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
40 Documents
<![CDATA[Performance and Quality Evaluation of the External Alarm System Based on ISO/IEC 25010 Standards ]]>
<![CDATA[M Aidil Irawan]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 1 (2026): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i1.88
<![CDATA[This study evaluates the performance and quality of an external alarm system using the ISO/IEC 25010 software product quality model. The evaluation focuses on eight quality characteristics, namely functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, to provide a comprehensive assessment of the system. A quantitative approach was employed, and data were analyzed using a measurement model to test construct validity and reliability. The results of the validation test show that all indicators have outer loading values above 0.70 and Average Variance Extracted (AVE) values exceeding 0.50, confirming satisfactory convergent validity. Reliability testing also indicates strong internal consistency, with Cronbach’s Alpha and Composite Reliability values for all constructs exceeding the recommended threshold of 0.70. These findings demonstrate that the proposed measurement model is valid and reliable for evaluating external alarm systems. Overall, the study confirms that ISO/IEC 25010 provides a robust and effective framework for assessing software quality and performance in safety-critical alarm systems.]]>
<![CDATA[Improving Operational Efficiency and Customer Service through a Web-Based Restaurant Management Information System ]]>
<![CDATA[Yeti Friyani]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 1 (2026): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i1.89
<![CDATA[The restaurant industry faces increasing pressure to improve operational efficiency and deliver high quality customer service in a competitive and fast paced environment. This study aims to design and implement a web-based Restaurant Management Information System (RMIS) to address common operational challenges, including order processing delays, inventory inaccuracies, and fragmented service workflows. The system was developed using an Agile approach to ensure flexibility and alignment with user requirements. Data were collected through observation, interviews, and literature review, while system evaluation was conducted through functional testing and user feedback. The results indicate that the RMIS significantly improves order processing efficiency, enhances coordination between staff, and increases customer satisfaction through faster service and real time order tracking. These findings demonstrate that a web based RMIS is an effective solution for supporting efficient restaurant operations and improving overall service quality.]]>
<![CDATA[Evaluating End-User Satisfaction of Retail Minimarket Applications Using the Net Promoter Score Method ]]>
<![CDATA[M Fakhri S]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 1 (2026): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i1.90
<![CDATA[The rapid adoption of digital applications in retail minimarkets has made end-user satisfaction a critical factor influencing customer loyalty and continued usage. This study evaluates end-user satisfaction of a retail minimarket application using the Net Promoter Score (NPS) method, which measures users’ willingness to recommend the application to others. A quantitative approach was employed by distributing an NPS-based questionnaire to active users of the application. Respondents were classified into promoters, passives, and detractors based on their ratings. The results show that 52.0% of users were promoters, 27.3% were passives, and 20.7% were detractors, resulting in an NPS value of 31.3. This positive score indicates that the application is generally well received and demonstrates a satisfactory level of user loyalty. However, feedback from passive and detractor users reveals opportunities for improvement in system performance and feature completeness. Overall, the study confirms that the NPS method is an effective and practical tool for assessing end-user satisfaction in retail minimarket applications.]]>
<![CDATA[A Data Processing Information System for Oil Palm Harvest Results ]]>
<![CDATA[M Febrianza]]>;
<![CDATA[Ahmad Sanmorino]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 1 (2026): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i1.91
<![CDATA[Oil palm plantations require accurate and timely harvest data to support effective operational and managerial decision making. However, many plantations still rely on manual data recording methods that are prone to delays, errors, and data inconsistency. This study develops a data processing information system for oil palm harvest results using the Agile–Scrum approach to improve efficiency and data reliability. The system supports structured harvest data entry, automated processing, and real time reporting. Black-box testing was conducted to evaluate functional correctness, and the results show that all tested system functions operated as expected, with a 100% pass rate across key scenarios, including data entry, validation, and report generation. Performance comparison results indicate that the proposed system reduces data entry time by approximately 70%, decreases error rates by up to 85%, and shortens daily report preparation time from several hours to less than five minutes. These results demonstrate that the developed system effectively enhances accuracy, efficiency, and data accessibility in oil palm harvest management.]]>
