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Contact Name
Mesran
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mesran.skom.mkom@gmail.com
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+6282370070808
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INDONESIA
Bulletin of Computer Science Research
ISSN : -     EISSN : 27743659     DOI : -
Core Subject : Science,
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
Articles 462 Documents
Perancangan Infrastruktur Virtual Laboratory Multi-Layanan Berbasis Cloud Computing Menggunakan Metode NDLC Metalika Gunena; Anritsu Steven Christian Polii; Antonius P. G. Manginsela; Franky Manoppo; Olga Engelien Melo
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.v6i5.1253

Abstract

The limited hardware specifications of student devices remain a major obstacle in conducting computation-based practical courses in higher education, particularly in practicums that require applications with high computational resource demands, such as programming software, network simulation tools, and various other supporting applications, which often cannot run optimally on student devices with low specifications. This condition causes practical learning to become inconsistent and highly dependent on the capability of each student's personal device. Based on this problem, this research aims to design and simulate a cloud computing-based Virtual Laboratory system that can be accessed through a web browser without requiring high hardware specifications on the user side, while also contributing an integrated virtual laboratory architecture that combines three cloud computing service models simultaneously, namely Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). As a solution, IaaS is built using Proxmox Virtual Environment (VE) as a type-1 hypervisor, pfSense as a virtual firewall, Ubuntu Server as the service host, and Docker as the containerization platform, along with Cloudflare Tunnel using a Zero Trust architecture to ensure accessibility from outside the campus network without dependence on a static public IP. The system is equipped with multi-role authentication (student, lecturer, admin) based on Student ID Number (NIM) or National Lecturer ID Number (NIDN) verification and OTP verification, a QR code-based Attendance module integrated with the Timetable module, and a main portal built as an installable Progressive Web App (PWA) on user devices. The system was developed using the Network Development Life Cycle (NDLC) method. Preliminary functional testing results show that all services in the Programming Class and Network Class, including the Attendance and Timetable modules, were successfully accessed from outside the campus network, and the multi-role authentication system functioned as designed.
Prediksi Time Series Permukaan Air Sungai Menggunakan Long Short-Term Memory-Autoregressive Integrated Moving Average Norita Febriyanti; Michael Ardita; I Komang Somawirata
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.v6i5.1262

Abstract

River water level prediction is an essential component of flood early warning systems, as it provides information on potential water level increases before overflow occurs. The Kebonagung River in Sumenep Regency was selected as the study area due to its fluctuating water levels, which are influenced by variations in river discharge and water volume, thereby requiring a prediction model capable of capturing both linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) model is effective in modeling linear and seasonal time-series patterns, whereas the Long Short-Term Memory (LSTM) model is capable of learning nonlinear relationships and long-term temporal dependencies with high accuracy. This study aims to compare three prediction approaches: Long Short-Term Memory (LSTM), ARIMA-based discharge integrated with LSTM, and the Hybrid Autoregressive Integrated Moving Average–Long Short-Term Memory (ARIMA–LSTM) model for predicting river water levels using one year of historical data collected at one-hour intervals, consisting of water level, discharge, and water volume variables. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results show that the LSTM model achieved an RMSE of 128.15, an MAE of 96.61, and a MAPE of 3.56%, while the ARIMA (discharge)-LSTM model obtained an RMSE of 127.63, an MAE of 96.33, and a MAPE of 3.55%. The Hybrid ARIMA–LSTM model produced the lowest RMSE of 127.06, although its MAE and MAPE were slightly higher at 96.81 and 3.59%, respectively. These findings indicate that integrating ARIMA and LSTM can reduce large prediction errors, resulting in a lower RMSE; however, the performance improvement over the other models is not substantial because all models exhibit relatively similar error levels. Therefore, the Hybrid ARIMA–LSTM model is better regarded as an approach that improves prediction stability rather than one that delivers a significant increase in predictive accuracy. The main contribution of this study lies in the evaluation of two ARIMA–LSTM integration strategies, namely the use of ARIMA-based discharge predictions as input features for the LSTM model and a residual-based Hybrid ARIMA–LSTM approach, using the same hydrological dataset to enable an objective comparison of the characteristics, strengths, and limitations of each approach. The findings are expected to provide a useful reference for the development of river water level prediction models to support time-series-based flood early warning systems.