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October 10, 2025JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)Open Access

Cluster-Based Machine Learning Approaches for Predicting Daily Maximum Temperatures in Indonesia Under Climate Change

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Authors

UCUston Nawawi ChristantoBMBrina MiftahurrohmahTBTaufiqotul Bariyah

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Overview

This research demonstrates improved temperature prediction in Indonesia using machine learning models, indicating effective climate adaptation strategies.

Key Points

  • SVR outperformed other models in predicting maximum temperatures across climate clusters in Indonesia.
  • In stable regions, SVR achieved high accuracy with a root mean square error of 0.10 and mean absolute error of 0.08.
  • The study employed K-Means clustering to categorize Indonesian regions, enhancing predictive model performance.
  • This innovative approach offers a robust methodology for climate early warning systems in high-risk zones.

Cite This Study

Christanto et al. (2025) studied this question.

synapsesocial.com/papers/68e861b07ef2f04ca37e4bb1https://doi.org/10.33480/jitk.v11i1.6749
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Also Consider

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  1. 1Application of machine learning for short-term climate prediction in Indonesia2024
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  4. 4Analysis of VAE-LSTM Performance in Detecting Anomalies in Average Daily Temperature Data in Jakarta 2000-20232025
  5. 5Integrated analysis of meteorological conditions and agricultural yields in Indonesia using causal learning and intelligent clustering for climate change mitigation2026 · 1 citations