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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 329 Documents
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Sistem Pendukung Keputusan Smart Leasing Value Asset dengan Perhitungan NPV dan NAL Berbasis Website Sibuea, Febry P J; Paramita, Astriyani Sandya
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.897

Abstract

The process of evaluating the feasibility of asset financing in business organizations is often carried out manually, making it prone to calculation errors, data inconsistencies, and delays in decision-making. This condition creates the need for a system capable of providing structured and objective analysis, particularly in determining whether an asset is more feasible to buy or lease. This study aims to develop a Smart Leasing Value Asset Decision Support System that integrates two financial analysis methods: Net Present Value (NPV) to calculate the present value of future cash flows and Net Advantage to Lease (NAL) to compare the economic benefits of purchasing and leasing an asset. The development method used is Rapid Application Development, which allows for rapid system development through a prototype approach and active user involvement. The implementation results show that the tested asset has a positive NPV value, thus being considered feasible, while a negative NAL value indicates that the purchase option is more profitable than leasing. These findings prove that the system is capable of producing accurate calculations, consistent with manual calculations, and effective in improving the speed and quality of decision-making. This system has the potential for further development by adding sensitivity analysis and real-time financial data integration.
Pengembangan Aplikasi Sistem Presensi Siswa dengan Integrasi Fitur Kehadiran Per Mata Pelajaran dan Izin Digital Budiarti, Arrum Junia; Joko Aryanto
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.826

Abstract

Student attendance is a crucial aspect of school administrative management; however, the conventional paper-based system implemented at SMP Negeri 1 Giritontro has several limitations, such as the risk of data loss and delays in attendance recapitulation. This study aims to develop a student attendance system application integrated with subject-based attendance features and a digital leave permission module. The software development methodology employed in this study is the Waterfall model, which includes the stages of requirements analysis, system design, implementation, and testing. The application was developed using Flutter as the frontend technology, Next.js as the backend framework, and MongoDB as the database. The results of functional testing using the Black Box Testing method indicate a 100% success rate across all normal-condition scenarios, including login functionality, subject data management, attendance recording, and digital leave submission. The system is capable of improving real-time attendance recording efficiency and providing faster access to information for students, teachers, and school administrators. Through this digitalization, student attendance management becomes more accurate, transparent, and significantly reduces administrative workload.
Assessing the Capability of E-Attendance Systems in Local Government Institutions Using COBIT 2019: A Case from Indonesia Siahaan, Andysah Putera Utama; Barus, Efriansyah Putra Bahari; Hasanuddin, Muhammad; Zulfan, Zulfan; Rizaldi, Fakhri
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.831

Abstract

This research evaluates the capability level of the E-Attendance system at the General Bureau of the Regional Secretariat of North Sumatra Province using the COBIT 2019 framework. A mixed-method approach was employed, integrating document analysis, interviews, questionnaires, and system log reviews to assess four key governance and management objectives: DSS06 (Manage Business Process Controls), APO13 (Managed Security), APO14 (Managed Data), and MEA01 (Performance and Conformance Monitoring). The results indicate that the current capability levels range between Level 1 (Performed) and Level 2 (Managed), reflecting partially implemented and inconsistently standardized governance practices. Based on organizational policies, regulatory requirements, and operational needs, the target capability level for all assessed domains is defined at Level 3 (Established), where processes are formally documented, standardized, and consistently implemented across organizational units. The gap analysis reveals deficiencies in data governance structures, security control enforcement, and performance monitoring mechanisms. Nevertheless, the findings demonstrate that improvements in process capability significantly enhance data quality dimensions, including completeness, accuracy, consistency, and timeliness. This study contributes to public sector IT governance literature by providing an evidence-based COBIT 2019 capability assessment and proposing a structured improvement roadmap to achieve the defined target capability level and strengthen digital governance maturity.
Platform Pemesanan dan Pelacakan Pengiriman Beton Siap Pakai Berbasis Web dan Mobile dengan Pendekatan SDLC dan TAM M Rifqi Al Amin Sp; Aryanto, Joko
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.838

Abstract

This study aims to develop a web and mobile-based platform for ordering and delivering ready-mix concrete as a solution to delays and data errors frequently found in manual processes. The system is designed to simplify the ordering process for customers while enabling suppliers to manage deliveries and production schedules in real-time. The research applies the System Development Life Cycle (SDLC) framework and refers to the Technology Acceptance Model (TAM) to ensure that the system is user-friendly and meets user needs. Data were collected through business process observation and system requirements analysis, and the platform was implemented using modern web and mobile technologies. The results show that the developed system increases ordering efficiency by up to 40% and reduces delivery errors by 25% compared to manual methods. Additionally, the platform offers an interactive dashboard that enables both customers and suppliers to monitor order status accurately. Based on these findings, it can be concluded that the developed platform effectively supports the digital transformation of business processes in the ready-mix concrete industry, enhancing the service quality of construction companies.
Analisis Perbandingan Format Pesan ISO 8583 dan JSON pada Sistem Transaksi Keuangan Saputra, Suhanda
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.839

