Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction - Arifin, Oki; Saputra, Kurniawan; Fathoni, Halim (Universitas Negeri Semarang, 2021)
Purpose: This study aims to developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC), which expected will give information and can use by the farmer and industrial food crops. Methods: On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. Result: The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438. Novelty: Prediction system of food crops by data mining.