<![CDATA[From Traditional to Intelligent Agriculture: A Vision for the Future ]]>
<![CDATA[Ican Anwar]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 1 (2026): JOSAPEN - January ]]>
Publisher : <![CDATA[PT. Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i1.94
<![CDATA[The transition from traditional agriculture to intelligent, data-driven farming systems is increasingly critical for addressing challenges related to climate change, resource limitations, and food security. This study presents a comprehensive framework for intelligent agriculture by integrating Internet of Things technologies, machine learning techniques, and decision support systems to enhance agricultural productivity and sustainability. The proposed approach follows a structured methodology involving data acquisition, preprocessing, feature selection, intelligent modeling, and performance evaluation. Experimental results indicate that intelligent agriculture improves water-use efficiency by approximately 28%, reduces fertilizer usage by 22%, and enhances crop yield prediction accuracy from 62% to 88% when compared with traditional farming practices. Early pest and disease detection capabilities are improved by nearly 35%, enabling timely intervention and reduced crop losses. These findings demonstrate that intelligent agriculture significantly outperforms conventional methods while promoting sustainable resource management. Despite challenges related to infrastructure and adoption, the study confirms that intelligent agriculture represents a promising and resilient solution for future agricultural systems.]]>
<![CDATA[Regulatory Approaches to AI-Generated Video Content ]]>
<![CDATA[Yatama Zahra]]>;
<![CDATA[Amirah]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 2 (2026): JOSAPEN - July ]]>
Publisher : <![CDATA[Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i2.115
<![CDATA[The rapid advancement of artificial intelligence (AI) has transformed the creation and distribution of video content, enabling the production of highly realistic synthetic media at unprecedented speed and scale. While AI-generated videos offer significant benefits in education, entertainment, marketing, and communication, they also create complex legal and regulatory challenges involving misinformation, privacy violations, intellectual property infringement, impersonation, and public trust. This study examines existing regulatory approaches to AI-generated video content and assesses their effectiveness in addressing emerging legal and governance challenges. Using a qualitative literature review and comparative policy analysis, the research examines regulatory frameworks across jurisdictions, focusing on transparency requirements, content labeling, accountability mechanisms, content moderation, privacy protection, and liability provisions. The findings indicate that existing regulations often struggle to keep pace with technological developments, creating gaps in enforcement, transparency, and legal certainty. The study proposes a balanced regulatory framework that promotes innovation while strengthening accountability, transparency, and protection of individual rights.]]>
<![CDATA[Implementation of a Progressive Web App for Stock Opname Management at Cakra Abhipraya ]]>
<![CDATA[Rafi Bowo Raharjo]]>;
<![CDATA[Kiki Setiawan]]>;
<![CDATA[Mesra Betty Yel]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 2 (2026): JOSAPEN - July ]]>
Publisher : <![CDATA[Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i2.123
<![CDATA[The advancement of information technology in the digital era encourages organizations to improve inventory management efficiency through modern and flexible web-based systems. Cakra Abhipraya still faces challenges in the stock opname process, including manual data recording, delays in stock information synchronization between divisions, and difficulties accessing the system via mobile devices in the field. These issues cause inventory monitoring to be less effective and prone to errors in stock data. This research aims to implement a Progressive Web App (PWA) on a web-based stock opname system at Cakra Abhipraya to improve efficiency, flexibility, and accessibility of inventory management. The system was developed using the Agile Development methodology with HTML5, CSS3, and plain JavaScript as the frontend, and Google Apps Script integrated with Google Spreadsheet as the backend. The PWA concept is applied through service workers and a web app manifest to support offline capability, responsive interface, and installability. Testing was conducted using the Black-Box Testing method and a Google Lighthouse audit. The results show that the system achieved a perfect PWA score (100) in the Google Lighthouse audit, improved the stock opname process efficiency by 73% compared to the manual method, and achieved an average user satisfaction score of 4.3 out of 5. The implementation of this system has proven to significantly support the digitalization of inventory management at Cakra Abhipraya.]]>
<![CDATA[Analysis of Information System Quality and Customer Satisfaction Using the PIECES Framework at Warkop Abas Jakarta ]]>
<![CDATA[Ridho Arif Hidayat]]>;
<![CDATA[Sri Lestari]]>;
<![CDATA[Mesra Betty Yel]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 2 (2026): JOSAPEN - July ]]>
Publisher : <![CDATA[Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i2.124
