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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
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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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Perancangan Keamanan Informasi pada Sistem Persuratan Berbasis Web Menggunakan Autentikasi dan Middleware Panggah Widiandana; Muhammad Hafidz Amali; Adhitya Admaja; Maulana Raka Saputra
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.v6i4.1178

Abstract

The management of digital correspondence through web-based systems has improved administrative efficiency, but on the other hand, it introduces new vulnerabilities to cyber threats. Information security is a crucial aspect in the development of web-based mailing systems because this system manages incoming mail, outgoing mail, and official documents that are highly sensitive. Without a good security mechanism, the system has the potential to experience unauthorized access, data manipulation, and information leaks. Therefore, this study aims to design an information security mechanism through the application of authentication (auth) and middleware as system access controllers. The research method used is a system design method that includes security requirement analysis, Access Control Matrix modeling, routing interceptor architecture design, and Black-box authorization testing. In its application, authentication is used to ensure user identity before accessing the system, while middleware serves as a security layer to restrict access based on user roles. The design results show that the application of auth and middleware can improve access control, maintain data confidentiality, and strengthen information security in web-based mailing systems. The main contribution of this research is the provision of an integrated Role-Based Access Control (RBAC) layered information security model that can be adopted by various institutions to prevent privilege escalation and data manipulation in internal administration systems.
Optimasi Pemilihan Jenis Kayu Berbasis Multi-Kriteria untuk Mendukung Keputusan pada Industri Furnitur Menggunakan Metode Simple Additive Weighting Iwan Giri Waluyo; Savitri Savitri; Wiwit Kurniawan
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.v6i4.1181

Abstract

Selecting the right type of wood is a crucial factor in the furniture industry, as it directly impacts product quality, cost efficiency, and market value. Relying on subjective selection processes can lead to inconsistent decision-making. This study aims to develop a decision support system to optimize wood selection for Sumber Rejeki Mebel using the Simple Additive Weighting (SAW) method. The wood types evaluated include Teak, Merbau, Kamper Samarinda, Meranti, and Borneo, based on four criteria: color, texture, price, and availability. Criterion weights were determined through subjective assessment by an expert—the furniture business owner, who possesses extensive experience in raw material selection—and were subsequently applied during the normalization and ranking stages of the SAW method. Data analysis results indicate that Kamper Samarinda achieved the highest preference score (0.668), followed by Teak (0.653), Merbau (0.615), Meranti (0.610), and Borneo (0.605). Implemented using PHP and MySQL, the system facilitates a faster, more consistent, and transparent evaluation process compared to manual methods. Theoretically, this research demonstrates that the SAW method effectively integrates various material quality criteria into a simple, easily implementable multi-criteria decision-making model. Practically, the developed system supports more objective raw material selection, thereby offering the potential to enhance product quality and operational efficiency within the furniture industry.
Analisis Perbandingan Kinerja Cloud Amazon Web Services dan Google Cloud Platform untuk Learning Management System Menggunakan Metode Analytical Hierarchy Process Reza Maulana; Faralita Faisal; Salman Fathy Shiroth; Hany Hidianti
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.v6i4.1210

Abstract

The digital transformation of education demands scalable and reliable LMS infrastructure, yet institutions often struggle to choose the right cloud platform because decisions rely only on catalog prices or features without empirical performance evidence. This study compares the performance of Amazon Web Services and Google Cloud Platform at the Infrastructure as a Service layer through five stages: provisioning two equivalent virtual machines, deploying Moodle with its database and monitoring stack via Docker containerization, executing JMeter load tests at 100, 250, and 500 concurrent users, collecting performance metrics, and evaluating them with the Analytical Hierarchy Process across four criteria of performance, reliability, cost, and integration. GCP excels in error containment (as low as 0.12%) and OS-level stability and is 27.7% cheaper, while AWS leads in throughput up to 11.7 requests per second with consistent maximum response times. AHP yields scores of 0.768 for AWS and 0.762 for GCP with a consistency ratio of 0.041. The contribution of this research is the first integrated evaluation framework combining empirical JMeter load testing on LMS workloads with multi-criteria AHP decision making, together with a portable Docker-based testing architecture replicable on both platforms.
Improved Convulational Neural Network dengan Transfer Learning dan Hyperparameter Tuning untuk peningkatan akurasi klasifikasi Citra Kanker Kulit Ega Wahyu Andani; Solikhun Solikhun; Timbo Faritcan P. Siallagan
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.v6i4.1217

