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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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Klasifikasi Hate Speech dan Offensive Language Menggunakan BERT dan Support Vector Machine Muhammad Tirta Syakban; Surya Agustian; Muhammad Fikry; Muhammad Affandes
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.v6i3.1061

Abstract

Hate speech and offensive language have become increasingly complex problems on social media, requiring classification approaches that can effectively capture linguistic context. While transformer-based models with end-to-end fine-tuning have become the dominant approach, the use of transformers as fixed feature extractors combined with classical machine learning algorithms remains relatively underexplored, particularly in benchmark settings such as HASOC 2021. This study aims to investigate the effectiveness of a feature-based transformer approach by combining embeddings from BERT and RoBERTa with Support Vector Machine (SVM) classifiers using multiple kernel configurations, including Linear, RBF, Polynomial, and LinearSVC. Experiments were conducted on Sub-task A and Sub-task B by comparing traditional feature-based methods (TF-IDF) with transformer-based embeddings. The experimental results show that RoBERTa embeddings consistently outperform other feature extraction methods. On the test dataset, the combination of RoBERTa and SVM achieves competitive performance compared to other systems in HASOC 2021. In Sub-task B, the optimal model achieves a Macro F1-score of 0.61, outperforming several BERT-based and classical baseline systems.These findings demonstrate that using transformer embeddings as fixed feature representations combined with optimized SVM classifiers can serve as an effective alternative to fine-tuning approaches, particularly in achieving more stable performance under class imbalance conditions. This study contributes by highlighting the potential of feature-based transformer methods as a flexible and competitive strategy for hate speech and offensive language detection.
Applying the Waterfall Method and Temporal Overlap Detection Algorithm in a Cross-Platform Badminton Court Booking Application Danis Wara Wardana; Joko Sutopo
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.v6i4.1062

Abstract

This study directly addresses the specific scheduling bottlenecks and operational inefficiencies at Furry Sport Center Seyegan, Sleman. The conventional booking workflow, dependent on manual physical logbooks and fragmented WhatsApp communications, suffers from severe limitations such as high vulnerability to double bookings and delayed staff responses. The digitalization of this management system is highly urgent to prevent ongoing operational disruptions and potential revenue loss caused by these manual errors. To resolve this, a cross-platform mobile booking application was developed utilizing the Waterfall software development model, engineered with a Flutter and Node.js architecture. The system incorporates a Temporal Overlap Detection Algorithm to systematically ensure conflict-free scheduling. To provide objective evidence of efficiency and eliminate subjective claims, system validation was conducted using the System Usability Scale (SUS). The testing yielded an overall average score of 79.25, placing the application in the acceptable usability category with high user satisfaction. These empirical parameters demonstrate that the application measurably resolves conventional scheduling conflicts and provides a proven, transparent solution for local sports venue management.
Kombinasi Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) dan Simple Additive Weighting (SAW) Pada Sistem Pendukung Keputusan Seleksi Magang Arya Fauzan Adima; Parjito Parjito
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.v6i3.1071

Abstract

The internship selection process at XZ University is still carried out conventionally, thus reducing time efficiency in decision making. The purpose of this study is to design a web-based Decision Support System (DSS) by applying a combination of Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) and Simple Additive Weighting (SAW) methods to increase the objectivity and efficiency of the selection process. The MOORA method functions to normalize and optimize alternative values as the basis for calculating SAW in internship selection, while the SAW method is used for normalizing and ranking alternatives based on weight criteria. This study uses a quantitative approach with a descriptive experimental method. Data were obtained through observation, interviews, and documentation. The criteria used include GPA, semester, collaboration time, video editing skills, camera operating skills, photography skills, videography skills, graphic design skills, creativity, attitude, discipline, responsibility. The results of data processing show that the five best alternatives with the highest preference values are A24 (100%), A3 (97%), A9 (96%), A5 (95%), dan A22 (93%.). These results show that the combination of the MOORA and SAW methods is able to provide the best alternative recommendations objectively and structured in the internship selection process.
Dynamic Interdependence Between Altcoin Dominance and Ethereum Price: A Temporal Pattern-Based Analysis Diva Ramadhani Ristiaji Putri; Rizky Parlika; Hendra Maulana
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.v6i3.1080

