Perbandingan Metode Klasifikasi Data Mining Untuk Rekomendasi Tanaman Pangan

Abstract

Determination of the right food crops needs to be done to improve the community's economy in the agricultural sector. The use of traditional cropping patterns needs to be changed by utilizing information technology. The utilization of data from local governments can be used to assist in providing recommendations for types of food crops by processing them with several data mining methods. This method can extract information to find patterns and knowledge from the data. The classification method approach is used as a grouping of data based on data attachment to sample data. This study uses several classification methods, namely Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Neural Network, Random Tree, Random Forest, dan K Nearest Neighbor (KNN). These methods were successfully compared to find out which method is the best to help recommend appropriate and accurate food crops based on the results of the classification performance of each method. Random Tree was chosen as the best method for the results of this performance comparison using discretization and normalization methods at the pre-processing stage of the data. It can be seen based on the results of the Accuracy, Precision, Recall, and F1-Score values on the use of discretization of 98%, respectively. Meanwhile, normalization showed that the results of the Accuracy, Precision, Recall, and F1-Score values are 99%, respectively.