Abstract

The rapid development of financial transaction systems requires data exchange mechanisms that are fast, reliable, and easily integrable. ISO 8583 is a message format standard that has long been used in electronic payment systems such as ATMs, EDCs, and transaction switching networks, while JSON has been widely adopted in modern systems based on web services and microservices architectures. This study aims to analyze and compare ISO 8583 and JSON message formats in financial transaction systems in terms of data structure, message size efficiency, processing time, integration flexibility, and ease of system development and maintenance. The research method employs an experimental approach by simulating transaction transmission and processing using both message formats under identical transaction scenarios. The results indicate that ISO 8583 outperforms JSON in terms of message size efficiency and processing speed, making it more suitable for real-time transaction systems with high performance requirements. In contrast, JSON offers superior readability, flexibility, and ease of integration with modern systems, despite having higher data overhead. This study concludes that the selection of a message format should be aligned with system requirements, where ISO 8583 is more appropriate for core payment systems, while JSON is better suited for service integration and modern financial application development.
Integrasi Model Algoritma Genetika dan Constraint Satisfaction Problem pada Optimasi Penjadwalan Shift Karyawan UMKM Kuliner Syuhada, Arya Firgi; Mardhiyyah, Rodhiyah; Sanjaya, Fadil Indra
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.842

Abstract

Employee shift scheduling in the Micro, Small, and Medium Enterprises (MSMEs) sector is a complex problem because it must consider various aspects such as workforce availability, work hour restrictions, and individual preferences. At the Nasi Balap Cucun MSME which operates in the culinary field, the challenge is even greater because most of its employees are active students with diverse class schedules. The scheduling process is still done manually often takes a long time and results in an unbalanced division of labor. To overcome this, this study developed an automatic scheduling system based on Genetic Algorithms combined with Constraint Satisfaction Problems (CSP). The system was built using the Python programming language with the DEAP library, considering shift needs, employee schedule requests, and operational constraints. The implementation results show that the system is able to generate efficient weekly schedules with an increase in time efficiency of up to 80%. After testing the system, it was found that the scheduling results would appear less than 10 seconds after the user generated the schedule. In addition, the system showed an increase in fitness value from -1000 in the initial generation to 54 in the 50th generation, which means this system is able to reduce potential conflicts in scheduling. This approach can be an effective solution for MSMEs in optimizing human resource management intelligently.
Evaluasi Komparatif Algoritma Machine Learning untuk Prediksi Dini Diabetes Astofa, Aniq; Rosyani, Perani; Rahmawati, Rahmawati; Apandi, Sopiyan
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.859

Abstract

Diabetes is one of the non-communicable diseases that is often detected at an advanced stage, thereby increasing the risk of serious complications. The application of machine learning has the potential to support early diabetes detection; however, most previous studies have focused on large-scale datasets and high predictive accuracy, while methodological evaluations on small-sized clinical data remain limited. This study aims to evaluate and compare the performance of several machine learning algorithms for early diabetes prediction using a limited clinical dataset, with particular emphasis on analyzing the impact of data characteristics on model performance. The dataset used in this study consists of 22 samples with eight clinical features and one target variable, which were divided into 17 training samples and 5 testing samples. The research stages include data preprocessing, training–testing data splitting, model training, and performance evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The algorithms evaluated include Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. The experimental results indicate that none of the evaluated models were able to effectively detect the diabetes class, as reflected by precision, recall, and F1-score values of zero across all models. Although Random Forest and XGBoost achieved an accuracy of 0.6, this value was largely influenced by the dominance of the non-diabetes class in the very limited test set. Correlation analysis further reveals that Glucose, BMI, and Diabetes Pedigree Function are the most influential features associated with diabetes status. The main contribution of this study lies in providing a realistic methodological evaluation of machine learning models applied to small-sized clinical datasets, highlighting that limited sample size and training–testing data partitioning have a substantial impact on model performance and the interpretation of evaluation metrics. These findings provide an important methodological reference for future studies aiming to develop more reliable early diabetes prediction models under constrained clinical data conditions.
School Accreditation Prediction Based on Literacy and Numeracy: Ordinal Logistic Regression vs KNN Syukri, Nabila; Hiola, Yani Prihantini; Putri, Mega Ramatika; Susetyo, Budi
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.861

