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Muhammad Wali
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International Journal Software Engineering and Computer Science (IJSECS)
Published by Lembaga Komunitas Informasi Teknologi Aceh
ISSN : 27764869     EISSN : 27763242     DOI : https://doi.org/10.35870/ijsecs
Core Subject : Science,
Computer Science & IT
IJSECS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJSECS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJSECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications..
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 41 Documents
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Search results for , issue "Vol. 5 No. 2 (2025): AUGUST 2025" : 41 Documents clear
Development of a Prototype for a Product Recommendation System Using Blockchain Technology Adrian Tri Setiawan; Djarot Hindarto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.3855

Abstract

Blockchain technology ensures data security and transparency through decentralization and immutability. Smart contracts facilitate automation and foster trust by reducing dependence on intermediaries. Nevertheless, most existing recommendation systems remain centralized, leaving them susceptible to manipulation and security breaches. Although recommendation algorithms are widely used, their application within blockchain-based systems remains limited. By leveraging the Ethereum blockchain and smart contracts, the proposed system enhances transparency, security, and decision-making reliability. The algorithm ranks products based on price, appearance, quality, size, and availability, with results permanently recorded on the blockchain. Experimental findings indicate that the integration of TOPSIS and PROMETHEE II algorithms improves the recommendation process by systematically evaluating multiple criteria. Each product is assessed according to its proximity to both positive and negative ideal solutions, with the final ranking score calculated as the ratio of the negative distance to the total distance (positive plus negative). For example, Pocari Sweat achieved the highest preference score of 0.9619, indicating it is the top recommendation, while Coca-Cola 390ml scored 0.4182, reflecting a lower ranking. These results demonstrate the algorithms’ capacity to distinguish products objectively, supporting accurate and transparent recommendations. The study advances blockchain-based decision support systems by providing secure and transparent recommendation mechanisms. Additionally, the integration of TOPSIS and PROMETHEE II within a blockchain framework demonstrates both feasibility and effectiveness in decentralized environments.
Development of a Web-Based E-Administration System for the Indralaya Ogan Ilir Police Department Utilizing the System Development Life Cycle (SDLC) Approach Devi Udariansyah; Asta Sapriyogi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.3931

Abstract

Information technology advancement has transformed administrative processes across government institutions, particularly within law enforcement agencies. This research examines the development of a web-based E-Administration application at the Indralaya Ogan Ilir Police Resort, designed to improve efficiency, accuracy, and accessibility in administrative services. The application development follows the System Development Life Cycle (SDLC) methodology, providing a structured approach through planning, analysis, design, implementation, testing, and maintenance phases. The study seeks to optimize administrative workflows, minimize paperwork dependency, and strengthen data security protocols. Results demonstrate that system implementation delivers more effective and efficient administrative processes, enhancing service quality and operational performance within the police department. Future research should focus on integrating advanced security measures and expanding functionality to accommodate broader administrative requirements.
Image Segmentation of East OKU Script Using the Bounding Box Method for Cultural Heritage Digitization M Fikri; Ilman Zuhri Yadi; Yesi Novaria Kunang; Leon Andretti Abdillah
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4045

Abstract

East Ogan Komering Ulu (OKU) is distinguished by its cultural heritage, which encompasses historical artifacts such as traditional houses, crafts, and ceremonial dances. Among the most significant cultural assets are relics inscribed with ancient scripts, including Pallawa and Ulu, which offer valuable insight into the region’s historical literacy. The present study addresses the segmentation of OKU Timur script images through the Bounding Box method. This approach was selected based on its practicality and efficiency, particularly in the context of datasets where script characters exhibit straightforward forms and the overall data volume remains manageable. The segmentation process utilizes Python within the Google Colaboratory platform, ensuring accessible and reproducible workflows. Accurate segmentation is essential to support ongoing digitization and preservation of cultural scripts. The methodology involves gathering data from local artifacts, converting images to binary format, and isolating characters using Bounding Boxes. The results demonstrate that the method effectively separates individual script characters, laying the groundwork for dataset development and subsequent image classification tasks.
Agile Code Board: An Integrated Task, Planning, and Brainstorming Tool for Agile Teams T. Vignesh; J.V. Johnsonselva
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4093