Abstract

Skin cancer is one of the diseases that requires early detection to increase the likelihood of successful treatment. The use of artificial intelligence, particularly Deep Learning, has become an effective alternative in assisting the automatic classification of skin cancer images. However, the high class imbalance and visual similarity between lesion types in skin cancer datasets remain challenges in achieving optimal classification performance. This study aims to improve the accuracy of skin cancer image classification using an Improved Convolutional Neural Network based on Transfer Learning and Hyperparameter Tuning. The dataset used is HAM10000, consisting of 10,015 dermoscopy images across seven diagnostic classes. The architecture employed is MobileNetV2 as a feature extractor combined with a custom classification head. The training process was carried out using a two-phase transfer learning strategy, namely the backbone freezing phase and the fine-tuning phase. To address class imbalance, class weighting and data augmentation were applied, while model optimization was performed using grid search over the parameters of learning rate, dense layer size, and dropout rate. Model performance was evaluated using accuracy, precision, recall, F1-score, and Area Under Curve (AUC) metrics. The results show that the proposed model achieved a test accuracy of 85.50%, a validation accuracy of 84.75%, a macro F1-score of 83.14%, and a mean AUC of 0.94. These results indicate that the combination of two-phase Transfer Learning and Hyperparameter Tuning is capable of improving the performance of MobileNetV2 in skin cancer image classification. The contribution of this research is the development of a classification model that achieves high accuracy, is computationally efficient, and is capable of handling class imbalance in the HAM10000 dataset.
Analisis Efektivitas IndoBERT untuk Klasifikasi Multilabel Terjemahan Hadis Bukhari Menggunakan Logistic Regression Achmad Yamin Harahap; Nazruddin Safaat H; Surya Agustian; Suwanto Sanjaya; Teddie D
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.v6i4.1219

Abstract

Hadith serves as the second source of guidance after the Quran, directing Muslims in various aspects of life; the *Sahih al-Bukhari* collection is among the most renowned. The complex nature of their meanings often encompassing multiple categories of messages poses a significant challenge for manual text classification, particularly as data volume grows. In this study, the content of the hadith often includes multiple message types, such as recommendations, prohibitions, and general information. This research aims to evaluate an automated classification system for Indonesian translations of *Sahih al-Bukhari* hadith, categorizing them into three classes: Information, Recommendation, and Prohibition. The study is motivated by the vast number of hadith, which requires significant time and deep understanding for people to grasp the core message of each one. This classification system is intended to facilitate the identification of primary messages, thereby making the processes of searching, studying, and understanding hadith more effective and efficient. IndoBERT is employed to generate contextual vector representations capable of capturing deeper semantic meaning, while Logistic Regression is selected for its efficiency and stability with high-dimensional data. Evaluation is conducted using a train-validation-test split approach, alongside accuracy and macro F1-score metrics. The study achieved an average F1-score of 67.43%, demonstrating that the combination of IndoBERT and Logistic Regression yields strong, consistent classification performance for this multi-label task.
Implementasi dan Optimasi Sistem Monitoring Kualitas Udara Berbasis Sensor Gas MQ-135 dan SHT21 dengan Metode IoT Theopilus S.P Sibarani; Eko Setia Budi; Abdul Rahman Kadafi
Bulletin of Computer Science Research Vol. 6 No. 5 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i5.1093