Abstract

School accreditation in Indonesia has traditionally relied on administrative inputs and institutional documentation, which often fail to capture the actual quality of student learning. In contrast, the National Assessment provides direct evidence of student literacy and numeracy outcomes, offering a more objective and outcome-based measure of educational quality. Leveraging these results as predictors for accreditation rankings is therefore crucial, as they reflect the competencies most relevant to effective learning delivery. This study aims to develop and evaluate classification models for school accreditation rankings using literacy and numeracy results as predictor variables. The dataset consists of secondary data from the 2023 and 2024 National School Assessments, covering 789 schools across four provinces: DKI Jakarta, Yogyakarta, Bali, and Banten. Two methods were applied, Ordinal Logistic Regression and K-Nearest Neighbors (K-NN) under two scenarios: with and without class imbalance handling. To address imbalance, two techniques were employed: Synthetic Minority Oversampling Technique (SMOTE) and Class Weight. The results indicate that K-NN consistently outperformed Ordinal Logistic Regression in both scenarios. On data without imbalance handling, K-NN achieved Accuracy, Precision, Recall, and F1-Score of 0.803, 0.705, 0.587, and 0.619, respectively. with imbalance treatment using SMOTE, the values were 0.753, 0.619, 0.686, and 0.644. While class balancing did not significantly improve overall accuracy, it enhanced the model’s ability to recognize minority classes. These findings highlight the strong relationship between literacy and numeracy outcomes and school accreditation status, demonstrating that outcome-based measures can complement traditional accreditation instruments, and that conventional statistical approaches are still relevant for modeling school accreditation.
Penerapan Algoritma Decision Tree Untuk Memprediksi Pengelolaan Inventaris Sarana Pembelajaran Kampus Martini, Martini; Nani Agustina; Entin Sutinah
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.889

Abstract

UBSI as an educational institution that has learning support facilities must be able to manage campus inventory effectively. This study aims to determine the management of asset management that needs to be done, both in the form of routine maintenance and updating of goods. The UBSI Jatiwaringin branch campus only makes reports on the condition of inventory items, so it cannot determine whether the reported inventory data is updated or repaired, so far it is not known which items are prioritized based on their level of importance. The data will then be followed up by the main campus to check the inventory data report. The method used to determine inventory predictions is the Decision Tree Algorithm which has priority, location, condition, frequency, and prediction attributes. As targets in the decision tree are prediction attributes that have maintenance or renewal classes. Determination of inventory data predictions by calculating the entropy, gain, gain info, and gain ratio values ??of each attribute and resulting in the Priority attribute being the root node in the formed decision tree. This indicates that the priority attribute has a strong influence in determining whether an item is included in the maintenance or renewal class. Based on testing results using RapidMiner software with the K-Fold Cross Validation method, the Decision Tree algorithm can generate a decision model with an average accuracy of 86.67% in campus inventory management. The results of this study are expected to be useful for Jatiwaringin Campus administrators to conduct initial inspections without waiting for repairs from the main campus.
Analisis Pengaruh BI Rate dan Kurs Terhadap Volatilitas Saham BCA Dengan GARCH-X Chaerunnisa, Rakhmadiani Ardinda; Setyoningrum, Ayatundira; Haq, Ichlasul Amal Al Ulil; Anantra, Piero Muharoja; Rani, Emeralita Wistyaka
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.898

Abstract

The Indonesian banking sector exhibits high sensitivity to macroeconomic dynamics, where market uncertainty is frequently triggered by external shocks and domestic policy adjustments. The main issue addressed in this study is the extent to which macroeconomic variables influence stock return volatility under fluctuating economic conditions. This research specifically examines the impact of the Bank Indonesia (BI) reference interest rate and the USD/IDR exchange rate on the return volatility of PT Bank Central Asia Tbk (BBCA) over the period from January 1, 2018, to October 31, 2025. The analysis employs a time-series econometric approach focusing on conditional volatility modeling using the Generalized Autoregressive Conditional Heteroskedasticity model with exogenous variables (GARCH-X), in which the BI Rate and exchange rate are incorporated into the variance equation. The estimation procedure is conducted using the R programming language and includes stationarity testing, detection of heteroskedasticity effects, and comparative evaluation of the sGARCH-X, eGARCH-X, and GJR-GARCH-X models. Model selection is based on the maximum log-likelihood value as well as the minimum Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The empirical results indicate that the eGARCH-X model provides the best fit, suggesting the presence of asymmetric behavior in BBCA stock volatility. Within this model, the BI Rate exhibits a negative and statistically significant effect on volatility, implying that monetary policy plays a stabilizing role in mitigating market risk. In contrast, the exchange rate tends to exert a positive influence on stock volatility, although its effect is marginal and not consistently significant at the 5% significance level. These findings highlight the importance of asymmetric GARCH modeling in capturing the response of banking stock volatility to monetary policy and exchange rate dynamics, and they provide relevant insights for investors in formulating effective risk management strategies.

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