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INDONESIA
Journal of Computer Science Application and Engineering
ISSN : 30312272     EISSN : 30312272     DOI : -
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 5 Documents
Search results for , issue "Vol. 1 No. 2 (2023): JOSAPEN - July" : 5 Documents clear
Web-Based Community Data Collection for the Family Hope Program in Jakabaring Sub District Mutiara Maharani; Dona M
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 2 (2023): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

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Abstract

This paper addresses the need for an information system to manage data for the Family Hope Program (PKH) in Jakabaring Sub District, Palembang City, Indonesia. The PKH aims to provide assistance to Very Poor Households (RTSM), intending to alleviate immediate burdens and break the cycle of poverty across generations. The absence of a dedicated information system has led to frequent data loss, prompting the proposal and development of a structured system to enhance data collection, processing, and management. Employing the Waterfall model, the system development approach was meticulously structured, progressing through sequential stages—analysis, design, coding, testing, and implementation. The system's design components, including Use case diagrams, Activity diagrams, Sequence diagrams, and Class diagrams, were crucial in outlining functionalities and relationships within the proposed system. Notably, the proposed system's interface, exemplified by an admin dashboard, offers a user-friendly layout featuring population graphs and intuitive menus for data management and reporting. Black Box testing results exhibited satisfactory performance across various system functionalities, affirming its potential to efficiently manage data for the PKH. Overall, this proposed information system stands as a promising solution to mitigate data loss challenges and enhance efficiency in supporting Very Poor Households in Jakabaring Sub District.
From Algorithms to Cures: AI's Impact on Drug Discovery Fitrah Karimah; Amirah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 2 (2023): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

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Abstract

This study explores the paradigm-shifting fusion of artificial intelligence (AI) and pharmaceuticals, heralding a new era of innovation in drug development. AI's transformative potential revolutionizes the traditionally arduous drug discovery process by seamlessly assimilating vast data volumes encompassing molecular structures, genetics, and disease pathways. This synergy expedites the identification of potential drug candidates with heightened precision and efficiency, propelling breakthrough treatments. The exploration navigates through AI-driven computational models, showcasing their role in expediting drug validation and optimization. AI's iterative learning enhances predictive capabilities, forecasting medication efficacy and safety profiles, thereby minimizing clinical trial risks and boosting success rates. Beyond acceleration, AI reshapes drug development strategies toward personalized medicine. Analyzing expansive patient datasets, AI tailors treatments based on genetic variations and disease characteristics, promising optimized therapeutic outcomes and minimized adverse effects, marking a departure from traditional healthcare approaches. The methodology employed various research techniques, including literature reviews, data collection, surveys, case studies, synthesis, and recommendations, offering comprehensive insights into AI's impact on drug discovery. In conclusion, the study emphasized AI's transformative potential in revolutionizing drug discovery, advocating for continued exploration and integration to optimize pharmaceutical research and development practices.
Improving Distance Learning Security using Machine Learning Asiyah Ahmad
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 2 (2023): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

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Abstract

This study explores the intersection of machine learning and distance learning security, aiming to fortify online educational platforms amidst the evolving digital landscape. With technological advancements fueling the rise of distance learning, concerns regarding cybersecurity in virtual educational environments have grown significantly. The fusion of machine learning and distance learning security represents a proactive approach to bolstering safety and integrity within virtual classrooms. Leveraging sophisticated algorithms, this amalgamation seeks to preempt security breaches by identifying irregular patterns, addressing vulnerabilities, and swiftly countering risks like phishing attempts and data breaches. By utilizing historical data and real-time monitoring, machine learning models offer predictive capabilities, enabling educational institutions to anticipate emerging threats and safeguard the learning process while ensuring data integrity and user privacy. While machine learning techniques, such as anomaly detection and predictive modeling, have shown promise in fortifying security measures, ethical considerations and collaborative efforts are essential for responsible implementation. This comprehensive study, involving literature review, knowledge enrichment, case studies, and informed conclusions, aims to guide further research and practical applications in enhancing distance learning security through machine learning.
Cosine Similarity-based Plagiarism Detection on Electronic Documents Lidia Permata Sari
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 2 (2023): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

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Abstract

This study addresses the prevalent issue of plagiarism in academic theses documents, recognizing the potential for undetected similarities within various sections of documents, escaping supervisor oversight. Proposing a solution utilizing the cosine similarity method—a robust technique in natural language processing and document analysis—this research aims to mitigate plagiarism occurrences. The method's benefits, such as independence from document length and high accuracy, advocate for its adoption in plagiarism detection. The study delineates the Waterfall model employed for systematic development, showcasing its structured but inflexible nature in accommodating evolving software requirements. Additionally, the elucidation of cosine similarity mechanics elucidates its pivotal role in quantifying textual resemblance between documents. Practical demonstrations using TF-IDF vectorization and cosine similarity computation offer a step-by-step understanding of the method's implementation. System design, illustrated through UML diagrams and system interface depictions, underscores the comprehensive approach taken in creating a plagiarism detection application. Lastly, successful Black Box testing confirms the application's adherence to functional criteria, validating its efficiency in identifying potential instances of plagiarism. This study contributes significantly to addressing plagiarism concerns through a robust detection mechanism.
Vector Space Model-based Information Retrieval Systems at South Sumatera Regional Libraries M. Akbar As Shiddiqi; A Sanmarino
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 2 (2023): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

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Abstract

This study presents an overview of the research aimed at optimizing library information retrieval through the utilization of the Vector Space Model (VSM) method in a computer science context. Libraries, as publicly financed collections, provide extensive knowledge resources, eliminating the need for individual book purchases. However, the challenge lies in efficiently navigating the expanding library collections. To tackle this issue, the study employs information retrieval techniques, particularly the VSM method, which assesses term similarity by assigning weights to terms, enabling document and query representation as vectors. The relevance between documents and queries is measured through vector similarity. This approach, integrated with indexing, streamlines collection retrieval in libraries. Employing the Waterfall model for system development, the research outlines phases like analysis, design, coding, testing, and implementation. While effective, the model's rigidity in accommodating evolving requirements poses limitations. The VSM method's numerical representation of text documents facilitates precise similarity calculations, supported by TF-IDF values indicating term importance in documents relative to the corpus. The study further extends to system design using UML diagrams and a visitor interface, integrating VSM for efficient search functionality. Black-box testing confirms the robustness of the system components and interfaces. Overall, this research presents a systematic approach to enhance information retrieval in libraries, emphasizing the VSM's pivotal role in optimizing document searches within expansive collections.

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