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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
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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 476 Documents
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Sistem Pembayaran Siswa Berbasis Web dengan Metode Web Engineering untuk Digitalisasi Administrasi Sekolah Indri Agustari; Nyimas Sopiah
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1382

Abstract

The administration of student payments at SMK Muhammadiyah 03 Palembang still relies on administrative ledgers and Microsoft Excel, so payment data is not integrated into a single database, data retrieval and report recapitulation take a relatively long time, paper-based payment receipts are prone to damage or loss, and students and parents cannot independently monitor their billing and arrears status. This study aims to develop a web-based student payment system that integrates student data management, invoice issuance, payment transactions, and reporting within a single centralized database using role-based access control. This study applies a descriptive qualitative approach with data collected through observation, interviews with the school payment officer, and a literature review. System development applies the Web Engineering method at the communication, planning, modeling, and construction stages, while the system design is modeled using use case, class, and activity diagrams. The system is implemented using the PHP programming language with the Laravel framework, an SQLite database, Tailwind CSS, and JavaScript. The resulting system provides five access rights, namely secretary, treasurer, student, parent, and principal, and is able to manage tuition contribution fees, registration fees, and re-registration fees through two channels, namely cash recording by the treasurer and bank transfer with proof upload and treasurer validation. Black box testing of 14 functional scenarios across all user roles produced a conformity rate of 100% with a valid status. The novelty lies in integrating three payment types, two payment channels, and five user roles, including parents and the principal, in one unified system.
Analysis of the Effect of SMOTE on the Performance of Random Forest in Classifying the Risk of Death in Heart Failure Patients Alyiza Dwi Ningtyas; Rama Prameswara Ritonga; Rizka Salsabila Riadi; Arif Hamied Nababan; Surizar Rahmi Danur
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1341

Abstract

Class distribution imbalance in the heart failure patient dataset has the potential to affect the ability of the classification model to identify patients experiencing death events. This study aims to analyze the effect of the Synthetic Minority Over-sampling Technique (SMOTE) application on the performance of the Random Forest algorithm in classifying death events in heart failure patients. The dataset used is the Heart Failure Clinical Records Dataset consisting of 299 patient data, with 12 predictor attributes and one target attribute, namely DEATH EVENT. Data processing was carried out using the Python programming language through the Google Colab platform. The research stages include data preparation, dataset division with a ratio of 80:20, application of SMOTE to training data, formation of a Random Forest model, and performance evaluation using accuracy, precision, recall, and F1-score metrics. In addition, Stratified 5-Fold Cross-Validation was applied as an additional evaluation to analyze model performance on several data divisions. The test results with an 80:20 data split showed that the model before and after applying SMOTE produced an accuracy value of 83.33%, a positive class precision of 80.00%, a recall of 63.16%, and an F1-score of 70.59%. Meanwhile, the results of Stratified 5-Fold Cross-Validation showed that the application of SMOTE increased the average recall value from 69.74% to 76.00% and the F1-score from 73.34% to 74.75%. However, the average accuracy value decreased from 83.93% to 83.60%, while the precision decreased from 77.93% to 73.74%. These findings indicate that the application of SMOTE has the potential to improve the ability of Random Forest in identifying patients with the label DEATH EVENT = 1, although it does not improve all evaluation metrics. This study is expected to provide an empirical contribution in evaluating the effectiveness of SMOTE in classifying imbalanced medical data, especially in improving the detection ability of minority classes.
Analysis of Food Distribution Efficiency Clustering Based on Cost and Delivery Time Using the K-Means Algorithm Muhammad Syahputra Novelan; Solly Aryza; Puspita Wanny
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1345

Abstract

Food distribution efficiency is an important factor in maintaining supply chain performance, particularly in controlling transportation costs and ensuring timely delivery. Inefficiencies in distribution may lead to increased operational expenses and delays in delivering food products to their destinations. This study aims to analyze food distribution efficiency by grouping distribution activities based on transportation costs and delivery time using the K-Means clustering algorithm. The research stages consist of data collection, data preprocessing, feature selection, data standardization, determination of the optimal number of clusters, implementation of the K-Means algorithm, and cluster evaluation. Transportation cost and delivery time are used as the main variables to identify distribution patterns and classify distribution activities into efficiency groups. The resulting clusters are intended to distinguish distribution activities with relatively efficient, moderate, and less efficient characteristics, providing a basis for identifying operational patterns that require improvement. Data visualization is also used to support the interpretation of clustering results and facilitate comparisons among groups. The experimental results, based on 330 simulated food distribution records, produced three clusters. Cluster 1 consisted of 110 records, with an average distribution cost of IDR 176,363 and an average delivery time of 2.83 hours. Cluster 2 contained 111 records, with an average distribution cost of IDR 338,472 and an average delivery time of 5.83 hours. Cluster 3 comprised 109 records, with an average distribution cost of IDR 535,296 and an average delivery time of 9.49 hours. The clustering evaluation yielded a Silhouette Score of 0.679 and a Davies–Bouldin Index of 0.444. This study is expected to provide an analytical framework for supporting data-driven decisions in food distribution management. The proposed approach can assist distribution managers in evaluating cost and delivery-time patterns and identifying opportunities to improve operational efficiency. The validity of the findings depends on the quality of the operational data and the evaluation of the resulting clusters.
Pengembangan Aplikasi Rekamedi Untuk Mendukung Interoperabilitas Data Pelayanan Rawat Jalan Menggunakan Standar FHIR Christina Yuli Anggita; Restu Ardi Putranto; Evapras Kurniawan Panji Saputra; Noor Hasan; Umi Khultsum
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1386

