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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research
Agustinus Rudatyo Himamunanto
Universitas Kristen Immanuel, Yogyakarta
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Perbandingan Sensitivitas Serta Stabilitas MQ-4 dan MQ-5 Berbasis Arduino Untuk Deteksi Kebocoran Gas LPG Yaaman Nazara; Gogor Christmass Setyawan; Agustinus Rudatyo Himamunanto
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9992

Abstract

LPG gas leakage is a serious household safety threat in Indonesia, where 88.59% of households use LPG as their primary cooking fuel. Although colorless and faintly odorous at low concentrations, its flammable nature can cause explosions or fires if undetected. This research aims to compare MQ-4 and MQ-5 sensor performance in a single Arduino Uno-based gas leakage detection system. A quantitative experiment with direct comparison was conducted under identical conditions inside a sealed acrylic chamber measuring 16 × 12 × 10 cm, using LPG gas from a refillable lighter to simulate household leakage. Parameters measured include sensitivity, defined as the slope of the log(Rs/R₀) versus log(relative ppm) curve, and reading stability from the standard deviation of repeated measurements, where Rs is sensor resistance when exposed to gas and R₀ is resistance in clean air. All ppm values are relative estimates based on official sensor datasheets. Results show MQ-5 has higher sensitivity with a slope of 0.7914 compared to MQ-4 (0.6505), while MQ-4 is more stable with a log₁₀(Rs/R₀) standard deviation of 0.656 compared to MQ-5 (0.669). The system successfully detected all 30 leakage scenarios (100%) without false alarms, with a classification success rate of 99.0% per session. This research recommends MQ-5 for rapid early detection and MQ-4 for long-term monitoring requiring data consistency, thus both contribute as empirical evidence for a simple, accurate, and economical gas leakage detection solution.
Perancangan Sistem Early Warning Kebakaran F-GUARD Menggunakan Decision-Level Sensor Fusion Berbasis Mikrokontroler ESP8266 Kearifan Yopimar; Gogor Christmass Setyawan; Agustinus Rudatyo Himamunanto
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10650

Abstract

Domestic fire is a fatal disaster threat that requires a responsive and reliable early detection system to minimize material losses. This research focuses on the design of the F-GUARD fire early warning system which uses a decision-level sensor fusion approach based on the NodeMCU ESP8266 microcontroller. This system integrates the MLX90614 contactless temperature sensor for precision thermal radiation detection, the MQ-2 smoke sensor, and the Infrared Flame Sensor to detect the presence of fire directly. The novelty of this research lies in the implementation of a single exponential smoothing algorithm to dampen noise in temperature data and a hold time mechanism for 15 seconds on the smoke sensor to eliminate chattering and false alarms. Environmental data is processed in real-time and visualized on an OLED screen, with buzzer actuator management based on non-blocking execution. The system transmits telemetry data to a cloud database every 5 seconds and sends instant emergency notifications via Telegram Bot. The main contribution of this research is the integration of the single exponential smoothing algorithm and hold time mechanism which successfully increased fire detection accuracy to 95% and reduced false alarms by up to 80% compared to conventional systems. Test results show that this multi-sensor integration with digital filtering logic is able to detect fire with an accuracy level of 95% and the average system response speed from detection to notification delivery is recorded at 2.5 seconds. Thus, F-GUARD is proven effective in providing more reliable, stable, and adaptive fire mitigation management for modern household environment protection needs.
Analisis Kinerja Random Forest Dalam Deteksi Gejala Alergi Rongga Mulut Berbasis Warna Gusi Juli Hartati Gea; Agustinus Rudatyo Himamunanto; Haeny Budiati
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.657

Abstract

Early detection of allergies in the oral cavity remains challenging due to the subjective nature of visual assessment and limited access to diagnostic facilities. This study proposes a novel approach using the Random Forest algorithm to classify the severity of allergic symptoms based on gum color analysis from digital images. A total of 2,742 gum images were clinically categorized using the Modified Gingival Index (MGI) into mild, moderate, and severe conditions. Preprocessing included conversion to HSV color space and adaptive segmentation using red thresholds on the hue channel (0–10 and 160–180), saturation > 50, and value > 40. Statistical features, including mean, standard deviation, skewness, kurtosis, and entropy, were extracted and normalized using Z-Score. Six parameter combinations were tested with an 80:20 train-test split. The optimal configuration with n_estimators=80, max_depth=9, and min_samples_leaf=2 achieved an accuracy of 95.81%. The highest performance was achieved in the mild class with precision and recall of 98.91%, and stable results in the moderate (93.80%) and severe (94.74%) classes, with only a 0.94% difference. Cross-validation evaluation demonstrated excellent model stability, with an average accuracy of 95.30% and a standard deviation of 0.67%, indicating consistent performance across data subsets. Feature importance analysis showed the dominance of the hue and saturation channels, particularly kurtosis and mean saturation. This study demonstrates that a Random Forest-based allergy detection system using gum color is highly accurate and effective as a non-invasive screening tool in dental and oral health, especially in resource-limited settings, with the potential to improve early screening access in primary healthcare facilities.
Deteksi Penyakit Tanaman Padi (Oryza Sativa L.) Menggunakan Support Vector Machine (SVM) Dan Random Forest Pada Citra Daun Bintang Karmila Gulo; Agustinus Rudatyo Himamunanto; Jatmika Jatmika
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.660

Abstract

Rice (Oryza sativa L.) is a major food crop that is susceptible to disease attacks, which can reduce farmers' productivity and yields. This study aims to develop a digital image-based rice leaf disease classification system using the Support Vector Machine (SVM) and Random Forest algorithms. The dataset consists of three disease classes (Blast, Blight, and Tungro), which are processed through pre-processing stages such as resizing, normalization, and augmentation. Feature extraction is performed using HSV histograms, RGB average values, and Gray Level Co-occurrence Matrix (GLCM) to obtain color and texture characteristics. The data is then divided with a ratio of 80:20 for model training and testing. The evaluation results show that Random Forest provides the best performance with an accuracy of 97.73%, precision and recall values ??above 0.94, and an average F1 score of 0.98. This study shows that a machine learning-based image classification approach can be an effective solution for early detection of diseases in rice plants.
Co-Authors Bintang Karmila Gulo Gogor Christmass Setyawan Haeny Budiati Jatmika Jatmika Juli Hartati Gea Kearifan Yopimar Yaaman Nazara