Journal of Computer Science Application and Engineering
Introduction Journal of Computer Science Application and Engineering (JOSAPEN) is a peer-reviewed open-access journal organized by the Lentera Ilmu Publisher, Indonesia. The journal invites academicians (student and lecturer), researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Computer Science and Electrical Engineering, which covers some major areas such as 1) Artificial Intelligence, 2) Image and Data Processing, 3) Information Technology and System, 4) Internet of Things (IoT), 5) Microprocessor and Embedded System, 6) Electrical and Electronics Engineering, 7) Control systems and Robotics, 8) Computer Networks, 9) Information Security, and other related areas. This journal also invites reviewers and editors to be involved in the review process. This journal will become a discussion forum between the author and the reviewer and mediated by the editor. Focus And Scope The Journal of Computer Science Application and Engineering (JOSAPEN) publishes original papers in the fields of computer science, informatics engineering, and electrical, which cover, but are not limited to, the following scope: Computer Science, Computer Engineering and Informatics: Computer Architecture, Parallel and Distributed Computer, Pervasive Computing, Computer Network, Embedded System, Human—Computer Interaction, Virtual/Augmented Reality, Computer Security, Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods), Programming (Programming Methodology and Paradigm), Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data), Network Traffic Modeling, Performance Modeling, Dependable Computing, High Performance Computing, Computer Security, Human-Machine Interface, Stochastic Systems, Information Theory, Intelligent Systems, IT Governance, Networking Technology, Optical Communication Technology, Next Generation Media, Robotic Instrumentation, Information Search Engine, Multimedia Security, Computer Vision, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Next Network Generation, Computer Network Security, Natural Language Processing, Business Process, Cognitive Systems. Telecommunication and Information Technology: Modulation and Signal Processing for Telecommunication, Information Theory and Coding, Antenna and Wave Propagation, Wireless and Mobile Communications, Radio Communication, Communication Electronics and Microwave, Radar Imaging, Distributed Platform, Communication Network and Systems, Telematics Services and Security Network; Electronics: Electronic Materials, Microelectronic System, Design and Implementation of Application Specific Integrated Circuits (ASIC), VLSI Design, System-on-a-Chip (SoC) and Electronic Instrumentation Using CAD Tools, digital signal & data Processing, , Biomedical Transducers and instrumentation, Medical Imaging Equipment and Techniques, Biomedical Imaging and Image Processing, Biomechanics and Rehabilitation Engineering, Biomaterials and Drug Delivery Systems; Electrical and Power Engineering: Electrical Engineering Materials, Electric Power Generation, Transmission and Distribution, Power Electronics, Power Quality, Power Economic, FACTS, Renewable Energy, Electric Traction, Electromagnetic Compatibility, High Voltage Insulation Technologies, High Voltage Apparatuses, Lightning Detection and Protection, Power System Analysis, SCADA, Electrical Measurements; Instrumentation and Control Engineering: Optimal, Robust and Adaptive Controls, Non Linear and Stochastic Controls, Modeling and Identification, Robotics, Image Based Control, Hybrid and Switching Control, Process Optimization and Scheduling, Control and Intelligent Systems, Artificial Intelligent and Expert System, Fuzzy Logic and Neural Network, Complex Adaptive Systems.
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Workload Analysis System Optimization through the Integration of an Interactive Dashboard
Karisa, Tiara;
S, Ahmad
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i2.62
In the era of digital transformation and bureaucratic reform, optimizing organizational performance in police institutions demands effective workload management. Workload analysis (Analisis Beban Kerja/ABK) is essential for ensuring fair task distribution that aligns with personnel capacity and competence. The Community Development Unit (Binmas) of the South Sumatra Regional Police plays a pivotal role in building public trust and cooperation. However, the absence of an integrated system to analyze workload distribution has resulted in potential inefficiencies, imbalanced task assignments, and increased work stress. Recent findings highlight a significant link between workload and stress levels among police personnel, emphasizing the urgency of system optimization. This study proposes the integration of an interactive dashboard to enhance the effectiveness of workload analysis in the Binmas Unit. Drawing on successful implementations such as the e-dikbangspes system and SI-ABK Precision application, the research underscores how dashboard technology can streamline data access, improve staffing decisions, and support the development of a more responsive organizational structure. By focusing on the Binmas Unit, this study aims to close the existing technological gap and contribute to improved personnel management and institutional performance through digital innovation in workload monitoring.