Abstract

The mandatory transition toward Electronic Medical Records (EMR) integrated with the SATUSEHAT platform confronts healthcare facilities with data interoperability issues, as most hospital and primary care information systems are developed independently with siloed database structures. The most difficult technical challenge is mapping operational data stored in tabular form into nested Fast Healthcare Interoperability Resources (FHIR) R4 structures, complete with ICD-10, ICD-9 CM, LOINC, and SNOMED-CT coding and the inter-resource references that must be satisfied before SATUSEHAT accepts the data. Unlike previous studies that stopped at conceptual reviews or performance testing, this research aims to design and build RekaMedi, a web-based application that addresses this challenge through automatic transformation of data into FHokIR resources, such as Patient, Encounter, Condition, and Observation, along the 28-step outpatient service flow of the SATUSEHAT Playbook, supported by medical code validation and synchronization through a RESTful API. The software was developed using the Waterfall method with PostgreSQL, Directus, Astro, and React. Blackbox Testing showed that all tested core functions performed according to specification, while Usability Testing involving nine participants yielded a Success Rate of 93.2% and a System Usability Scale (SUS) score of 75.6 (Grade B), above the reference average of 68. These results indicate that automatic data transformation can be delivered through an interface that users learn effectively, although API-level integration testing and evaluation by healthcare workers in real facilities remain for future work.
Perbandingan K-Means dan K-Medoids untuk Analisis Perilaku Operasional Kunjungan Perpustakaan Sekolah Menengah Atas Rian Septian Anwar; Entin Sutinah; Nani Agustina
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1412

Abstract

Library visit records provide information about patron behavior, yet aggregate counts do not explain differences in individual activity intensity and recency. This gap hampers senior high school library managers with limited staff in setting service priorities. This study compares K-Means and K-Medoids to analyze the operational visit behavior of the SMAN 48 Jakarta library. The dataset contains 10,050 transactions recorded between August 18, 2025 and October 7, 2026, represented as 1,033 anonymized visitor profiles. The study follows the CRISP-DM framework, constructs nine recency–frequency–temporal features, applies logarithmic transformation and Z-score standardization, and evaluates 14 configurations with two to eight clusters. K-Means with two clusters achieves the highest Silhouette score of 0.4114 and a Davies–Bouldin Index of 1.1651, whereas the best K-Medoids configuration uses five clusters with scores of 0.2825 and 1.2348. The selected partition contains 165 low-intensity visitors (15.97%) and 868 relatively more active visitors (84.03%). Ten initialization runs reproduce the membership exactly, while removing active days, which duplicates visit frequency, lowers the Silhouette to 0.3896 with an Adjusted Rand Index of 0.9705; the two-segment structure persists although part of its separation stems from double weighting. The novelty lies in an evaluation protocol for school library visit logs that combines a two-algorithm comparison, metric re-verification, initialization stability testing, and feature sensitivity analysis. Its practical contribution comprises a basis for peak-hour staff scheduling, a follow-up list of infrequent visitors, and a semester-based segment update cycle. The results describe recorded utilization and do not establish reading interest, satisfaction, or academic impact.
Pemetaan Skor Pola Pangan Harapan Tingkat Provinsi Menggunakan K-Means Clustering dengan Inisialisasi Centroid Deterministik dan Validasi Multimetrik Afriza Fliyani; Wanayumini Wanayumini
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1421

Abstract

Food security is determined not only by food availability but also by the diversity and nutritional balance of household consumption, which in Indonesia is measured by the Desirable Dietary Pattern score. Geographic, social, and economic differences among provinces lead to widely varying scores, while objective, updatable mappings remain limited. This study aimed to map Indonesian provinces based on their Desirable Dietary Pattern scores, to analyze the spatial and temporal characteristics of each group, and to implement the K-Means Clustering algorithm in a web-based system. The data consisted of 242 provincial score records for 2018–2024 obtained from the Indonesian One Data portal. The data were normalized with the Min-Max method and grouped into three clusters using Euclidean distance, with initial centroids taken from the highest, median, and lowest values. The number of clusters was examined with the Elbow method, cluster quality was assessed with the Silhouette Score and the Davies-Bouldin Index, and the stability of the results was tested through a sensitivity analysis of 100 random and 100 K-Means++ initializations together with a computational runtime measurement. The algorithm converged at the fifth iteration and produced a High cluster of 84 records (centroid 91.08), a Medium cluster of 109 records (centroid 83.43), and a Low cluster of 49 records (centroid 74.12). A Silhouette Score of 0.578 and a Davies-Bouldin Index of 0.516 indicated a good cluster structure. The three initialization strategies differed in the membership of only two records lying at cluster boundaries, while the deterministic initialization converged in 5 iterations with a median runtime of 0.16 ms, faster than random initialization (0.29 ms) and K-Means++ (0.30 ms). The number of provinces in the High category rose from 5 in 2018 to 22 in 2024, whereas East Nusa Tenggara and Maluku consistently remained in the Low category. The web system produced results identical to the manual calculation, making it a suitable tool for prioritizing food security policy. The contributions of this study are (1) a reproducible K-Means procedure with deterministic initialization whose stability has been tested, (2) a seven-year category map of the Desirable Dietary Pattern scores of all provinces with explicit score boundaries between categories, and (3) a transparent, updatable web-based system that supports the prioritization of food security policies.

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