Abstract

The decline in air quality caused by industrialization and urbanization requires an automated, real-time, and affordable monitoring method, since manual measurement by environmental health officers is still limited to periodic site visits. This study aims to implement and optimize an air quality monitoring system based on the Internet of Things (IoT) using the MQ-135 gas sensor and the SHT21 temperature-humidity sensor, targeting improved gas-sensor calibration accuracy through linear regression and faster, more reliable data transmission to a MySQL database. The system is designed to detect environmental parameters such as hazardous gas concentrations, temperature, and humidity, which are then transmitted in real-time to a web-based platform for visualization and data analysis. The implementation process involves integrating hardware, including the Arduino Uno and NodeMCU ESP8266 microcontrollers with the MQ-135 and SHT21 sensors, as well as developing software that enables information processing and IoT communication. Testing was conducted to evaluate sensor accuracy, data transmission stability, and system reliability under varying environmental conditions. The results demonstrate that the system can provide accurate information and respond effectively to changes in environmental parameters, sending notifications when gas concentrations exceed predefined thresholds. This system not only serves as a monitoring tool but also as an educational medium to raise awareness of the importance of maintaining air quality. The main contribution of this research is a self-hosted air quality monitoring architecture that combines a quantitatively validated MQ-135 gas-sensor calibration method with an integrated web- and Telegram-based early-warning system. Further development opportunities, such as incorporating predictive analytics and AI for more accurate air quality data analysis, emerge as outcomes of this research.
Implementasi Algoritma Prophet dengan Grid Search Hyperparameter Tuning untuk Prediksi Konsumsi Energi Listrik Berbasis IoT Suhardi Suhardi; Tedy Rismawan; Cucu Suhery; Irma Nirmala
Bulletin of Computer Science Research Vol. 6 No. 5 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i5.1182

Abstract

?Real-time monitoring of household electricity consumption is not yet sufficient to support adaptive energy management. Therefore, an accurate yet easily interpretable prediction capability is required. This study implements the Prophet algorithm, an additive time series model based on trend and seasonal components, as the core method for predicting daily energy consumption in an Internet of Things (IoT)-based system with per-room granularity. Data were obtained from PZEM-004T sensors and NodeMCU ESP32 modules in three rooms over 30 days, processed through a two-stage grid search procedure for model hyperparameter optimization. The evaluation results show a testing MAPE of 1.05%–2.01% across the three rooms, all falling into the highly accurate category (<10%). Furthermore, the average MAPE difference between the training and testing data reached only 0.42 percentage points, indicating good model generalization without overfitting. Component decomposition analysis reveals that the consumption pattern is dominated by a stable linear trend with a low-amplitude weekly seasonal pattern (±0.06 kWh), thereby providing a higher level of interpretability compared to black-box models. The 30-day-ahead projection yields a total estimated consumption of approximately 384 kWh (~IDR 554,817) for the three rooms, which can be utilized as a basis for budget planning and adaptive electrical load management. The main contribution of this study is a transparent and reproducible Prophet tuning procedure for per-room electricity consumption data with limited historical volume, supported by metrological validation of the acquisition sensor as an input quality assurance step, a context that has not been widely explored in prior Prophet literature
Pola Perilaku Pemain Roblox: Pemodelan Klasifikasi Berbasis Naïve Bayes Risqi Nur Avianti; Cucut Hariz Pratomo
Bulletin of Computer Science Research Vol. 6 No. 5 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i5.1188

Abstract

The development of digital technology has driven the growth of online gaming as a medium for entertainment, social interaction, and creativity development. One platform that has grown rapidly is Roblox, which allows users to play, interact, and create digital content. This diversity of activities causes player behavior characteristics to become increasingly complex, making them difficult to identify manually. Therefore, a machine learning-based approach is needed to classify player behavior more objectively and systematicallys. This study aims to classify Roblox player behavior into four categories, namely active, casual, social, and creative players, using the Naïve Bayes algorithm. This algorithm was chosen because it has a simple and efficient computational process and is suitable for questionnaire data that has been transformed into numerical form. A total of 523 responses were successfully collected, and after the selection and preprocessing stages, 520 data points were obtained to be used as the research dataset. The data were processed through data cleaning, encoding, missing value handling, and dataset splitting using an 80% training data and 20% test data. The results showed that the model achieved an accuracy of 62.5%. Evaluation using precision, recall, and F1-score metrics revealed that The results showed that the model produced an accuracy of 62.5%, with a precision value of 63%, recall of 62%, and F1-score of 62%. Although the accuracy obtained is not yet high, these results indicate that the Naïve Bayes algorithm can be used as a baseline in classifying player behavior based on questionnaire data that has subjective and complex characteristics. The his study contributes by providing a baseline classification model for Roblox player behavior based on questionnaire data, along with insights into player characteristics that can serve as a reference for developers in understanding user behavior, thereby supporting the development of more adaptive features that better align with players' needs.
Evaluasi Kinerja U-Net ResNet34 dan MDSBN: Studi Komparatif untuk Segmentasi Naskah Kuno Indonesia Rino Zakharia; Budi Nugroho; Eka Prakarsa Mandyartha
Bulletin of Computer Science Research Vol. 6 No. 5 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i5.1236

