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IMPLEMENTASI METODE N-GRAM DAN ALGORITMA

NAIVE BAYES UNTUK MENDETEKSI EMAIL SPAM

BERBAHASA INDONESIA BERBASIS WEB SERVICE

SKRIPSI

Diajukan sebagai salah satu syarat untuk memperoleh gelar

Sarjana Komputer (S. Kom.)

Yustinus Vernanda

13110110111

PROGRAM STUDI TEKNIK INFORMATIKA

FAKULTAS TEKNIK DAN INFORMATIKA

UNIVERSITAS MULTIMEDIA NUSANTARA

TANGERANG

2017

Implementasi Metode..., Yustinus Vernanda, FTI UMN, 2018

iv

KATA PENGANTAR

Puji syukur kepada Tuhan Yang Maha Esa yang selalu menyertai selama

masa pengerjaan skripsi dan laporan skripsi berjudul “Implementasi Metode N-

gram dan Algortima Naive Bayes untuk Mendeteksi Email Spam Berbahasa

Indonesia Berbasis Web Service.” sehingga dapat diselesaikan dengan baik dan

benar. Skripsi ini diajukan kepada Program Studi Teknik Informatika, Fakultas

Teknik dan Informatika, Universitas Multimedia Nusantara.

Penyelesaian skripsi ini juga dibantu dan didukung oleh berbagai pihak,

seperti teman-teman, dosen-dosen pembimbing, dan keluarga. Oleh karena itu,

ucapan terima kasih yang sebesar-besarnya diucapkan kepada:

1. Dr. Ninok Leksono, selaku Rektor Universitas Multimedia Nusantara,

2. Hira Meidia, Ph. D., selaku Wakil Rektor Bidang Akademik,

3. Ir. Andrey Andoko, M.Sc., selaku Wakil Rektor Bidang Administrasi

Umum dan Keuangan,

4. Ika Yanuarti, S.E., MSF., selaku Wakil Rektor Bidang Kemahasiswaan,

5. Prof. Dr. Muliawati G. Siswanto, M.Eng.Sc., selaku Wakil Rektor Bidang

Hubungan dan Kerjasama,

6. Maria Irmina Prasetiyowati, S.Kom., M.T., selaku Ketua Program Studi

Teknik Informatika Universitas Multimedia Nusantara dan dosen

pembimbing pengerjaan skripsi,

7. Marcel Bonar Kristanda, S.Kom., M.Sc., selaku dosen pembimbing pertama

pengerjaan skripsi,

8. Seng Hansun, S.Si., M.Cs., selaku dosen pembimbing kedua pengerjaan

skripsi,


vi

IMPLEMENTASI METODE N-GRAM DAN ALGORITMA

NAIVE BAYES UNTUK MENDETEKSI EMAIL SPAM

BERBAHASA INDONESIA BERBASIS WEB SERVICE

ABSTRAK

Indonesia termasuk dalam rangking 18 dari total penyebaran spam di dunia.

Spam filter berbasis web service dengan jenis REST API dapat digunakan untuk

mendeteksi email spam berbahasa Indonesia pada email server maupun berbagai

jenis aplikasi email server. Dengan REST API, maka antar aplikasi dapat

melakukan pertukaran data bertipe JSON menggunakan perintah-perintah yang ada

pada HTTP. Salah satu jenis spam filter adalah Bayesian Filtering, dimana

algoritma Naive Bayes sebagai algoritma klasifikasinya. Sedangkan, Metode N-

gram digunakan untuk menambah akurasi dari pengimplementasian algoritma

Naive Bayes. Metode N-gram dan algoritma Naive Bayes untuk mendeteksi email

spam berbahasa Indonesia berbasis web service berhasil diimplementasikan dengan

hasil nilai akurasi sekitar 0,615 hingga 0,94, dengan nilai precision berkisaran pada

0,566 hingga 0,924, kemudian nilai recall berkisaran pada 0,96 hingga 1, dan nilai

f-measure berkisaran pada 0,721 hingga 0,942. Dengan metode 5-gram sebagai

metode N-gram yang memiliki nilai tertinggi untuk mendeteksi email spam

berbahasa Indonesia dengan nilai akurasi sebesar 0,94, nilai precision sebesar

0,924, nilai recall sebesar 0,96, dan nilai f-measure sebesar 0,942.

Kata Kunci: N-gram, Naive Bayes, spam filter, web service


vii

IMPLEMENTATION OF N-GRAM METHOD AND NAIVE

BAYES ALGORITHM FOR DETECTING INDONESIAN

LANGUAGE EMAIL SPAM BASED ON WEB SERVICE

ABSTRACT

Indonesia is ranked 18th out of the total spread of spam in the world. Web

Service-based spam filter has been able to detect spam emails in Bahasa Indonesia

on email servers and various types of email client applications. With REST API,

there will be data exchange in a form of JSON using HTTP commands. One of

spam filter technique is called Bayesian Filtering. This technique implements Naïve

Baiyes algorithm to classify such information into spam or non-spam category.

