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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal of Statistics and Its Applications
Arbaynah, Siti
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Computer Science & IT Mathematics Other

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Long Short-Term Memory as a Rainfall Forecasting Model for Bogor City in 2025-2026 Fadhilah, Nur Anggraini; Dzulhij Rizki, Muhammad Abshor; Azahran, Muhammad Ryan; Arbaynah, Siti; Antique Yusuf, Rakesha Putra; Angraini, Yenni; Nurhambali, Muhammad Rizky
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9068

Abstract

Indonesia is a country with a tropical climate that has unique and changing weather patterns. Accurate rainfall prediction can help local governments, farmers, and the broader community plan activities that depend on rainfall patterns. This research aims to develop a rainfall prediction model for Bogor City using past rainfall data in Bogor City, which is known as an area with high rainfall levels and dynamic rainfall patterns. The analysis utilizes rainfall data recorded by the JAXA satellite from January 1, 2014, to December 31, 2024. The prediction method implemented in this research is the long short-term memory (LSTM). The LSTM modelling process evaluates various models by comparing RMSE, MAE, and correlation values through expanding window cross-validation, selecting the model with the lowest average RMSE and MAE with the highest correlation as the optimal choice. The best-performing model was achieved with 25 epochs and a batch size of 1, resulting in an average RMSE of 56.3340, MAE of 35.5223, and correlation of 0.3209. This best-performing model is then employed to predict rainfall for the next two years. The results show significant daily variations in the predicted rainfall but can capture existing seasonal patterns.
Optimization of Fuzzy C-Means Clustering with Particle Swarm Optimization on Socioeconomic Indicators of ASEAN Countries Indriyani, Cindy; Arbaynah, Siti; Wijaya, Ananda Putra; Oktaviani, Lusi; Yumna, Fadhilah; Othman, Norashida; Oktarina, Sachnaz Desta; Rahma Anisa
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p274-288

Abstract

Grouping data based on similarity in characteristics is commonly applied in various exploratory analyses. The Fuzzy C-Means algorithm offers flexibility through the degree of membership of data points in each cluster, but it is vulnerable to poor cluster center initialization, which increases the risk of getting trapped in local optima. To enhance the performance of Fuzzy C-Means, this study integrates the Particle Swarm Optimization method for determining cluster centers. The evaluation is conducted by comparing Fuzzy C-Means and Fuzzy C-Means-Particle Swarm Optimization across several cluster counts using three internal validation metrics, namely the silhouette coefficient, partition coefficient, and Xie-Beni Index. The results show that Fuzzy C-Means-Particle Swarm Optimization consistently yields higher silhouette coefficient and partition coefficient values, along with lower Xie-Beni Index values, compared to standard Fuzzy C-Means. This indicates that the integration of Particle Swarm Optimization can improve clustering quality in terms of cluster compactness and separation. This hybrid approach demonstrates significant potential in complex data clustering scenarios.
Co-Authors Antique Yusuf, Rakesha Putra Azahran, Muhammad Ryan Dzulhij Rizki, Muhammad Abshor Fadhilah, Nur Anggraini Indriyani, Cindy Lusi Oktaviani Nurhambali, Muhammad Rizky Othman, Norashida Rahma Anisa Sachnaz Desta Oktarina Wijaya, Ananda Putra Yenni Angraini Yumna, Fadhilah