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All Journal Jupiter Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informasi dan Komputer Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik Journal of Computer System and Informatics (JoSYC) Journal of Computer Networks, Architecture and High Performance Computing Soeropati: Journal of Community Service Journal of Applied Data Sciences TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Jurnal Publika Pengabdian Masyarakat Jurnal Ilmu Siber dan Teknologi Digital Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) IJISCS (International Journal of Information System and Computer Science)
Triloka, Joko
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Journal : Jurnal Informatika

 1 
Evaluasi Kinerja Model Deep Learning dalam Memprediksi Kejadian Hujan Di Wilayah Panjang Bandar Lampung Tarjono; Triloka, Joko; Mutiara, Suci
Jurnal Informatika Vol 25 No 1 (2025): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jurnalinformatika.v25i1

Abstract

Global warming and climate change have increased the frequency and intensity of extreme weather events, significantly impacting human life and the environment. Urban areas such as Kecamatan Panjang in Bandar Lampung City frequently experience flooding due to extreme rainfall and poor drainage systems. This study compares the effectiveness of three deep learning model architectures- Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformers — in predicting rainfall events in Kecamatan Panjang. The data used includes key meteorological variables such as air temperature, dew point, humidity, and air pressure, collected from the Maritime Meteorology Station in Panjang (BMKG) over the past three years. The models were trained using this historical data, with the data divided into training and testing sets. The study results indicate that the Transformer model performs best with the highest accuracy compared to CNN and RNN. The Transformer model efficiently captures long-term dependencies in sequential data, providing more accurate and timely predictions. Model performance evaluation was conducted using accuracy, F1 score, precision, recall, ROC AUC, RMSE, and MAE metrics. The use of deep learning models in rainfall prediction is expected to assist in flood risk mitigation and planning for adaptation to increasingly frequent extreme weather due to climate change. This research significantly advances more accurate and efficient weather prediction systems for urban areas prone to hydrological disasters.
ANALISIS DISTRIBUSI ARAH DAN KECEPATAN ANGIN UNTUK DETEKSI CROSSWIND DI BANDARA RADIN INTEN II MENGGUNAKAN METODE WINDROSE Amidayantik, Damil; Triloka, Joko; Mutiara, Suci
Jurnal Informatika Vol 25 No 2 (2025): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jurnalinformatika.v25i120

Abstract

Aviation safety is strongly influenced by wind direction and wind speed, particularly during critical phases of flight such as take-off and landing. One of the most challenging wind conditions for airport operations is crosswind, which occurs when the wind blows perpendicular to the runway direction and can reduce aircraft controllability. Therefore, a comprehensive understanding of wind characteristics around an airport is essential to support safe and efficient flight operations. This study analyzes the distribution of wind direction and wind speed at Radin Inten II Airport using the windrose method as the primary analytical approach, supported by histogram and heatmap visualizations. The dataset consists of historical wind observations collected hourly by the Meteorology, Climatology, and Geophysics Agency (BMKG) Lampung over a ten-year period from 2014 to 2023. The data were processed to identify dominant wind directions, wind speed variability, and periods with a higher potential for crosswind conditions that may disrupt airport operations. The results indicate that the dominant wind direction at Radin Inten II Airport generally originates from the northwest, with prevailing wind speeds ranging between 2 and 6 knots, reflecting relatively stable wind conditions. However, several periods exhibit higher wind speeds that may increase the risk of crosswind occurrences. Further analysis reveals specific months with an increased frequency of crosswind events approaching critical operational thresholds, which require greater attention from airport operators. The findings of this study provide practical recommendations for airport management in determining appropriate runway usage, enhancing situational awareness during high-risk periods, and developing mitigation strategies such as flight schedule adjustments and runway infrastructure improvements. In addition, this research is expected to serve as a reference for other airports with similar wind characteristics in improving aviation safety and operational efficiency.
Co-Authors Abda Abda Amidayantik, Damil Andani, Mia Andika, Tahta Herdian Andini, Dwi Yana Ayu Aras, Irsan Arie Ardiansyah, Arie Aris Kurniawan Assidik, Reksa Qodri Aviv Fitria Yulia Aziz, RZ. Abdul Badri, Rico Elhando Bintoro, Panji Chairani Chairani Dewi, Deshinta Arrova Effendi, Mukhammad Khoirul Fadila, Kurnia Fauzi, Adi Ahmad Fitriana, Jesi Goesderilidar handoyo widi nugroho Hartono Hartono Indera Indera Kurnia Muludi Kurniawan Kurniawan Kurniawan, Tri Basuki Kurniawan, Wildan Indra M Said Hasibuan Maizary, Ary Mutiara, Suci Nouvan, M. Ahadi Novita Sari Nur ‘Azah Pramodiyo Wilantara, Rangga Priyatna, Aziz R Rizal Isnanto Rachmadi, Rachmadi Rintiana, Rintiana Rio Kurniawan Saputra, Muhammad Setiawan, Agustinus Eko Seyhan Siti Maesaroh Sugianto, Dian Sutedi Sutedi Tarjono Wasilah Wasilah Wibaselppa, Anggawidia Y. Suhendro Yusuf Irianto, Suhendro Zulkifli