Meanwhile, the N-gram method has the ability to increase the accuracy of the

implementation of the Naive Bayes algorithm. Combination of N-gram method and

the Naive Bayes algorithm to detect spam email in Bahasa Indonesia based on web

service was successful with accuracy about 0,615 until 0,94, precision at 0,566 until

0,924, then recall at 0,96 until 1 and f-measure at 0.721 until 0.94. Moreover, the

5-gram method gives the best result based on test between 0-gram to 10-gram in

spam detection with calculated evaluation in accuracy at 0,94 , precision at 0,924,

recall at 0,96 and f-measure at 0,942.

Keyword: N-gram, Naive Bayes, spam filter, web service


viii

DAFTAR ISI

LEMBARAN PENGESAHAN SKRIPSI ............................................................. ii

PERNYATAAN TIDAK MELAKUKAN PLAGIAT ......................................... iii

KATA PENGANTAR ........................................................................................ iv

ABSTRAK ......................................................................................................... vi

ABSTRACT ...................................................................................................... vii

DAFTAR ISI .................................................................................................... viii

DAFTAR TABEL ............................................................................................... x

DAFTAR GAMBAR .......................................................................................... xi

DAFTAR RUMUS ........................................................................................... xiv

BAB I PENDAHULUAN ................................................................................. 1

1.1 Latar Belakang Masalah......................................................................... 1

1.2 Rumusan Masalah .................................................................................. 3

1.3 Batasan Masalah .................................................................................... 3

1.4 Tujuan Penelitian ................................................................................... 4

1.5 Manfaat Penelitian ................................................................................. 4

1.6 Sistematika Penulisan ............................................................................ 4

BAB II LANDASAN TEORI ............................................................................ 6

2.1 Email ..................................................................................................... 6

2.2 Spam dan Ham ...................................................................................... 8

2.3 Klasifikasi Spam dengan Algortima Naive Bayes ................................ 10

2.3.1 Penelitian Terdahulu ..................................................................... 10

2.3.2 Spam Filtering Teknik .................................................................. 12

2.3.3 Text Mining .................................................................................. 12

2.3.4 Metode N-gram ............................................................................ 18

2.3.5 Algoritma Naive Bayes ................................................................. 19

2.4 Efektivitas Spam Filter ........................................................................ 23

2.5 REST ................................................................................................... 25

2.6 Arsitektur Email Spam Filter ............................................................... 29

BAB III METODOLOGI PENELITIAN DAN PERANCANGAN SISTEM ..... 31

3.1 Metodologi Penelitian .......................................................................... 31

3.2 Perancangan Sistem ............................................................................. 32

3.2.1 Data Flow Diagram....................................................................... 34

3.2.2 Hierarki Menu .............................................................................. 41

3.2.3 Flowchart ..................................................................................... 44

3.2.4 Perancangan Application Programming Interface(API) ................. 78

3.2.4 Database Schema .......................................................................... 91

3.2.5 Struktur Tabel ............................................................................... 92

3.2.6 Perancangan Antarmuka ............................................................... 98

BAB IV IMPLEMENTASI DAN UJI COBA .................................................. 104

4.1 Spesifikasi Sistem .............................................................................. 104

4.2 Implementasi Algoritma .................................................................... 105

4.3 Implementasi Antarmuka ................................................................... 110

4.4 Implementasi Data JSON ................................................................... 114

4.5 Implementasi Web Service................................................................. 118

4.6 Uji Coba Perhitungan Manual ............................................................ 122

4.7 Uji Coba Spam Filter ......................................................................... 129


ix

4.8 Analisis Data ..................................................................................... 137

BAB V KESIMPULAN DAN SARAN .......................................................... 142

5.1 Kesimpulan........................................................................................ 142

5.2 Saran ................................................................................................. 142

DAFTAR PUSTAKA ...................................................................................... 144

DAFTAR LAMPIRAN .................................................................................... 148


x

DAFTAR TABEL

Tabel 2.1 Hasil Penelitian Mathew dan Bai ........................................................ 11

Tabel 2.2 Daftar Prefiks yang Meluluh ............................................................... 17

Tabel 2.3 Daftar Kemungkinan Perubahan Prefiks ............................................. 17

Tabel 2.4 Daftar Kombinasi Prefiks dan Sufiks yang Tidak Diperbolehkan ........ 17

Tabel 2.5 Daftar Kata Kategori Spam................................................................. 20