Abstract

The cryptocurrency market exhibits dynamic and time-varying relationships driven by shifts in market structure and investor behavior. This study investigates the dynamic interdependence between altcoin dominance and Ethereum price, addressing the limitations of static correlation analysis by applying a temporal pattern-based approach. Using 1,416 daily observations from 2022 to 2025, the data are segmented into monthly periods to capture time-varying relationships. The analysis combines correlation, trend, and volatility metrics with pattern classification to identify recurring relationship structures across different market conditions. The results reveal a moderate negative correlation (r = ?0.48) at the aggregate level. However, the monthly analysis shows that this relationship is not stable over time, but instead varies across different market regimes. The relationship is dominated by inverse patterns (40.43%), followed by weak (38.30%) and positive (21.28%) patterns. From an economic perspective, the negative relationship can be explained by capital rotation dynamics within the cryptocurrency market. When altcoin dominance increases, market liquidity tends to shift from major assets such as Ethereum to a broader set of alternative tokens, leading to downward pressure on Ethereum prices. Conversely, during certain bullish periods, capital inflows can simultaneously strengthen both altcoin dominance and Ethereum price, resulting in positive relationships. These findings demonstrate that the relationship between altcoin dominance and Ethereum price is dynamic and context-dependent. The study highlights the importance of temporal segmentation and pattern-based analysis in capturing complex market behavior that cannot be explained by a single aggregate correlation measure.
SIMPATA: Integrasi Administrasi dan Monitoring Progres Kegiatan Magang Berbasis Web Afif Rifai Nimal Abdu; Hanifah Permatasari; Agustina Srirahayu
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.v6i4.1085

Abstract

Internship management at the Statistics Indonesia Office of Sukoharjo Regency still relies on several separate and unintegrated media, covering document submission and verification, attendance recording, activity documentation, and participant task-progress monitoring. This condition causes data to be scattered, makes the recapitulation process less practical, and prevents participant activities from being monitored through a single system. This study aims to design and develop SIMPATA (Internship Participant Management and Governance Information System) as a web-based system that integrates internship administration and activity monitoring. The system was developed using an adapted Extreme Programming approach through the stages of planning, design, coding, and testing. Research data were collected through interviews, observations, and literature studies. The system was designed using the Unified Modeling Language, while its implementation employed PHP, the Laravel framework, and a MySQL database. The main contribution of SIMPATA lies in integrating registration, document verification, acceptance-status determination, participant and supervisor management, digital attendance, activity logbooks, task assignment, work submission, and progress monitoring into a single system. Black-box testing involving ten test scenarios showed that all major functions operated according to the specified functional requirements. Furthermore, User Acceptance Testing involving one administrator, three supervisors, and ten students obtained an overall score of 88.71%, which was classified in the Strongly Agree category. These results indicate that SIMPATA was well accepted by its users and can support more centralized and well-documented internship administration and activity monitoring.
Persistensi Artefak Telegram Web pada Memori Setelah Perubahan Sistem dengan Metode NIST SP 800-86 Sigit Puspito Wigati Jarot; Lukman Rosyidi; Haura Tsabitah
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.v6i3.1086

Abstract

The increasing incidence of cyberbullying on online communication platforms presents significant challenges for digital forensic investigations, particularly when perpetrators delete all message histories. Telegram Web, a browser-based messaging platform, produces volatile digital artifacts because its activity data is stored in system memory (RAM). This study aims to analyze the persistence of Telegram Web digital artifacts in volatile memory under six device condition variations using the NIST SP 800-86 framework, addressing a research gap in the quantitative evaluation of acquisition conditions for browser-based platforms. A cyberbullying simulation was conducted via Telegram private chat, generating 10 digital artifacts text messages, images, a document, and an audio file all subsequently deleted by the perpetrator. Memory acquisition was performed using Exterro FTK Imager under six conditions: immediately post-incident, sleep mode, hibernate mode, browser closed, browser closed with subsequent application use, and shutdown. Artifact identification employed keyword-based analysis on memory images. Results show that the first three conditions yielded 100% artifact recovery, as RAM preserved Chrome process data through DRAM self-refresh (ACPI S3) and byte-for-byte copying to hiberfil.sys (ACPI S4). Closing the browser reduced recovery to 40%, subsequent application use further reduced it to 10% due to zero-fill operations on reallocated memory pages, and shutdown produced 0% as all DRAM capacitor charges were lost. These findings demonstrate that artifact recovery rates are predictable from computer memory architecture, providing empirical guidance for digital forensic practitioners in web-based cybercrime cases.
Perbandingan Grid Search dan Random Search untuk Optimasi Hyperparameter Random Forest pada Klasifikasi Kanker Payudara Fiona Yenisya Dewi; Bayu Rizkya Pratama; Sunaryono Sunaryono
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.v6i4.1087

Abstract

Breast cancer is the most prevalent type of cancer in Indonesia, with 71% of patients diagnosed at advanced stages due to limited access to early detection. This condition necessitates the development of machine learning-based screening systems that are not only accurate but also computationally efficient to enable widespread implementation in healthcare facilities with limited resources, making the selection of an efficient hyperparameter optimization method crucial. This study compares two hyperparameter optimization methods, namely Grid Search  and Random Search, applied to the Random Forest algorithm using the UCI Wisconsin Diagnostic Breast Cancer Dataset (569 samples, 30 numerical features) with an identical search space encompassing 288 hyperparameter combinations and stratified 5-fold cross-validation. Experimental results demonstrate that Random Search RF achieves performance equivalent to Baseline RF on threshold-based metrics (accuracy 0.9737; F1-Score 0.9630) while producing the highest AUC-ROC of 0.9950 in 88,26 seconds. In contrast, Grid Search  RF yields performance below the baseline (accuracy 0.9561; F1-Score 0.9367) with a computation time of 526,73 seconds, attributable to the optimizer's curse phenomenon in which the selected combination based on cross-validation does not produce optimal generalization on the test data. Random Search is proven to be 5.97 times more efficient than Grid Search  with superior solution quality, empirically confirming the theoretical proposition that Random Search is capable of finding competitive configurations at substantially lower computational cost compared to exhaustive search in high-dimensional hyperparameter spaces.
Implementasi Local-First RAG dengan Hybrid Retrieval IndoBERT dan BM25 untuk Pendukung Keputusan Akademik Romi Wahyudi Hasibuan; Ahmad Rio Adriansyah; Henry Saptono
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.v6i4.1088

