xuan Trinh, Tran Thanh Huy, Nguyen Tri Khiem, Nguyen Van Tac
Decision-Making in the Digitalization of Library Reference Services through Social Media: A Case Study of the National Library of Indonesia
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.
Copyright (c) 2025 Destiya Puji Prabowo, Eni Maryani, Atwar Bajari, Wina Erwina
Enhanced CSMA/CA Protocol-Based Optimal Robust Dynamic Query-Driven Clustering for Improved QoS in Heterogeneous WSNs
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
Copyright (c) 2025 Ahmed Mahdi Jubair, Akeel Abdulraheem Thulnoon, Foad Salem Mubarek
Magie Broom: Revolutionizing Cleaning with User-Centered Ergonomic Design
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.
Copyright (c) 2025 Roikhanatun Nafi'ah, Adhe Lingga Dewi, Kamila Nur Rosya
Blockchain Framework for Secure IoT Operations in Military Applications: Integrating LoRaWAN and Helium Network
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.
Copyright (c) 2025 Jebarani Evangeline S, Krishna Prakash Arunachalam, Seethalakshmi V, Senthil Kumar A, Reeda Lenus C, Saranya R
The Paradox of Smart Tourism: Does Technology Empower or Discourage Experience Sharing
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.
Copyright (c) 2025 Mareta Kemala Sari, Ratni Prima Lita, Sari Lenggogeni, Ma'ruf Ma'ruf
Investigating The Influence of Aquatic Algorithms on Underwater Wireless Routing Beyond Surface Constraints
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.
Copyright (c) 2025 Hamdi Aziz, Istikmal Istikmal, Rendy Munadi
Impact of SWCC Measurement Methods on the Numerical Investigations of Unsaturated Soils
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.
Copyright (c) 2025 Abderrahim Mihoubi, Samir Benmoussa, Abdelkader Houam, Mohamed Salah Laouar
Clustering Analysis of Patchouli Plantations for Sustainable Patchouli Oil Supply Chain Using K-Means Algorithm
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.
Copyright (c) 2025 Hazful Maizi, Prima Denny Sentia, Geta Ambartiasari, Friesca Erwan
Optimization of Convolutional Neural Network for Classification of Hydroponic Vegetable Cultivation Using Machine Learning
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%.
Copyright (c) 2025 Arif Ridho Lubis, Santi Prayudani, Purwa Hasan Putra, Yuyun Yusnida Lase
Fine-Tuning Whisper Model for Mandar Speech Recognition: Approach and Performance Evaluation
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.
Copyright (c) 2025 Jafar Jafar, Mar Athul Wazithah Tb, Firman Aziz, Rosary Iriany, Norma Nasir
Optimizing MSME Product Authenticity Verification Using Serverless Event-Driven Microservices on Hybrid Blockchain Architecture
The rapid proliferation of counterfeit products poses critical risks to Micro, Small, and Medium Enterprises (MSMEs), eroding consumer trust and hindering market competitiveness. This study proposes a Serverless Event-Driven Microservices Hybrid Blockchain Architecture to enable real-time, cost-efficient MSME product authentication. The architecture integrates Solana blockchain, leveraging its high throughput and low transaction fees, with AWS Lambda–based serverless microservices and Redis in-memory caching to enhance scalability and responsiveness. The system was empirically evaluated across transaction loads ranging from 1,000 to 100,000, measuring throughput, latency, scalability, and cost efficiency. Experimental results demonstrate that the proposed framework achieved up to 970 transactions per second (TPS) with average latency between 12 and 32 ms, while reducing operational costs by approximately 80 % compared with traditional monolithic systems. Statistical validation confirmed the reliability of performance gains (p < 0.001), and regression analysis (R² = 0.97) indicated near-linear scalability under increasing workloads. These findings substantiate the architectural advantages of combining serverless elasticity, event-driven orchestration, and hybrid blockchain consensus, resulting in a self-scaling, fault-tolerant verification platform suitable for MSME environments. The framework establishes an empirically grounded pathway for practical blockchain adoption in resource-constrained ecosystems and provides a foundation for future extensions involving edge computing, predictive caching, and cross-chain interoperability.
Copyright (c) 2025 Adnan Zulkarnain, Roby Firnando Yusuf
Social Media Marketing and Business Performance: Analyzing The TOE Framework and Relational Capability
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.
Copyright (c) 2025 Rihan Hafizni, Ratni Prima Lita, Yulia Hendri Yeni, Syafrizal Syafrizal
Exploring the Effectiveness of VR-Based Educational Games in Improving Student Engagement and Comprehension in Computer Architecture
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.
Copyright (c) 2025 Dedy Irfan, Andhika Herayono, Fitri Ayu, Rinaldi Rinaldi, Syafril Syafar, Edidas Edidas, Hanesman Hanesman