Tabel 2.6 Daftar Kata Kategori Ham .................................................................. 21

Tabel 2.7 Daftar Kata yang Diklasifikasi ............................................................ 21

Tabel 2.8 Nilai Probabilitas pada Kata Diklasifikasi dengan Kategori Spam ...... 22

Tabel 2.9 Nilai Probabilitas pada Kata Diklasifikasi dengan Kategori Ham ........ 22

Tabel 3.1 Aturan Kombinasi Awalan dan Akhiran yang Tidak Diperbolehkan ... 58

Tabel 3.2 Aturan Peluruhan Imbuhan ................................................................. 61

Tabel 3.3 Struktur Tabel Dataset ........................................................................ 92

Tabel 3.4 Struktur Tabel dataset_fil ................................................................... 93

Tabel 3.5 Struktur Tabel dataset_kosakata ......................................................... 94

Tabel 3.6 Struktur Tabel Keys............................................................................ 95

Tabel 3.7 Struktur Tabel Klasifikasi ................................................................... 95

Tabel 3.8 Struktur Tabel Limits ......................................................................... 96

Tabel 3.9 Struktur Tabel Login .......................................................................... 96

Tabel 3.10 Struktur Tabel ngram ........................................................................ 97

Tabel 3.11 Struktur Tabel Stopword ................................................................... 97

Tabel 3.12 Struktur Tabel tb_katadasar .............................................................. 98

Tabel 4.1 Frekuensi Setiap Kata ....................................................................... 122

Tabel 4.2 Probabilitas Setiap Kata Terhadap Spam dan Ham ........................... 124

Tabel 4.3 Confusion Matrix untuk Spam Filter tanpa Metode N-gram .............. 130

Tabel 4.4 Confusion Matrix untuk Spam Filter dengan Metode 1-gram ............ 130






Tabel 4.10 Confusion Matrix untuk Spam Filter dengan Metode 7-gram .......... 132



Tabel 4.13 Confusion Matrix untuk Spam Filter dengan Metode 10-gram ........ 133

Tabel 4.14 Hasil Uji Coba Nilai Akurasi .......................................................... 133

Tabel 4.15 Hasil Uji Coba Nilai Recall ............................................................ 134

Tabel 4.16 Hasil Uji Coba Nilai Precision ........................................................ 135

Tabel 4.17 Hasil Uji Coba Nilai F-measure ...................................................... 136

Tabel 4.18 Hasil Uji Spam Filter ...................................................................... 138


xi

DAFTAR GAMBAR

Gambar 2.1 Cara Kerja Email ............................................................................. 7

Gambar 2.2 Contoh Proses Case-folding ............................................................ 13

Gambar 2.3 Contoh Proses Tokenizing .............................................................. 14

Gambar 2.4 Contoh Proses Filtering ................................................................... 14

Gambar 2.5 Contoh Proses Stemming ................................................................ 15

Gambar 2.6 Format Kata Berimbuhan dalam Bahasa Indonesia.......................... 16

Gambar 2.7 Arsitektur REST API ...................................................................... 27

Gambar 2.8 Contoh HTTP request ..................................................................... 27

Gambar 2.9 Contoh HTTP Respond Error .......................................................... 28

Gambar 2.10 Contoh HTTP Respond Success .................................................... 28

Gambar 2.11 Arsitektur Spam Filter Sebagai Local Proxy .................................. 29

Gambar 2.12 Arsitektur Spam Filter Proses Paralel ............................................ 29

Gambar 2.13 Arsitektur Spam Filter dalam Mail Client ..................................... 30

Gambar 2.14 Arsitektur Spam Filter dalam Mail Store ....................................... 30

Gambar 3.1 Sistem Flow Spam Filter ................................................................. 33

Gambar 3.2 Diagram Konteks Aplikasi API Spam Filter .................................... 34

Gambar 3.3 Diagram level 1 Aplikasi API Spam Filter ...................................... 35

Gambar 3.4 Diagram Level 2 Pada Proses 1.5 Aplikasi API Spam Filter ............ 36




Gambar 3.8 Hierarki Menu Visitor dan User ...................................................... 42

Gambar 3.9 Hierarki Menu Admin ..................................................................... 43

Gambar 3.10 Flowchart Menu Utama Visitor dan User ...................................... 45

Gambar 3.11 Flowchart Menu Register .............................................................. 47

Gambar 3.12 Flowchart Menu Sign In ............................................................... 48

Gambar 3.13 Flowchart Menu Case Folding ...................................................... 49

Gambar 3.14 Flowchart Case Folding ................................................................ 49

Gambar 3.15 Flowchart Menu Tokenizing ......................................................... 50

Gambar 3.16 Flowchart Tokenizing ................................................................... 51

