Source Code Algoritma Naive Bayes Menggunakan Pemrograman Java
Table of Contents
Naive bayes merupakan salah satu algoritma klasifikasi yang populer. Sering saya jumpai tugas akhir mahasiswa yang menggunakan metode ini, baik untuk data teks, citra, bahkan suara. Selain simpel, tidak adanya proses training dalam naive bayes membuat algoritma ini relatif lebih cepat dari sisi waktu komputasi. Mesipun demikian, performa algoritma ini jangan dianggap enteng. Naive bayes telah terbukti efektif dalam mengatasi berbagai masalah.
Di posting ini, secara khusus saya menjabarkan bagaimana membangun source code naive bayes menggunakan pemrograman java. Source code naive bayes ini dikembangkan dari hitungan manual naive bayes yang pernah saya tuliskan di perhitungan manual naive bayes. Maka, saya sarankan, kawan-kawan membaca artikel hitung manual dulu sebelum source code.
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| Gambar 1. Data Input |
Data training naive bayes menggunakan data set sederhana sebagaimana ditunjukkan pada Gambar 1. Jadi, data set antara hitung manual dan coding sengaja disamakan untuk mempermudah proses belajar anda semua.
/**
*
* @author CHARIS
*/
public class NaiveBayes {
/**
* @param args the command line arguments
*/
static double PGo_out;
static double PStay_home;
static double PSunnyGo_out;
static double PRainyGo_out;
static double PSunnyStay_home;
static double PRainyStay_home;
static double PWorkingGo_out;
static double PBrokenGo_out;
static double PWorkingStay_home;
static double PBrokenStay_home;
static String[] Wheater = new String[10];
static String[] Car = new String[10];
static String[] Class = new String[10];
static Map Wheater_Temp = new HashMap<>();
static Map Car_Temp = new HashMap<>();
static Map Class_Temp = new HashMap<>();
public static void main(String[] args) throws IOException {
// TODO code application logic here
String[] Wheater = {"sunny", "rainy", "sunny",
"sunny", "sunny", "rainy",
"rainy", "sunny", "sunny", "rainy"};
String[] Car = {"working", "broken", "working",
"working", "working", "broken",
"broken", "working", "broken", "broken"};
String[] Class = {"go-out", "go-out", "go-out", "go-out",
"go-out", "stay-home", "stay-home", "stay-home",
"stay-home", "stay-home"};
// readFromExcel("D://data.xls");
System.out.println("PRINT MAP");
konvert2arr(Wheater_Temp, Wheater);
konvert2arr(Car_Temp, Car);
konvert2arr(Class_Temp, Class);
System.out.println("===== DATA TRAINING =====");
for (int a = 0; a < Class.length; a++) {
System.out.println(Wheater[a] + " " + Car[a] + " " + Class[a]);
}
System.out.println("===== PROBABILITAS KELAS =====");
int jumlahGo_out = hitungJumlah(Class, "go-out");
int jumlahStay_home = hitungJumlah(Class, "stay-home");
System.out.println("Jumlah Kelas go-out : " + jumlahGo_out);
System.out.println("Jumlah Kelas stay-home : " + jumlahStay_home);
PGo_out = P_class(jumlahGo_out, jumlahStay_home);
PStay_home = P_class(jumlahStay_home, jumlahGo_out);
System.out.println("Probabilitas Kelas go-out : " + PGo_out);
System.out.println("Probabilitas Kelas stay-home : " + PStay_home);
System.out.println("===== PROBABILITAS WHEATER =====");
int jumlahSunnyGo_out = hitungJumlahWheater(Wheater, Class, "sunny", "go-out");
int jumlahRainyGo_out = hitungJumlahWheater(Wheater, Class, "rainy", "go-out");
