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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
Core Subject : Science,
Computer Science & IT
Bulletin of Computer Science Research covers the whole spectrum of Computer Science, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, Fault analysis, and Diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High-Performance Computing • Information storage, security, integrity, privacy, and trust • Image and Speech Signal Processing • Knowledge-Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition, and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Support Vector Machines • Ubiquitous, grid and high-performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data • Cryptography • Model and Simulation • Image Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 462 Documents
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Implementasi Role-Based Access Control (RBAC) pada Sistem Monitoring Kenaikan Jabatan Fungsional Guru Muhammad Rivaldi; Triase Triase
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1040

Abstract

As educators, functional position promotion (JaFung) serves as a means of advancing teachers’ careers as well as an indicator of their success in meeting established quality standards. However, in practice, this process still faces several challenges, such as teachers having difficulty monitoring the progress of their submitted proposals and institutions needing to contact teachers individually when issues arise. These problems indicate the absence of integration between progress monitoring and communication within a unified system. This study aims to propose a system model that integrates monitoring, communication, and RBAC-based security mechanisms. The Role-Based Access Control (RBAC) mechanism is used as a framework for managing interactions among actors across institutions and is designed using a scenario-driven role engineering approach to produce an RBAC model that adheres to the principle of least privilege. The system development method employed in this study is the Software Development Life Cycle (SDLC) using the Waterfall model. The results of black-box testing show that the developed system operates in accordance with its specifications and fulfills functional requirements. Access violation testing involving 10 unauthorized access attempts resulted in a violation rate of 0%, indicating that all unauthorized access was successfully prevented by the system. In addition, there was a 37.21% reduction in the number of permissions in the RBAC-implemented system, demonstrating the application of the least privilege principle. The proposed system not only facilitates document management and proposal progress monitoring but also enhances security and ensures more structured access management in the functional position promotion process.
Peningkatan Kualitas K-Means Clustering Data Audio Musik Menggunakan Transformasi TableDC Muhammad Aksa Hermawan; Florentina Yuni Arini
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1043

Abstract

Clustering audio music data with high features typically suffers from performance degradation due to the curse of high dimensionality. A dataset with 518 classical K-Means features typically struggles to model nonlinear relationships between data. The purpose of this study is to analyze the implementation of the TableDC latent space transformation technique in the preprocessing stage before K-Means on the FMA Small dataset. This case study contains 8,000 songs with 518 audio features and is divided into eight music genres. The performance of K-Means on the original data is compared with that of K-Means on the latent space extracted by TableDC. The analysis is performed using several metrics such as the Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index, Adjusted Rand Index, inertia or WCSS, and the number of iterations. The experimental results indicate a percentage improvement offered by the method. The Silhouette Score increased by 53 percent from the initial value of 0.0249 to 0.0382. Similarly, the ARI value increased from the initial value of 0.0876 to 0.0893. However, these absolute values remain very low, indicating that the formed cluster structures are still weak and substantially overlapping. In this case, the latent representation contributed to increasing the convergence efficiency from 63 to 47 iterations. The WCSS value also decreased from 3,433,413 to 20,628. However, unlike the two previous indicators, the linear-based DBI and CHI actually obtained better results compared to the initial model, which demonstrates the model's weakness in the context of conventional evaluation. Overall, the TableDC transformation has been shown to improve computational efficiency, but its performance has not fully resolved the issue of overlapping class separation.
Evaluasi Keamanan Website Direktori Akademik Menggunakan NIST SP 800-115 Fito Nardian; Rahmad Abdillah; Benny Sukma Negara; Reski Mai Candra
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1044

Abstract

Evaluating the security of web-based academic information systems has become crucial as cyber threats in higher education environments increase. The track record of security incidents in information systems at UIN Sultan Syarif Kasim Riau has prompted an urgent need for preventative action; therefore, the website https://seminar-fst.uin-suska.ac.id, as an active academic service that stores sensitive data, requires a proactive evaluation. Testing used a black-box testing approach through four phases: planning, discovery, attack, and reporting. The results revealed a critical vulnerability in the form of SQL injection in URL parameters, which allows unauthorized database enumeration (MariaDB), thus threatening data confidentiality and integrity. Additionally, medium-level vulnerabilities were discovered, such as the use of an outdated JavaScript library (Moment.js 2.8.1) and misconfiguration of HTTP security headers, including the absence of a Content Security Policy (CSP) and an Anti-CSRF mechanism. Recommendations include prepared statements, strict input validation, updating dependencies, and strengthening security configurations.
Transformasi Pencatatan Keuangan Melalui Implementasi Odoo POS dan Accounting Berbasis RAD Pada UMKM Roro Mawar Amalia; Suhendi Suhendi
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1048

