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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Saptono Nugrohadi
Universitas PGRI Semarang
Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology

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Rain Prediction Using Rule-Based Machine Learning Approach Muchamad Taufiq Anwar; Saptono Nugrohadi; Vita Tantriyati; Vikky Aprelia Windarni
Advance Sustainable Science, Engineering and Technology (ASSET) Vol 2, No 1 (2020)
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v2i1.6019

Abstract

Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.
Co-Authors Anwar, Muchamad Taufiq Vikky Aprelia Windarni Vita Tantriyati