<![CDATA[Advancements in information technology have encouraged business operators, including micro, small, and medium-sized enterprises (MSMEs), to leverage information systems to enhance service quality and customer satisfaction. Warkop Abas, a culinary business in East Jakarta, utilizes an information system to support its operations; however, the quality of this system has never been systematically evaluated. This study aims to analyze the information system's quality based on the six PIECES dimensions (Performance, Information, Economics, Control, Efficiency, and Service) and their impact on customer satisfaction, both simultaneously and partially, while identifying the most dominant dimension. A quantitative approach was employed, utilizing a survey of 1,000 Warkop Abas customers; data were analyzed using IBM SPSS Statistics through validity, reliability, and multiple linear regression tests. The results indicate that all statement items were valid and all variables were reliable (Cronbach's Alpha > 0.70). Simultaneously, the six PIECES dimensions significantly influenced customer satisfaction (F = 1069.263; p = 0.000), with an R-square value of 0.866. Individually, the Performance, Information, Economics, Control, and Efficiency dimensions showed a significant impact, whereas the Service dimension did not. The Efficiency dimension emerged as the most dominant factor (Beta = 0.272) influencing customer satisfaction at Warkop Abas.]]>
<![CDATA[Explainable Machine Learning for DDoS Attack Detection with Physical Network Validation ]]>
<![CDATA[Muhammad Azzam Anshori]]>;
<![CDATA[Rahyul Amri]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 2 (2026): JOSAPEN - July ]]>
Publisher : <![CDATA[Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i2.131
<![CDATA[Distributed Denial-of-Service (DDoS) attacks remain one of the most disruptive threats to network infrastructure, yet many machine learning (ML)-based detection studies report only offline benchmark performance without verifying whether that performance holds under real network conditions. This study evaluates two explainable ML classifiers, XGBoost and Random Forest, for DDoS detection and examines whether their near-perfect offline accuracy translates into reliable physical-network operation. The study combines offline benchmarking on the CIC-DDoS2019 dataset (293,485 flows) with physical-network validation using a working Intrusion Detection System (IDS) prototype under a controlled SYN-flood attack. Session-disjoint stratified sampling prevented flow-level leakage across attack sessions, while SHapley Additive exPlanations (SHAP) interpreted global and local feature importance. Offline, both classifiers achieved near-perfect performance (accuracy 99.99% for XGBoost, 99.98% for Random Forest; F1 = 0.9999; ROC-AUC up to 1.0000), with no statistically significant difference between them (McNemar's exact test, p = 0.2188), though XGBoost achieved approximately 3.69 times higher inference throughput (1,819,816 flows/s). SHAP identified Min Packet Length, Fwd Packet Length Min, Inbound, Protocol, and Init_Win_bytes_forward as the most influential features. In physical deployment, however, the IDS prototype flagged 6.26% of captured flows (6,935 of 110,762) as ATTACK during the SYN-flood test, and a separate 397-flow ambient-benign subset yielded a 6.80% false positive rate (95% Wilson CI: 4.72–9.71%), with short-duration SSDP/UPnP-style UDP control traffic accounting for 70% of observed false positives. This gap shows that near-perfect offline accuracy does not guarantee low false positives in real deployment, indicating that offline benchmarks alone are insufficient for validating IDS readiness.]]>
<![CDATA[Cybersecurity Threat Detection in IT Infrastructure Using an XGBoost-Based Model ]]>
<![CDATA[Temitayo Afolen]]>;
<![CDATA[Amariel Nkemdilim]]>
<![CDATA[ Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 2 (2026): JOSAPEN - July ]]>
Publisher : <![CDATA[Lentera Ilmu Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.70356/josapen.v4i2.136
<![CDATA[The increasing complexity of IT infrastructure has created significant challenges in detecting cybersecurity threats, particularly malicious network activities that can evade conventional security mechanisms. This study proposes an XGBoost-based machine learning model for detecting cybersecurity threats in network traffic. A controlled cybersecurity simulation environment was used to generate 50,000 network-flow observations representing benign activities, including web browsing, DNS requests, file transfer, and client-server communication, as well as malicious activities involving DoS, DDoS, port scanning, and brute-force attacks. After preprocessing, 48,000 observations were retained and divided into 80% training and 20% testing datasets using stratified sampling. XGBoost feature importance and randomized hyperparameter optimization with five-fold cross-validation were applied to improve model performance. The optimized XGBoost model achieved 98.30% accuracy, 98.10% precision, 97.90% recall, 98.00% F1-score, and 99.20% ROC-AUC, with a reported false-positive rate of 1.20%. XGBoost also outperformed Logistic Regression, Decision Tree, Random Forest, and SVM. SHAP analysis identified Flow Packets/s, Flow Bytes/s, Flow Duration, and packet-related characteristics as influential features. These findings demonstrate the potential of XGBoost as an accurate and explainable approach for cybersecurity threat detection in IT infrastructure.]]>
