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All Journal KOMPUTIKA - Jurnal Sistem Komputer Jurnal Meteorologi dan Geofisika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Wiujianna, Atri
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Physics

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Prediksi Kabut Menggunakan Recurrent Neural Network dengan Attention Mechanism di Bandara Ruteng Wiujianna, Atri; Pribadi, Feddy Setio; Djuniadi, Djuniadi; Sunarno, Sunarno; Iqbal, Iqbal
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.15380

Abstract

Fog phenomena pose a significant challenge in aviation operations, particularly in regions with complex topography such as Ruteng Airport. Thick fog can reduce visibility and increase flight safety risks. This study aims to develop a deep learning-based fog prediction model by comparing the performance of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) enhanced with Attention Mechanism (AM). The dataset consists of 61,187 entries, covering hourly recorded weather parameters over the past ten years (2013–2023). The experimental results show that the addition of Attention significantly improves model performance. The RNN+Attention model emerges as the best-performing model with an accuracy of 0.9981, precision of 0.7755, recall of 0.76, and F1-score of 0.7677, along with the lowest number of False Positives. Meanwhile, the LSTM+Attention model excels in reducing False Negatives, making it suitable for systems prioritizing comprehensive fog detection. Models without Attention demonstrate perfect recall (1.00), but their low precision indicates overfitting. Overall, the integration of the Attention Mechanism enhances the balance between recall and precision and improves model reliability in handling data imbalance. The contribution of this research is that it can serve as a reference for future studies in developing artificial intelligence-based weather prediction models, particularly in addressing fog phenomena. Keywords – Attention Mechanism; Long Short-Term Memory; Fog Prediction; Recurrent Neural Network
Perbandingan Performa Model Long Short-Term Memory dan Bidirectional untuk Prediksi Kabut Wiujianna, Atri; Sunarno, Sunarno; Iqbal, Iqbal
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10588

Abstract

Fog is a weather phenomenon that can significantly reduce visibility and impact transportation safety as well as public activities. The Citeko region in Bogor, located in a highland area, experiences a relatively high frequency of fog events, especially during the morning and rainy seasons. This study aims to develop and compare the performance of fog prediction models using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) algorithms based on historical weather data from 2013 to 2023. The data, obtained from the Citeko Meteorological Station, includes weather parameters such as dry-bulb temperature, wet-bulb temperature, dew point, visibility, relative humidity, cloud cover, wind direction and speed, and hourly weather conditions. The data underwent several preprocessing steps, including missing value interpolation, fog classification based on weather parameters, normalization, and splitting into training and testing sets (80:20 ratio). The LSTM and BiLSTM models were then trained using a deep learning approach, both with and without early stopping. The results show that BiLSTM with early stopping achieved the best performance: 99.93% accuracy, 96.53% precision, 98.81% recall, and an F1-score of 97.66%, with only 9 false positives and 3 false negatives. This study contributes to the development of fog prediction systems based on artificial intelligence.
Simulation of Volcanic Ash Dispersion From Mount Ruang Using the Puff Model (April 29–30, 2024): English Hidayanti, Arifatul; Hardyanto, Wahyu; Munandar, Arif; Wiujianna, Atri; Meinofelia, Erika
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1082

Abstract

This study aims to simulate and predict volcanic ash dispersion from the 29 April 2024 eruption of Mount Ruang by coupling the PUFF model with RGB analysis of Himawari-8 imagery. Driven by meteorological fields from the NOAA Global Forecast System, the PUFF model provides 24-hour forecasts of ash transport by integrating Lagrangian and Eulerian representations of particle motion. For validation, Himawari-8 satellite imagery was processed using the RGB method with IR1, IR2, and IR4 channels to visually detect the spatial distribution of ash clouds, enabling effective differentiation between volcanic ash and meteorological clouds and improving detection accuracy. The model forecasts closely match the timing and distribution patterns observed in the satellite imagery, indicating strong agreement between numerical simulation and remote sensing analysis. Overall, the results demonstrate that the PUFF model delivers reliable short-term guidance on ash dispersion, and the integration of numerical modeling and satellite-based analysis confirms its effectiveness in supporting early-warning capabilities, aviation safety, and volcanic hazard risk mitigation.
Co-Authors Arif Munandar Djuniadi Djuniadi Feddy Setio Pribadi Hidayanti, Arifatul Iqbal Iqbal Meinofelia, Erika Sunarno Sunarno Wahyu Hardyanto