Abstract

This study discusses the integrated implementation of Odoo Point of Sale (POS) and Accounting modules at Tahu Bandung Sari Mawar, a small and medium enterprise (SME) that previously managed financial records manually using notebooks. The main issues identified were unstructured transaction recording, difficulties in retrieving past data, poorly documented expense transactions, and the absence of monthly financial reports. This research employed a qualitative case study approach using the Rapid Application Development (RAD) method adapted for ready-made software implementation. In this study, the construction phase focused on system configuration, workflow adjustment, module integration, and testing instead of software development from scratch. Data were collected through observation, interviews, and documentation. System testing used Black Box Testing and User Acceptance Testing (UAT), while evaluation was conducted through qualitative comparative analysis. Black Box Testing results showed that all 7 testing scenarios were successfully executed. UAT results obtained a score of 42 out of 50 (84%), indicating good user acceptance. The comparative evaluation showed that transaction recording time decreased from approximately 5 minutes to 2 minutes after implementation. In addition, Odoo enabled automatic integration between POS and Accounting modules, more detailed transaction recording, and the generation of financial reports from recorded transactions.
Pengaruh Penggunaan User Centered Pada Perancangan UI/UX Pada Model Aplikasi Penjualan Es Teler Berbasis Website Mutiara Nurikhlimah; Ade Dwi Putra
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1049

Abstract

Abstract The rapid development of information technology encourages businesses to adapt by utilizing digital platforms, one of which is through e-commerce websites. The Es Teler El website is developed as a web-based sales platform that requires optimal User Interface and User Experience (UI/UX) design to provide convenience, ease of use, and efficiency in transaction processes. This study aims to design the UI/UX of the Es Teler El website using the User-Centered Design (UCD) method and to evaluate the usability level of the resulting prototype. The UCD method is implemented through four main stages: Understand Context of Use, Specify User Requirements, Design Solution, and Evaluate Design Against Requirements. Data collection was conducted through interviews with 20 respondents to identify user needs. The results of the needs analysis were then implemented into wireframe, mockup, and high-fidelity prototype designs using Figma. Usability evaluation was carried out using the System Usability Scale (SUS) method involving the same respondents through predefined testing scenarios. The results show that the Es Teler El website prototype achieved an SUS score of 82.5, which falls into the “Excellent” category (Grade A). This indicates that the design meets usability aspects, including ease of use, efficiency, and user satisfaction. Therefore, the application of the UCD method is proven to be effective in producing UI/UX designs that align with user needs and improve the quality of user experience in web-based culinary e-commerce platforms.
Penerapan Algoritma K-Means Clustering untuk Pengelompokan Pola Penjualan Sembilan Bahan Pokok pada Pusat Distribusi Berbasis Dataset Kaggle Fitriah Fitriah; Dia Komalla; Muhajir Yunus
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1054

Abstract

Sales data management of staple food products (sembako) is an important concern for distribution centers because stock storage decisions are still largely based on the estimation or experience of warehouse staff rather than on adequate historical data analysis. This condition creates the risk of overstocking slow-moving products on one hand, and stockouts of high-demand products on the other, potentially causing operational losses. This study aims to apply the K-Means algorithm to cluster staple food sales data based on sales patterns, using product type and total units sold as variables after being encoded and scaled. The dataset used was obtained from Kaggle, consisting of 1,200 sales transaction records. The research stages include problem identification, data collection, data pre-processing (cleaning, transformation, and standardization), implementation of the K-Means algorithm with k=3, result analysis, and model evaluation using the Silhouette Score. The clustering process was carried out using Python libraries in Google Colaboratory. The results show that all sales data were successfully grouped into three clusters labeled -1, 0, and 1, namely cluster -1 (not in demand), cluster 0 (less in demand), and cluster 1 (in demand). Cluster 1 dominates with 930 data points (77.5%), cluster 0 contains 269 data points (22.4%), while cluster -1 contains only 1 data point (0.1%), indicating an outlier among products with very low sales volume. These findings demonstrate that the K-Means algorithm is effective in identifying sales patterns and can be used as a basis for decision-making in inventory management strategies at distribution centers, particularly in determining storage priorities based on product demand levels.
Deteksi Pelanggaran Durasi Berhenti Kendaraan pada Area Yellow Box Junction Menggunakan Algoritma YOLOv8 Abdul Kholik; Muhammad Dzulfikar Fauzi
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1055

