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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika INTECOMS: Journal of Information Technology and Computer Science International Journal Software Engineering and Computer Science (IJSECS) Abdi Jurnal Publikasi CSRID Journal of Informatics and Vocational Education
Arya Adhyaksa Waskita
Universitas Pamulang
Computer Science & IT Education Social Sciences Other

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Journal : csrid

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Classification of Low-Level Wind Shear on Imbalanced Radiosonde Data Using a Hybrid CNN-LSTM Architecture Sarnubih Hasan; Agung Budi Susanto; Arya Adhyaksa Waskita
CSRID (Computer Science Research and Its Development Journal) Vol 18 No 3 (2026): Oktober 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.3.2026.426-438

Abstract

Low-Level Wind Shear (LLWS) during the takeoff and landing phases poses a severe and dynamic threat to aviation safety. However, it remains inherently challenging to detect directly from surface observations due to its highly non-linear, transient, and sequential nature in the lower atmosphere. This study proposes an advanced deep learning approach to classify LLWS events based on high-resolution vertical radiosonde profile data spanning the 2024–2025 period at Soekarno-Hatta International Airport, Indonesia. Addressing the critical challenge of a highly imbalanced dataset—where hazardous meteorological events are exceptionally rare compared to normal atmospheric conditions—the classification target was simplified into an event-level binary scheme. This scheme utilizes a hazard intensity threshold of ≥ 4 knots/100 ft, adhering strictly to the International Civil Aviation Organization (ICAO) moderate-to-severe hazard categories. The comprehensive research methodology involved fixed-grid interpolation into 21 uniform vertical observation points (spanning 0 to 2000 ft), rigorous wind vector transformation into zonal and meridional components, and circular encoding for wind direction to maintain absolute numerical stability during model training. A hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) architecture was developed to capture both local spatial gradients and long-term vertical dependencies, which was then systematically compared against a Transformer baseline equipped with a global self-attention mechanism. The empirical results and operational discussion demonstrate that the proposed CNN-LSTM model significantly outperformed the Transformer baseline, achieving an outstanding Area Under the Precision-Recall Curve (PR-AUC) of 0.9672 compared to the Transformer's 0.7533. Furthermore, the confusion matrix analysis conclusively proved the superior capability of the CNN-LSTM network in minimizing false negatives (missed detections) from 17 down to merely 7 critical events. In conclusion, the integration of local feature extraction through CNN and sequential dependency modeling via LSTM is highly effective for addressing imbalanced meteorological datasets, providing a highly robust and physically consistent computational foundation for automated aviation safety early warning systems.
Perfomance Comparison of LSTM and Transformer Model for Rainfall Data Imputation on Automatic Weather Station Tri Nurmayati; Agung Budi Susanto; Arya Adhyaksa Waskita
CSRID (Computer Science Research and Its Development Journal) Vol 18 No 3 (2026): Oktober 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.3.2026.491-501

Abstract

High-resolution rainfall observations from Automatic Weather Stations (AWS) are essential for hydrometeorological analysis, yet operational sensor and communication failures frequently create missing values. This study compared Long Short-Term Memory (LSTM) and Transformer architectures for imputing missing 10-minute rainfall observations recorded at the Banten Climatological Station AWS in South Tangerang during 2021-2025. Rainfall was modeled using a sliding window containing rainfall at the previous three intervals together with concurrent air temperature and relative humidity. The chronologically ordered dataset comprised 242,497 valid samples and was divided into training, validation, and testing subsets using a 60:20:20 ratio. Each architecture was trained with the Adam optimizer for up to 100 epochs under batch sizes of 32, 16, and 8, with early stopping used to control overfitting. Performance was assessed using root mean square error, mean absolute error, and the coefficient of determination. The best LSTM configuration used a batch size of 8 and achieved an RMSE of 1.1520, an MAE of 0.5140, and an R² of 0.6626 on the test set. The Transformer with a batch size of 8 obtained an RMSE of 0.8287, an MAE of 0.3635, and an R² of 0.8244. These results demonstrate that self-attention represented short-range multivariate dependencies more effectively than recurrent memory under the imbalanced rainfall distribution Keywords :automatic weather station; imputation; rainfall; LSTM; tansformer..
Co-Authors Abdul Haris Achmad Hindasyah Agung Budi Susanto Agung Budi Sutanto Agung Sutopo Choirul Basir Diah Ariefianty Ferdiansyah Winardi Ferhat Aziz Ferry Setefanus Indriyani Astuti Iwan Muttaqin Muhammad Al-Farisy Putu Dedi Juliana Sahrul Ramadhan Sapta Nugraha Sarnubih Hasan Syarif Hidayatullah Tri Nurmayati