Implementasi Data Mining Untuk Memprediksi Masa Panen Dalam Bercocok Tanam Dengan Menggunakan Algoritma Naive Bayes Classifier

Abstract

Abstract−This study intends to predict the harvest period in farming at the Regional Technical Implementation Unit of Gabe Hutaraja Various Crops. The method used in this study is the Naive Bayes Classifier. The data used in this study are time series data from 2015 to 2017. The results of this study are predictions of rice, peanut, and corn crops in the future.
 Naive Bayes Classifier is a simple probabilistic classification process based on the application of Bayes' Theorem (Bayes rule) with the assumption of strong independence, in other words, in Naive Bayes, the model used is an independent feature model. Implementation using RapidMinner5.3 is used to help find an accurate value.
 The benefits of writing this research are that it can predict rice, peanut, and corn crops so that it is needed in farming as a field of planning and improving the quality of crop yields that aim to reduce farmers' losses.