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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Rekayasa Sistem Informasi dan Teknologi
Alif Hakim Al Faruq
Universitas Islam Sultan Agung
Computer Science & IT Decision Sciences, Operations Research & Management

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IMPLEMENTASI METODE LSTM DAN RNN UNTUK PREDIKSI CURAH HUJAN DI KABUPATEN BATANG Alif Hakim Al Faruq; Ahmad Tri Yulianto
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 3 No. 2 (2025): November
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jrsit.v3i2.3111

Abstract

Increased intensity of extreme rainfall due to climate change has made Batang Regency prone to hydrometeorological disasters. This study aims to develop an hourly rainfall prediction model using Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) based on historical data from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The model was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The results showed that LSTM had higher accuracy than RNN, with an MAE value of 0.0395 and an RMSE value of 0.0665. Meanwhile, RNN obtained an MAE value of 0.0439 and an RMSE of 0.0695. LSTM was also more stable in predicting temperature, wind direction, and wind speed variables. These findings indicate that LSTM is more effective for weather time series data and can be used as a basis for developing data-based early warning systems for disasters in local areas.
Car parking lot availability detection using the faster R-CNN method Andi Riansyah; Alif Hakim Al Faruq; Badieah Badieah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1038-1046

Abstract

Parking demand continues to rise as private vehicle use increases, making timely information about available spaces essential for efficient parking management. Many existing monitoring approaches still rely on fixed slot sensors or visual detectors that report accuracy without examining how confidence settings affect the final availability decision. This work investigates Faster region-based convolutional network (Faster R-CNN) with a ResNet-50 backbone for image-based parking availability detection using a public parking-lot dataset annotated in Pascal visual object classes (VOC) format. The experiment evaluates several confidence thresholds to determine how each setting changes the balance among accuracy, precision, recall, and F1-score. The most balanced setting was obtained at a threshold of 0.5, where the model achieved 95% accuracy and 97.3% for precision, recall, and F1-score. These results show that threshold configuration is an important factor in reducing missed detections and false alarms, although validation using real campus CCTV data and direct comparison with lightweight detectors remain necessary before practical deployment.
Co-Authors Ahmad Tri Yulianto Badieah Assegaf Riansyah, Andi