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Rifandi, Julian
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Computer Science & IT

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KLASIFIKASI DATA MINING UNTUK MENGESTIMASI POTENSI CURAH HUJAN MENGGUNAKAN ALGORITMA NAIVE BAYES Nuris, Nuzuliarini; Salsabila, Nina Nur; Rifandi, Julian; Diantika, Sri
JURNAL AKADEMIKA Vol 17 No 1 (2024): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i1.1320

Abstract

One of the things that is very important in Indonesia is climate. Climate itself greatly influences human survival. The relationship between rainfall and climate is very close and complex, where climate change can affect rainfall patterns in an area. High or low rainfall can have negative impacts on the environment and agriculture. Therefore, a deep understanding of rainfall patterns is very important in development planning and natural resource management. With the classification of potential rainfall, it is hoped that it can provide accurate predictions and assist in planning and managing related risks. The classification used in this research is Naive Bayes with monthly rainfall data in East Jakarta City from 2019 - 2022. Based on the calculation results, an accuracy rate of 80% was obtained, either using manual calculations or using the RapidMiner application.
Co-Authors Diantika, Sri Nuzuliarini Nuris Salsabila, Nina Nur