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Yahya Auliya Abdillah
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Jalan Timur, SMA Nurul Jadid, Dusun Krajan, RT 015 / RW 007, No. 89, Desa Karanganyar, Kecamatan Paiton, Kabupaten Probolinggo, Jawa Timur, Indonesia.
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Journal of Artificial Intelligence
Published by CV Narasi Khatulistiwa Indonesia
ISSN : 3163852X     EISSN : 30899710     DOI : https://doi.org/10.67490/kjarti.v3i1
Core Subject :
Journal of Artificial Intelligence is a Journal of Published by The CV Narasi Khatulistiwa Indonesia, Probolinggo, East Java, Indonesia. It publishes biannually on June and December (twice a year). Khatulistiwa : Journal of Artificial Intelligence covers a wide range of topics in the field of Artificial Intelligence (AI). Its focus includes the development of algorithms, machine learning techniques, and AI applications for problem-solving across various sectors. The journal also highlights innovations in big data processing, natural language processing, and knowledge-based intelligent systems. Additionally, it emphasizes ethical considerations and the social impact of AI to promote responsible technological advancements. Journal of Artificial Intelligence menerbitkan karya tulis orisinal di bidang informatika dan teknologi komputasi, yang mencakup, tetapi tidak terbatas pada, bidang *Artificial Intelligence, Computer Vision, Software Engineering, Natural Language Processing, Data Science, Soft Computing,* dan *Big Data Infrastructure*.
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Articles 21 Documents
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Rainfall Prediction in East Java Using Attention-Based LSTM Beny Yusman
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 3 No 1 (2026): June 2026
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v3i1.675

Abstract

Rainfall is one of the most influential meteorological parameters affecting agriculture, transportation, infrastructure, public health, and disaster mitigation. Accurate rainfall prediction is highly important, especially in East Java, one of the largest rice-producing regions in Indonesia. This study aims to develop a daily rainfall prediction model using the Attention-Based Long Short-Term Memory (LSTM) method. The dataset used in this study was obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG), covering the period from January 1, 2000, to December 31, 2023, with a total of 8,764 daily records. The variables include average temperature, relative humidity, rainfall, sunshine duration, and average wind speed. The research stages include data collection, preprocessing, missing value handling, outlier replacement using the Z-Score method, correlation-based feature selection, Min-Max normalization, lagged feature construction, data splitting, Attention-Based LSTM model design, model training, model testing, and Streamlit application integration. The best model was obtained using a batch size of 16 and 100 epochs, achieving a validation MSE of 0.007038 and a validation RMSE of 0.083893. On testing data, the model achieved an MSE of 0.008076 and an RMSE of 0.089871. These results indicate that Attention-Based LSTM can model daily rainfall patterns effectively and has potential as a data-driven rainfall prediction support system

Page 3 of 3 | Total Record : 21

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