Prediksi Produktivitas Tanaman Padi di Kabupaten Karawang Menggunakan Bayesian Networks

Abstrak

This research is aimed to build a model for predicting rice productivity level in Karawang district. The prediction using Bayesian Networks allowed three stages, pre-processing of data, implementation and evaluation stages. Pre-processing is transformation of numerical data into nominal data by using two scenarios, using threshold mean and discretization. Implementation stage is to apply Bayesian Networks algorithm, that is through structure learning process and parameter learning. The learning process of structures and parameters on bayesian networks using CaMML 1.41 software. Evaluation of Bayesian Networks performance in predicting rice productivity with confusion matrix, ie calculating prediction accuracy and log loss. The experiment results show the satisfactory results, the accuracy above 90%. The best model generated from pre-processing using the data discretization and 5-year training and 1-year testing data. This explain that the selection techniques of pre-processing and the technique of dividing the training data and testing the data affect the results of the performance evaluation of the structure of Bayesian Networks.