int jumlahRainyStay_home = hitungJumlahWheater(Wheater, Class, "rainy", "stay-home");
int jumlahSunnyStay_home = hitungJumlahWheater(Wheater, Class, "sunny", "stay-home");
System.out.println("Jumlah Wheater Sunny Go-Out : " + jumlahSunnyGo_out);
System.out.println("Jumlah Wheater Rainy Go-Out : " + jumlahRainyGo_out);
System.out.println("Jumlah Wheater Rainy Stay-Home : " + jumlahRainyStay_home);
System.out.println("Jumlah Wheater Sunny Stay-Home : " + jumlahSunnyStay_home);
PSunnyGo_out = P_atribut(jumlahSunnyGo_out, jumlahGo_out);
PRainyGo_out = P_atribut(jumlahRainyGo_out, jumlahGo_out);
PSunnyStay_home = P_atribut(jumlahSunnyStay_home, jumlahStay_home);
PRainyStay_home = P_atribut(jumlahRainyStay_home, jumlahStay_home);
System.out.println("Probabilitas weather=sunny|class=go-out : " + PSunnyGo_out);
System.out.println("Probabilitas weather=rainy|class=go-out : " + PRainyGo_out);
System.out.println("Probabilitas weather=sunny|class=stay-home : " + PSunnyStay_home);
System.out.println("Probabilitas weather=rainy|class=stay-home : " + PRainyStay_home);
System.out.println("===== PROBABILITAS CAR =====");
int jumlahWorkingGo_out = hitungJumlahCar(Car, Class, "working", "go-out");
int jumlahBrokenGo_out = hitungJumlahCar(Car, Class, "broken", "go-out");
int jumlahWorkingStay_home = hitungJumlahCar(Car, Class, "working", "stay-home");
int jumlahBrokenStay_home = hitungJumlahCar(Car, Class, "broken", "stay-home");
System.out.println("Jumlah Car Working Go-Out : " + jumlahWorkingGo_out);
System.out.println("Jumlah Car Broken Go-Out : " + jumlahBrokenGo_out);
System.out.println("Jumlah Car Working Stay-Home : " + jumlahWorkingStay_home);
System.out.println("Jumlah Car Broken Stay-Home : " + jumlahBrokenStay_home);
PWorkingGo_out = P_atribut(jumlahWorkingGo_out, jumlahGo_out);
PBrokenGo_out = P_atribut(jumlahBrokenGo_out, jumlahGo_out);
PWorkingStay_home = P_atribut(jumlahWorkingStay_home, jumlahStay_home);
PBrokenStay_home = P_atribut(jumlahBrokenStay_home, jumlahStay_home);
System.out.println("Probabilitas car=working|class=go-out : " + PWorkingGo_out);
System.out.println("Probabilitas car=broken|class=go-out : " + PBrokenGo_out);
System.out.println("Probabilitas car=working|class=stay-home : " + PWorkingStay_home);
System.out.println("Probabilitas car=broken|class=stay-home : " + PBrokenStay_home);
System.out.println(" ===== DATA TESTING ===== ");
String wheater="sunny";
String car="working";
System.out.println("wheater = " + wheater);
System.out.println("car = " + car);
System.out.println("Hasil Klasifikasi Naive Bayes");
System.out.println("Kesimpulan = "+ PrediksiNaiveBayes(wheater, car));
}
public static int hitungJumlah(String[] arrVariabel, String nilaiVar) {
int hitung = 0;
for (int a = 0; a < arrVariabel.length; a++) {
if (arrVariabel[a].equals(nilaiVar)) {
hitung++;
}
}
return hitung;
}
public static int hitungJumlahWheater(String[] wheater, String[] Class, String nilaiWheater, String nilaiClass) {
int hitung = 0;
for (int a = 0; a < Class.length; a++) {
if (wheater[a].equals(nilaiWheater) && Class[a].equals(nilaiClass)) {
hitung++;
}
}
return hitung;
}
public static int hitungJumlahCar(String[] car, String[] Class, String nilaiCar, String nilaiClass) {
int hitung = 0;
for (int a = 0; a < Class.length; a++) {
if (car[a].equals(nilaiCar) && Class[a].equals(nilaiClass)) {