Abstract

The need for rapid and accurate access to academic information for educational institution stakeholders, such as campus management and academic advisors, still relies on manual administrative processes, thereby leading to operational inefficiencies. This study develops a locally hosted, open-source Retrieval-Augmented Generation (RAG) system as an automated solution for academic information services while alleviating administrative burdens. The system integrates the Mistral-7B-Instruct LLM with a hybrid search approach within the Elasticsearch ecosystem, combining IndoBERT-based dense retrieval for narrative academic guideline documents and BM25-based sparse retrieval for structured student data. Evaluation was conducted using ROUGE-1, ROUGE-2, and ROUGE-L metrics against 60 test data points generated by Claude Sonnet 4.6. The system successfully answered 58 out of 60 queries, achieving a ROUGE-L f1-score of 0.29. An asymmetrical pattern was observed, where recall values were consistently higher than precision across all metrics, which indicates the impact of the language generation capacity gap between Mistral 7B and the reference model. An average input prompt length ranging from 1,270 to 1,380 tokens contributed to an average latency of 30 seconds per query, representing the primary contemporary challenge of the system. This research is expected to serve as a baseline for developing open-source RAG systems within Indonesian language domains, specifically in the context of higher education academic administration.
Latency and Reliability Evaluation of an HTTP-Controlled ESP8266 Wi-Fi Robot Car Using MIT App Inventor Ryan Fikri; Agariadne Dwinggo Samala; Thamrin Thamrin; Delsina Faiza
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.v6i4.1091

Abstract

Low-cost Wi-Fi robot cars are frequently presented as functional prototypes, but many studies do not quantify whether motion commands remain reliable and responsive as wireless distance increases. This study therefore designed and experimentally evaluated an ESP8266-based differential-drive robot controlled by an Android application developed with MIT App Inventor. The proposed solution applies an HTTP command-mapping method in which each mobile-interface event is converted into a request, parsed by an ESP8266 web server, mapped to an L298N H-bridge state, and acknowledged after the control action is issued. The objective was to determine functional command accuracy and characterize the relationship among control distance, command-response latency, received signal strength indicator (RSSI), and communication reliability. Five commands forward, backward, left, right, and stop were tested in 150 functional trials, while 500 communication trials were conducted at 1, 5, 10, 15, and 20 m under indoor line-of-sight conditions. The robot correctly executed 148 of 150 functional commands, corresponding to 98.67% success. Distance-based reliability remained 100% at 1–5 m, decreased to 99% at 10 m and 97% at 15 m, and reached 92% at 20 m. Mean command-response latency increased from 88 to 248 ms while RSSI declined from ?38 to ?77 dBm. The main contribution is a reproducible, low-cost evaluation framework that links interface commands, HTTP communication, wireless quality, and physical motion execution. The results indicate that the platform is appropriate for responsive laboratory teleoperation within 15 m, while operation near 20 m requires stronger fail-safe and acknowledgement mechanisms.
Sistem Prediksi Prestasi Akademik Siswa Menggunakan Algoritma K-Nearest Neighbor (KNN) Febriansyah Febriansyah; Siti Muntari
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.v6i4.1092

Abstract

This study aims to classify student achievement levels based on academic ability in mastering subject matter using the K-Nearest Neighbor (K-NN) method. This study is motivated by the limitations of the student grade data processing system which is still done manually using Microsoft Excel, where the process of adding and grouping grades into low to high categories takes a long time and makes it difficult for teachers to identify student achievement levels, such as Good, Sufficient, and Poor categories. The data used consists of 134 students with 11 subject attributes as input variables in the classification process. The results show that from 134 student data, 90 students are classified into the Good category, 20 students are classified into the Sufficient category, and 24 students are classified into the Poor category. Testing using RapidMiner shows that the K-NN method obtains an accuracy level of 89.55%, which indicates that this method is effective in grouping student achievement levels. Performance evaluation is carried out using a Confusion Matrix to compare the classification results with actual data. Testing was conducted 10 times with a total of 134 student data obtained, the K-Nearest Neighbor (KNN) algorithm produced an accuracy of 89.55% and a kappa value of 0.763. These figures indicate that the model has excellent classification performance and a high level of reliability. The resulting model was then developed into a web-based prediction system that was tested using the expert system method through the Black-box Testing approach and obtained a feasibility level of 83.44%.

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