Abstract

Agile methodologies are widely adopted in software development for their effectiveness in team coordination, iterative progress, and comprehensive project oversight. This study analyzes the Agile Code Board, a real-time project management platform designed to assist Agile teams in organizing and executing tasks throughout each sprint. The platform offers multiple workspaces with role-based access, enabling task tracking, immediate team collaboration, and sprint-level planning. Key features include a drag-and-drop Kanban board, a collaborative whiteboard, calendar integration for deadlines and milestones, a filterable task table, and a cross-team program board inspired by SAFe. Agile Code Board is built using Next.js and Hono, with Appwrite for authentication and Liveblocks for real-time collaboration. The system introduces mechanisms that facilitate efficient teamwork and project coordination. This research outlines the system architecture, development practices, and the ways in which the platform enhances Agile workflow management in collaborative environments.
Business Intelligence and Decision Support to Enhance Decision-Making Quality in Higher Education Syamsiah Syamsiah; Agus Darmawan; Halimatusa'diah Halimatusa'diah; Reko Syarif Hidayatullah; Nasrulloh Isnain
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4273

Abstract

The availability of accurate and reliable data is essential for organizational sustainability. Business intelligence (BI) enhances an organization's ability to analyze challenges, support decision-making, and improve performance. The term “Business Intelligence System” refers to applications and technologies that facilitate BI activities, including data collection, storage, access, and analysis—thus providing insights into performance and aiding informed decisions. These activities include decision support systems, querying, reporting, OLAP, statistical analysis, forecasting, and data mining. BI applications encompass reporting tools, analytics platforms, dashboards, alerts, and portals, and involve technologies such as data integration, quality management, warehousing, and content analysis. Accordingly, a Business Intelligence System can function as a Decision Support System. This study uses SPSS version 17 for data analysis to evaluate the impact of BI and decision support on decision-making quality in colleges in Jakarta and Bekasi. ANOVA (F-test) results show an F-value of 117.041, exceeding the F-table value of 3.29, with a significance of 0.000 < α = 0.05. Since the calculated F-value surpasses the critical value and the significance level is below 0.05, the null hypothesis is rejected. Thus, BI and decision support significantly and simultaneously influence decision-making quality (Y). These findings highlight the essential role of BI and decision support in improving decision-making within higher education institutions.
Palembang to Indonesian Language Translation Machine Using the No Language Left Behind Approach Muhammad Finaldo; Yesi Novaria Kunang
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4105

Abstract

The Palembang language, deeply rooted in the cultural fabric of South Sumatra, continues to serve as a vital means of daily communication for many communities. As globalization accelerates, safeguarding such regional languages has become increasingly urgent, particularly through technological solutions that can bridge communication between local speakers and visitors. This study introduces an automatic translation system designed to convert Palembang text into Indonesian, employing the No Language Left Behind (NLLB) algorithm—a recent development in artificial intelligence for language processing. A dataset containing 7,917 pairs of Palembang and Indonesian sentences was assembled for this purpose. The translation models were trained and assessed using BLEU (Bilingual Evaluation Understudy) and chrF (Character n-gram F-score) metrics. The initial model achieved a BLEU score of 22.55 and a chrF++ score of 43.22. Subsequent improvements raised these scores to 30.72 and 55.39, respectively, reflecting a significant enhancement in translation quality and clarity for Indonesian readers. By focusing on a language with limited digital resources, this research demonstrates the potential of modern translation technologies to support both linguistic preservation and practical communication needs in diverse cultural settings.
Log-Based Code Maniac E-Learning Web Development Model Utilizing Adaptive Web Development Techniques Reko Syarif Hidayatullah
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4274

