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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The design of an information system for managing teaching staff salaries
Maastri, Adi;
S, Ahmad
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i1.45
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.
Optimizing the Traveling Salesman Problem Using Machine Learning and Predictive Algorithms
Ahmad, Asiyah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i1.46
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.
Regulating AI in Legal Practice: Challenges and Opportunities
Zahra, Yatama
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i1.47
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.
Early Detection and Mapping of Dengue Fever Outbreaks in Urban Areas
Maulana, Muhammad Mico
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i1.48
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.
Smart Mobile Application for Heavy Equipment Rental
Tobias, Neec Chander
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher
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DOI: 10.70356/josapen.v3i1.49
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.