hitung++;
}
}
return hitung;
}
public static double P_class(int jumlah_a, int jumlah_b) {
double Pclass = (double) jumlah_a / (jumlah_a + jumlah_b);
return Pclass;
}
public static double P_atribut(int jumlah_a, int jumlah_b) {
double Pclass = (double) jumlah_a / (jumlah_b);
return Pclass;
}
public static String PrediksiNaiveBayes(String wheater, String car) {
double hasilGo_Out = 0;
double hasilStay_Home = 0;
if (wheater.equals("sunny") && car.equals("working")) {
hasilGo_Out = PSunnyGo_out * PWorkingGo_out * PGo_out;
hasilStay_Home = PSunnyStay_home * PWorkingStay_home * PStay_home;
} else if (wheater.equals("sunny") && car.equals("broken")) {
hasilGo_Out = PSunnyGo_out * PBrokenGo_out * PGo_out;
hasilStay_Home = PSunnyStay_home * PBrokenStay_home * PStay_home;
} else if (wheater.equals("rainy") && car.equals("working")) {
hasilGo_Out = PRainyGo_out * PWorkingGo_out * PGo_out;
hasilStay_Home = PRainyStay_home * PWorkingStay_home * PStay_home;
} else if (wheater.equals("rainy") && car.equals("broken")) {
hasilGo_Out = PRainyGo_out * PBrokenGo_out * PGo_out;
hasilStay_Home = PRainyStay_home * PBrokenStay_home * PStay_home;
}
System.out.println("hasil Go-Out = " + hasilGo_Out);
System.out.println("hasil Stay-Home = " + hasilStay_Home);
if (hasilGo_Out > hasilStay_Home) {
return "Go-Out";
} else {
return "Stay-Home";
}
}
public static void readFromExcel(String file) throws IOException {
HSSFWorkbook myExcelBook = new HSSFWorkbook(new FileInputStream(file));
HSSFSheet myExcelSheet = myExcelBook.getSheet("training");
//evaluating cell type
FormulaEvaluator formulaEvaluator = myExcelBook.getCreationHelper().createFormulaEvaluator();
int a = 0, b = 0, c = 0;
for (Row row : myExcelSheet) //iteration over row using for each loop
{
for (Cell cell : row) //iteration over cell using for each loop
{
switch (formulaEvaluator.evaluateInCell(cell).getCellType()) {
case Cell.CELL_TYPE_NUMERIC: //field that represents numeric cell type
System.out.print(cell.getNumericCellValue() + "\t\t");
break;
case Cell.CELL_TYPE_STRING: //field that represents string cell type
if (cell.getColumnIndex() == 0) {
System.out.print(cell.getStringCellValue() + "\t\t");
Wheater_Temp.put(a, cell.getStringCellValue());
a++;
} else if (cell.getColumnIndex() == 1) {
System.out.print(cell.getStringCellValue() + "\t\t");
Car_Temp.put(b, cell.getStringCellValue());
b++;
} else if (cell.getColumnIndex() == 2) {
System.out.print(cell.getStringCellValue() + "\t\t");
Class_Temp.put(c, cell.getStringCellValue());
c++;
}
break;
}
}
System.out.println();
myExcelBook.close();
}
}
public static void konvert2arr(Map map, String[] arr) {
Set s = map.entrySet();
Iterator it = s.iterator();
int a = 0;
while (it.hasNext()) {
Map.Entry entry = (Map.Entry) it.next();
Integer key = (Integer) entry.getKey();
String value = (String) entry.getValue();
arr[a] = value;
System.out.println("Arr " + arr[a]);
a++;
}//while
System.out.println("========================");
}//printMap
}
hasil running program
Semoga bisa bermanfaat ya. Terutama bagi anda para pejuang tugas akhir dan skripsi. Silakan anda sesuaikan dengan data dan studi kasus. Gambar berikt adalah contoh penerapan naive bayes dalam bentuk GUI via Netbeans
Jika kurang paham silahkan bertanya.
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