Abstract

Education is a fundamental requirement for human civilization, particularly for children and adolescents. The recent pandemic has compelled the education sector to adopt online learning alternatives. Codemaniac is an e-learning tool developed with gamification techniques to enhance student motivation. However, Codemaniac still lacks adaptive features that optimize user engagement based on individual behaviors. To address this limitation, further development will incorporate adaptive features by utilizing recorded user behavior from log files. This behavioral data will be clustered using the fuzzy c-means algorithm, resulting in three distinct user groups, each receiving a tailored user interface. The system is developed following the SDLC waterfall model, with Python used for clustering implementation. The development process involves three user roles, five additional functional requirements, and one non-functional requirement. System testing employs white-box methods for unit testing and black-box methods for validation.
Implementation of Augmented Reality as an Interactive Medium for Firearms Education Muhammad Nazar Darman; Yuli Asriningtias
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4285

Abstract

Augmented Reality (AR) has been utilized as an interactive educational tool to support safer and more effective learning about various firearms. The application was created with Unity3D and C#, and its functionality was evaluated through black box testing. Findings show that the application operates successfully on targeted devices, though some performance issues were observed, including extended loading times and occasional lag during navigation. AR visual and interactive features enable users to explore firearm components and operational procedures without exposure to real-world risks, as the use of physical firearms is not required. The inclusion of offline access further enables users to engage with the learning materials at their convenience. AR demonstrates considerable promise for improving the quality of firearm training and may be further adopted in technical instruction, military education, and the broader development of digital learning environments.
Hybrid Quantum-Classical Optimization for Energy-Efficient Large Language Models Loso Judijanto; Yuswardi Yuswardi; Fitriyani Fitriyani
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.5099

Abstract

The rapid evolution of Large Language Models (LLMs) has transformed natural language processing, enabling sophisticated applications across various sectors. However, the substantial computational demands associated with training and deploying LLMs result in significant energy consumption and carbon emissions. This study introduces an optimized hybrid quantum-classical framework that integrates variational quantum algorithms (VQAs) with accelerated classical learning techniques. By harnessing quantum computing for complex non-linear optimization and employing prompt learning to minimize full model retraining, the proposed approach enhances both computational efficiency and sustainability. Simulation outcomes indicate that the hybrid method can reduce energy usage by up to 30% and shorten computation time by 25% relative to conventional classical approaches, without diminishing model accuracy. These improvements are substantiated through quantitative analysis and visualized energy metrics. The adaptability of the framework supports its application in diverse areas, including sustainable energy management, supply chain optimization, and environmentally conscious transportation systems. Nevertheless, the broader implementation of such hybrid solutions remains constrained by current quantum hardware capabilities and integration challenges with classical infrastructure. The findings underscore the potential of hybrid quantum-classical optimization as a pathway toward sustainable AI development. Future research should prioritize advancements in quantum hardware reliability and interdisciplinary collaboration to accelerate practical adoption, thereby supporting global efforts in energy efficiency and environmental responsibility.
Social Media Sentiment Analysis of Twitter Regarding People's Housing Savings (TAPERA) Using Naïve Bayes Avry Liyanah Dewy; Mia Kamayani
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4126

Abstract

The advancement of technology has transformed how people interact and express opinions on social media platforms. This research examines Twitter conversations regarding Indonesia's government-initiated Housing Savings Program (TAPERA) through sentiment analysis. The study employed Naïve Bayes classification methodology, with data acquisition conducted via Google Colab platform utilizing the tweet-harvest library. The collection process yielded 1,800 tweets matching predetermined search parameters. Data underwent rigorous preprocessing, including text cleaning and manual sentiment annotation to establish reliable training datasets. Examination of 720 test tweets revealed 473 (65.69%) expressed negative sentiment while 247 (34.31%) conveyed positive sentiment toward the program. The implemented Naïve Bayes model achieved 84.17% accuracy, with negative class precision at 88.71% and recall at 88.60%, while positive class precision reached 78.54% with 76.08% recall. Results indicate the Naïve Bayes approach effectively categorizes public sentiment regarding the TAPERA program, offering valuable feedback for stakeholders responsible for program assessment and enhancement.

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