Abstract

Traffic congestion at urban intersections in Indonesia, particularly in Palembang, is exacerbated by the frequent violation of Yellow Box Junction (YBJ) regulations. This study develops an automated detection system for vehicle stopping duration violations in YBJ areas using the YOLOv8 deep learning algorithm, specifically the yolov8n.pt model, optimized with a 3-frame skip technique to enhance computational efficiency. The system is designed to identify vehicles remaining within a predefined Region of Interest (ROI) for more than 5 seconds. Testing conducted at Simpang Angkatan 45 recorded 168 violations compared to 149 violations from manual observation. The primary contribution of this research lies in the development of an automated traffic law enforcement solution tailored to Indonesia's heterogeneous traffic conditions, as well as the implementation of computational optimization techniques that enable near real-time operation on mid-range hardware without significantly compromising detection accuracy . Although a 12.75% detection variance occurred due to ID switching and occlusion factors, this study provides a foundation for more accountable and scalable intelligent surveillance systems in the future.
Penerapan Algoritma Apriori dalam Menganalisis Pola Minat Beli Konsumen di Coffee Shop Renaldi Nur Fahrizal; Ita Arfyanti; Ulfah Nurfadhila
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1056

Abstract

The currently intensive increase in competition within the cafe industry demands that business operators, such as Coffee shop Zecoff Tenggarong, not only focus on product quality but also gain a deep understanding of consumer behavior and buying interest patterns. This understanding is crucial for formulating targeted and sustainable business strategies. This research specifically focuses on analyzing consumer buying interest patterns at Coffee shop Zecoff Tenggarong through the identification of products that tend to be purchased together in a single transaction. To achieve this objective, the study employs a Data Mining approach using the Association Rule Mining technique. The core method implemented on the cafe's sales transaction data over a specific period is the Apriori Algorithm. This algorithm was chosen due to its effectiveness in processing large datasets and identifying frequently co-occurring itemsets. The data analysis process includes the stage of determining critical parameters: support (the frequency degree of the itemset), confidence (the strength of the causal relationship), and lift (the value of association improvement), which are collectively used to filter and generate the strongest and most relevant association rules. The empirical results of the study show that the Apriori Algorithm is highly effective in uncovering hidden purchasing patterns that are difficult to detect through conventional data analysis. The strong association rules derived from this mining process provide important and actionable information for the owner of Zecoff Tenggarong. The strategic implications of these findings include: formulating more targeted cross-selling marketing strategies (for example, recommending companion products that are certainly in demand), optimizing product arrangement (placing strongly associated items in close proximity), and increasing inventory management efficiency (ensuring that items frequently bought together are always in stock). In conclusion, this research concludes that the utilization of Data Mining technology with the Apriori Algorithm is a vital and transformative tool. It not only supports daily operational decision-making but also significantly enhances the coffee shop's competitiveness amidst a tight market rivalry.
Analisis Ternak Menggunakan K-Means Clustering Dalam Business Intelligence Sulaiman Savero Sagi; Safrizal Safrizal
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1059

Abstract

This study aims to analyze livestock population patterns and classify regions in Central Java using data from 2020–2023 covering cattle, goats, and chickens from official sources. The Knowledge Discovery in Database (KDD) framework and K-Means Clustering were applied, with the optimal number of clusters determined using the Elbow Method and Silhouette Score. The results show that the optimal number of clusters is two (K=2), with a Silhouette Score of 0.328, indicating a relatively weak clustering structure with potential overlap. Despite this limitation, the results reveal meaningful segmentation when combined with Business Intelligence analysis. Cluster 0 represents regions with lower population but higher growth, while Cluster 1 represents regions with higher population but lower or negative growth. Further analysis indicates that the relationship between population, growth, and production is not linear, where high production does not necessarily correspond to strong growth. These findings highlight the importance of distinguishing between current production capacity and future growth potential, providing more informative insights for data-driven decision-making in livestock sector management.
Implementasi Business Intelligence Untuk Analisis Data Tingkat Kerawanan Kebakaran Berbasis Wilayah Javier Alvino Alfian; Denny Ganjar Purnama
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1060

Abstract

This study aims to implement a Business Intelligence (BI) approach to analyze fire risk levels based on regional characteristics using open government data from Satu Data Jakarta. The dataset consists of 5,471 records for the period 2024–2025, including hazard, vulnerability, and capacity indicators at the neighborhood (RW) level. The methodology involves Extract, Transform, Load (ETL) using Python, data warehouse design with a star schema in PostgreSQL, OLAP-based analysis using SQL queries, and visualization through a web-based dashboard. The results indicate a significant increase in the proportion of high-risk areas from 16.62% in 2024 to 33.42% in 2025. However, this increase does not fully reflect actual changes in field conditions and may also be influenced by data distribution and the underlying risk classification system. Furthermore, the analysis reveals that fire risk is not evenly distributed but concentrated in specific regions, particularly in several districts of East Jakarta and South Jakarta, highlighting the importance of spatial-based approaches in determining mitigation priorities. This study utilizes pre-defined risk categories provided by the data source without performing predictive modeling or reclassification. The BI implementation not only integrates disparate data but also uncovers distribution patterns, risk trends, and regional priorities more systematically compared to conventional descriptive analysis. The findings contribute to supporting data-driven decision-making, especially in identifying priority areas for fire risk mitigation.

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