Machine Learning-Based Route Optimization for Smart Urban Transportation Systems
Anson, Adriel Moses;
Amirah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i2.65
Urban transportation systems face increasing challenges due to rapid population growth, traffic congestion, and unpredictable road conditions. Traditional routing algorithms like Dijkstra and A* are limited in their ability to respond to real-time events such as accidents, roadwork, or weather disruptions. This study aims to develop a smarter, more adaptive route optimization system using machine learning techniques. The goal is to enhance travel time accuracy, reduce congestion, and improve commuter satisfaction through intelligent, data-driven decision-making. The proposed method integrates supervised learning for travel time prediction and reinforcement learning for real-time route selection, using data from GPS trajectories, traffic flow, weather reports, and user behaviors. A grid-based environment is used for reinforcement learning simulations, while OpenStreetMap data supports city-level route optimization. Results show that the machine learning-enhanced model significantly outperforms traditional algorithms in terms of adaptability, responsiveness, and reliability. In particular, reinforcement learning proved effective in dynamic environments, learning optimal routes over time and adjusting to disruptions. This research contributes to the development of intelligent transportation systems and supports the broader vision of smart cities, where mobility is safer, faster, and more efficient through the power of AI and real-time data integration.
Ensuring Data Privacy in the Age of Artificial Intelligence
Zahra, Yatama
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i2.66
This study explores the intersection of data privacy and artificial intelligence (AI) within the context of Indonesia’s evolving digital landscape. As AI technologies become increasingly embedded in key sectors such as healthcare, finance, education, and public services, the need for robust data protection mechanisms grows more urgent. The 2022 enactment of Indonesia’s Personal Data Protection (PDP) Law marks a significant step toward safeguarding individual privacy rights and regulating the use of personal data in AI systems. However, challenges remain in ensuring compliance with legal principles such as transparency, purpose limitation, and user consent, especially as many AI models operate as opaque "black boxes." Through a comparative analysis of global data privacy regulations—including the GDPR, CCPA, and PIPL—this study highlights international best practices and their relevance to AI governance. A conceptual framework is presented to illustrate the foundational principles necessary for aligning AI development with data privacy standards. The study concludes by emphasizing the importance of a harmonized, ethics-driven regulatory approach that supports responsible AI innovation while protecting individual rights. Stronger collaboration among government, industry, and civil society is essential to achieving a secure, trustworthy, and inclusive digital future for Indonesia.
Web-Based Monitoring and Management System for Livestock Operations
Firdaus, Ananda Ade
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i2.69
Monitoring livestock health and productivity is a critical aspect of agricultural management, yet many organizations still rely on manual or disconnected systems that lead to inefficiencies, delayed reporting, and lack of real-time insight. These challenges hinder timely decision-making, especially in identifying diseases, recording births and deaths, and maintaining accurate data. To address this gap, this study introduces a Web-Based Livestock Monitoring System designed to streamline data entry, enable centralized access, and support role-based interaction for Admins, Livestock Staff, and the Head of Department. The system was developed using a structured web application model, with core features including user authentication, livestock categorization, health monitoring, and monthly report generation. A black-box testing approach was used to evaluate its functionality from the user's perspective without delving into internal code logic. Testing results showed that all features—such as login validation, data input, and report generation—operated successfully and provided the expected outputs. This system offers a practical solution for modernizing livestock monitoring processes, enhancing efficiency, data accuracy, and communication across roles. It contributes to more effective livestock management by ensuring that health data is up-to-date and accessible, allowing stakeholders to make informed decisions and respond proactively to issues in the field.
Shaping the Future of Agriculture with Intelligent Systems
Anwar, Ican
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i2.72
This study explores the implementation of intelligent systems in agriculture as a solution to longstanding challenges such as inefficient resource use, disease management, and low productivity. By integrating technologies like Artificial Intelligence (AI), the Internet of Things (IoT), computer vision, and robotics, intelligent systems enable precision farming that optimizes water usage, enhances crop monitoring, automates labor-intensive tasks, and improves overall decision-making. Real-world applications such as CropX and NetBeat for smart irrigation, Plantix and Nuru for disease detection, and John Deere’s autonomous tractors for automated fieldwork demonstrate the tangible benefits of these innovations. Additionally, tools like Moocall and Ida offer real-time livestock health monitoring, while platforms such as AgriPredict and aWhere provide data-driven decision support to farmers globally. A sample block diagram of a smart irrigation system, supported by a simplified calculation, illustrates the practical operation and measurable benefits of such systems. The study emphasizes the potential of intelligent agriculture not only to boost productivity and sustainability but also to make advanced tools more accessible to small and medium-scale farmers. Future advancements should aim to enhance integration, affordability, and ease of use, ultimately supporting the transition to more resilient and efficient agricultural practices in the face of growing global food demands.