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.
Articles
35 Documents
<![CDATA[The design of an information system for managing teaching staff salaries ]]>
<![CDATA[Maastri, Adi]]>;
<![CDATA[S, Ahmad]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i1.45
<![CDATA[Effective management of teaching staff salaries is crucial for maintaining operational efficiency and ensuring employee satisfaction within educational institutions. Traditional manual systems often suffer from inefficiencies, data inconsistencies, and security risks, highlighting the need for a dedicated solution tailored to salary management. This study addresses this gap by designing a comprehensive information system that automates and streamlines salary-related processes. Utilizing the waterfall development methodology, the system was structured through sequential phases of analysis, design, coding, testing, and implementation. The proposed system incorporates key features such as user authentication, hierarchical access control, and automated periodic salary updates. It accommodates multiple user roles, including teachers, financial operators, and administrators, ensuring secure and role-specific access to salary data. Use case and activity diagrams were developed to illustrate the system’s functionality, including login validation and the submission of salary update forms. By bridging the gap between theoretical frameworks and practical implementation, this study contributes a robust, user-friendly solution that enhances transparency and reduces administrative workload.]]>
<![CDATA[Optimizing the Traveling Salesman Problem Using Machine Learning and Predictive Algorithms ]]>
<![CDATA[Ahmad, Asiyah]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i1.46
<![CDATA[The Traveling Salesman Problem (TSP) is a foundational challenge in optimization, with applications in logistics, routing, and scheduling. Traditional algorithms such as dynamic programming and brute-force search guarantee optimal solutions but become computationally expensive as the number of cities grow, hindering scalability. Consequently, research has shifted towards machine learning (ML) and predictive algorithms, which show promise in approximating optimal solutions more efficiently. This study aims to optimize TSP using ML models, specifically focusing on enhancing scalability and minimizing computational overhead. The approach incorporates techniques like reinforcement learning (RL) and graph neural networks (GNNs), leveraging their ability to learn and generalize from smaller problem instances. The primary contribution of this work is an ML-driven framework for TSP, which demonstrates improved efficiency and adaptability compared to traditional algorithms. Evaluation metrics, including total path length, convergence time, and optimality gap, validate the model's effectiveness, achieving optimal paths with reduced execution time. This research offers a practical ML-based solution for TSP that balances accuracy with computational speed, providing a feasible alternative for large-scale and dynamic real-world applications.]]>
<![CDATA[Regulating AI in Legal Practice: Challenges and Opportunities ]]>
<![CDATA[Zahra, Yatama]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i1.47
<![CDATA[The integration of Artificial Intelligence (AI) in legal practice is transforming the legal profession by enhancing efficiency and accessibility while presenting significant ethical and regulatory challenges. AI applications such as predictive analytics, automated document drafting, and AI-driven legal research hold immense potential to reduce administrative burdens, streamline case management, and improve access to justice. However, issues such as algorithmic bias, lack of transparency, and data privacy concerns raise critical questions about fairness and accountability in AI-driven decision-making. This study aims to analyze the dual landscape of challenges and opportunities associated with AI adoption in legal practice, emphasizing the need for balanced regulatory frameworks. A systematic review of existing literature was conducted to identify the obstacles and benefits of AI integration. Key challenges include algorithmic biases, inadequate legal frameworks, and the digital divide among legal professionals, while opportunities range from cost reduction to improved dispute resolution processes. The findings contribute to ongoing discussions on AI governance by proposing actionable strategies such as fairness audits, explainable AI practices, and targeted training programs for legal professionals.]]>
<![CDATA[Early Detection and Mapping of Dengue Fever Outbreaks in Urban Areas ]]>
<![CDATA[Maulana, Muhammad Mico]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i1.48
<![CDATA[Dengue Hemorrhagic Fever (DHF) poses a significant public health challenge in tropical regions like Indonesia, where environmental conditions favor the proliferation of Aedes aegypti mosquitoes. The Karya Maju Health Center, serving eight villages in South Sumatra, struggles with monitoring dengue cases due to manual data recording and limited tools for analyzing outbreak patterns. This study aims to address these challenges by developing a system for early detection and mapping of dengue outbreaks. The methodology employs Unified Modeling Language (UML) diagrams, including use case, activity, and class diagrams, to design an intuitive, user-centered system. Use case diagrams outline interactions between healthcare staff and the system, while activity diagrams map the process flow from data collection to visualization. The interface design prioritizes usability, providing stakeholders with clear and accessible tools for monitoring outbreaks. The system was evaluated through pilot testing, which confirmed its ability to meet all predefined criteria. Users found the interface intuitive, with well-structured menus and visualizations facilitating efficient interaction and data analysis. This study contributes to public health by offering a scalable and effective tool for dengue monitoring, enabling healthcare providers to proactively manage outbreaks and allocate resources more effectively.]]>
<![CDATA[Smart Mobile Application for Heavy Equipment Rental ]]>
<![CDATA[Tobias, Neec Chander]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i1.49
<![CDATA[This study explores the development of an Android-based heavy equipment rental application tailored for PT. Gajah Unggul Internasional. It highlights the pivotal role of information systems in the heavy equipment rental sector, focusing on the specific needs of PT. Gajah Unggul Internasional, a provider of various construction and industrial equipment. To address the demand for a streamlined system to manage borrowing, returning, and restocking equipment, the study proposes an Android application built with the Ionic Framework, a robust open-source SDK. The methodology employs a comprehensive five-stage research design: data collection, inception, elaboration, construction, and transition. The results and discussion section offers an in-depth evaluation of the application, presenting critical components such as use case diagrams, activity diagrams, sequence diagrams, class diagrams, and the system interface. Functionality and efficiency are validated through black box testing, confirming the system's reliability in processes such as login, registration, equipment data access, and rental history tracking. In conclusion, the study demonstrates the successful application of the Ionic Framework to enhance heavy equipment rental operations at PT. Gajah Unggul Internasional. The user-friendly application caters to various stakeholders, emphasizing practicality and efficiency. The article provides valuable insights into leveraging technology to optimize business processes within the heavy equipment rental industry.]]>
<![CDATA[Workload Analysis System Optimization through the Integration of an Interactive Dashboard ]]>
<![CDATA[Karisa, Tiara]]>;
<![CDATA[S, Ahmad]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i2.62
<![CDATA[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.]]>
<![CDATA[Machine Learning-Based Route Optimization for Smart Urban Transportation Systems ]]>
<![CDATA[Anson, Adriel Moses]]>;
<![CDATA[Amirah]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i2.65
<![CDATA[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.]]>
<![CDATA[Ensuring Data Privacy in the Age of Artificial Intelligence ]]>
<![CDATA[Zahra, Yatama]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i2.66
<![CDATA[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.]]>
<![CDATA[Web-Based Monitoring and Management System for Livestock Operations ]]>
<![CDATA[Firdaus, Ananda Ade]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i2.69
<![CDATA[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.]]>
<![CDATA[Shaping the Future of Agriculture with Intelligent Systems ]]>
<![CDATA[Anwar, Ican]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
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DOI: 10.70356/josapen.v3i2.72
<![CDATA[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.]]>
