KLASIFIKASI DATA MINING UNTUK MENGESTIMASI POTENSI CURAH HUJAN MENGGUNAKAN ALGORITMA NAIVE BAYES

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.

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Publication Details

Journal
JURNAL AKADEMIKA
Published
2025-04-06
DOI
https://doi.org/10.53564/akademika.v17i1.1320
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

KLASIFIKASI DATA MINING UNTUK MENGESTIMASI POTENSI CURAH HUJAN MENGGUNAKAN ALGORITMA NAIVE BAYES

Sri Diantika, Nuzuliarini Nuris, Nina Nur Salsabila, Julian Rifandi
JURNAL AKADEMIKA
Data Mining and Machine Learning Applications
article

KLASIFIKASI DATA MINING UNTUK MENGESTIMASI POTENSI CURAH HUJAN MENGGUNAKAN ALGORITMA NAIVE BAYES

Sri Diantika, Nuzuliarini Nuris, Nina Nur Salsabila, Julian Rifandi
article en

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.

JURNAL AKADEMIKAVol. 17(1)
Universitas Informatika dan Bisnis Indonesia (ID)
Openalex Percentile: Top 89%
Data Mining and Machine Learning Applications
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