DataMiningLogHistoriKIRI.DecisionTree.java Source code

Introduction

Here is the source code for DataMiningLogHistoriKIRI.DecisionTree.java

Source

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package DataMiningLogHistoriKIRI;

import java.util.logging.Level;
import java.util.logging.Logger;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.Id3;
import weka.classifiers.Classifier;
import weka.core.Instances;

/**
 *
 * @author Jovan Gunawan
 */
public class DecisionTree {
    private Classifier tree;

    public String[] id3(Instances arff) {
        J48 tree = new J48();
        try {
            tree.buildClassifier(arff);
        } catch (Exception ex) {
            Logger.getLogger(Controller.class.getName()).log(Level.SEVERE, null, ex);
        }
        System.out.println(tree.toString());

        int nilaiBenar = 0, resultInt;
        float result = 0;
        for (int i = 0; i < arff.numInstances(); i++) {
            try {
                result = (float) tree.classifyInstance(arff.instance(i));
                resultInt = Math.round(result);
                //System.out.println(dataAfterPreprocessing.get(i)[6] + " " + arff.instance(i).stringValue(6));
                if (resultInt == Integer.parseInt(arff.instance(i).stringValue(6))) {
                    nilaiBenar++;
                }
            } catch (Exception ex) {
                Logger.getLogger(Controller.class.getName()).log(Level.SEVERE, null, ex);
            }
        }
        System.out.println("nilai: " + nilaiBenar + " " + arff.numInstances());
        double confident = nilaiBenar * 1.0 / arff.numInstances() * 100;
        System.out.println("Confident = " + confident + "%");

        String[] result2 = new String[5];
        return result2;
    }

    public String[] j48() {
        String[] result = new String[5];
        return result;
    }
}