Bulletin of Computer Science Research

Implementasi dan Optimasi Sistem Monitoring Kualitas Udara Berbasis Sensor Gas MQ-135 dan SHT21 dengan Metode IoT

2026-05-29T23:27:20+00:00

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.

Evaluasi Kinerja U-Net ResNet34 dan MDSBN: Studi Komparatif untuk Segmentasi Naskah Kuno Indonesia

2026-07-01T02:44:54+00:00

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.

Implementasi Algoritma Prophet dengan Grid Search Hyperparameter Tuning untuk Prediksi Konsumsi Energi Listrik Berbasis IoT

2026-07-01T14:32:26+00:00

?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

Perancangan Infrastruktur Virtual Laboratory Multi-Layanan Berbasis Cloud Computing Menggunakan Metode NDLC

2026-07-10T04:07:19+00:00

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.

Pola Perilaku Pemain Roblox: Pemodelan Klasifikasi Berbasis Naïve Bayes

2026-07-13T16:46:49+00:00

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.

Analisis Prediksi Rasio Elektrifikasi Rumah Tangga Indonesia Menggunakan Algoritma Prophet Pendekatan Logistic Growth

2026-07-01T02:39:54+00:00

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.

Prediksi Time Series Permukaan Air Sungai Menggunakan Long Short-Term Memory-Autoregressive Integrated Moving Average

2026-07-13T16:41:04+00:00

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.