Abstract

The digitization of ancient documents is an important step in preserving historical and cultural information. However, the resulting images often suffer from degradation, such as stains, uneven background textures, faded ink, and low contrast, making text-background separation difficult. This study compares two deep learning architectures, namely U-Net ResNet34 and the Modified Deep Semantic Binarization Network (MDSBN), for the segmentation of Indonesian ancient documents. The dataset consists of Balinese palm-leaf manuscripts, Sundanese manuscripts, and additional ancient document images obtained from Wikimedia Commons. The experiments were conducted through a learning rate search and batch size sensitivity analysis, and the models were evaluated using the Dice Coefficient, Intersection over Union (IoU), Precision, Recall, and Root Mean Squared Error (RMSE). This study contributes through a controlled evaluation of both architectures using a consistent dataset, preprocessing pipeline, loss function, evaluation metrics, and computational environment, enabling performance differences to be analyzed more objectively. The results show that U-Net ResNet34 achieved its best performance using a learning rate of 5e-5 and a batch size of 16, with a test Dice score of 0.79338 and a test IoU score of 0.65752. It outperformed MDSBN, which achieved its best performance using a learning rate of 1e-6 and a batch size of 32, with a test Dice score of 0.75338 and a test IoU score of 0.60433. The functional advantage of U-Net ResNet34 is associated with the ability of its residual encoder to extract hierarchical features from complex textures and degradation patterns, while its skip connections help preserve the spatial details of thin text strokes. These characteristics make U-Net ResNet34 more adaptive to variations in degradation within the Indonesian ancient document dataset than the more compact MDSBN architecture.
Analisis Prediksi Rasio Elektrifikasi Rumah Tangga Indonesia Menggunakan Algoritma Prophet Pendekatan Logistic Growth Lifio Syifa Kurniawan; Bernadus Very Christioko
Bulletin of Computer Science Research Vol. 6 No. 5 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i5.1237

Abstract

The Indonesian government continues striving for 100% household electricity access, yet regional disparities remain significant, particularly in eastern regions and New Autonomous Regions. Methodologically, forecasting the electrification ratio faces the challenge of data scarcity and the need to keep predictions below the absolute 100% ceiling. Conventional models and standard non-linear approaches such as penalized Logistic Regression have limitations in handling very small univariate time series and often fail to capture trends without a dynamic saturation point. This study therefore proposes the Prophet algorithm with a Logistic Growth approach to forecast the electrification ratio across 38 provinces for the 2026–2030 period. Prophet was selected for its robustness to minimal historical data and missing values, while Logistic Growth sets a logical maximum capacity (cap = 100.5%) so that predictions do not exceed the 100% asymptotic limit. The evaluation results show the model performs with precision in regions with mature historical data, evidenced by a MAPE of 0.41% and RMSE of 0.62 in DKI Jakarta. Conversely, predictions for DOB provinces such as Central Papua show high uncertainty, with errors reaching 34.30% due to inadequate initial data ranges. Projections through 2030 confirm that all provinces on Java remain stable at a 100.00% ratio, while an anomaly is detected in Southwest Papua, which is projected to decline sharply to 34.65%. The main contribution of this study is the first Prophet-Logistic Growth forecasting framework applied to 38 Indonesian provinces. This approach offers a mathematically stable forecasting framework as a basis for government decision-making on energy infrastructure allocation, particularly when combined with field-data verification in data-scarce regions.

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