Gambar 3.17 Flowchart Menu Filtering ............................................................. 51

Gambar 3.18 Flowchart Filtering ....................................................................... 52

Gambar 3.19 Flowchart Menu Stemming ........................................................... 53

Gambar 3.20 Flowchart Stemming ..................................................................... 54

Gambar 3.21 Flowchart Function Del_Inflection_Suffixer ................................. 55

Gambar 3.22 Flowchart Function Del_Derivation_Suffixes ............................... 59

Gambar 3.23 Flowchart Function Del_Derivation_Prefix ................................... 60

Gambar 3.24 Flowchart Menu N-gram ............................................................... 62

Gambar 3.25 Flowchart N-gram ......................................................................... 63

Gambar 3.26 Flowchart Menu Naive Bayes ....................................................... 65

Gambar 3.27 Flowchart Algoritma Naive Bayes ................................................ 66

Gambar 3.28 Flowchart Menu API..................................................................... 70

Gambar 3.29 Flowchart Menu Generate Key User ............................................. 70

Gambar 3.30 Flowchart Menu List User............................................................. 71

Gambar 3.31 Flowchart Menu Remove Key User .............................................. 71

Gambar 3.32 Flowchart Menu Add Dataset ........................................................ 71


xii

Gambar 3.33 Flowchart Menu Remove Dataset ................................................. 72

Gambar 3.34 Flowchart Menu List Dataset ........................................................ 72

Gambar 3.35 Flowchart Menu Check Spam ....................................................... 72

Gambar 3.36 Flowchart Menu Console .............................................................. 73

Gambar 3.37 Flowchart Menu Profile ................................................................ 73

Gambar 3.38 Flowchart Menu Upgrade ............................................................. 74

Gambar 3.39 Flowchart Menu Halaman Admin ................................................. 75

Gambar 3.40 Flowchart Menu Active Key ......................................................... 76

Gambar 3.41 Flowchart Menu Suspend Key ...................................................... 76

Gambar 3.42 Flowchart Menu Upgrade Key ...................................................... 77

Gambar 3.43 Flowchart Menu Sign Out ............................................................. 77

Gambar 3.44 API Flow Spam Filter ................................................................... 78

Gambar 3.45 Header dan Body Method Generate Key User ............................... 80

Gambar 3.46 Header dan Body Method Remove Key User ............................... 81

Gambar 3.47 Header dan Body Method List User .............................................. 82

Gambar 3.48 Header dan Body Method List Dataset .......................................... 83

Gambar 3.49 Header dan Body Method Remove Dataset ................................... 84

Gambar 3.50 Header dan Body Method Add Dataset ......................................... 85

Gambar 3.51 Header dan Body Method Check Spam ......................................... 86

Gambar 3.52 Struktur Data JSON Failure .......................................................... 87

Gambar 3.53 Struktur Data JSON Method Generate Key User ........................... 87

Gambar 3.54 Struktur Data JSON Method Remove Key User ............................ 88

Gambar 3.55 Struktur Data JSON Method List User .......................................... 88

Gambar 3.56 Struktur Data JSON Method List Dataset ...................................... 89

Gambar 3.57 Struktur Data JSON Method Remove Dataset ............................... 89

Gambar 3.58 Struktur Data JSON Method Add Dataset ..................................... 90

Gambar 3.59 Struktur Data JSON Method Check Spam ..................................... 90

Gambar 3.60 Database Schema Spam Filter ....................................................... 91

Gambar 3.61 Rancangan Halaman Konten ......................................................... 98

Gambar 3.62 Rancangan Menu Visitor............................................................... 99

Gambar 3.63 Rancangan Menu User ................................................................ 100

Gambar 3.64 Rancangan Menu Sign In ............................................................ 101

Gambar 3.65 Rancangan Menu Register .......................................................... 101

Gambar 3.66 Rancangan Menu Console ........................................................... 102

Gambar 3.67 Rancangan Menu Halaman Admin .............................................. 103

Gambar 4.1 Potongan Kode Case Folding ........................................................ 105

Gambar 4.2 Potongan Kode Tokenizing ........................................................... 105

Gambar 4.3 Potongan Kode Filtering ............................................................... 106

Gambar 4.4 Potongan Kode Stemming............................................................. 106

Gambar 4.5 Potongan Kode N-gram ................................................................ 107

Gambar 4.6 Potongan Kode Algoritma Naive Bayes ........................................ 108

Gambar 4.7 Implementasi Desain Halaman Konten ......................................... 110

Gambar 4.8 Implementasi Desain Menu Visitor ............................................... 111

Gambar 4.9 Implementasi Desain Menu User .................................................. 111

Gambar 4.10 Implementasi Desain Menu Sign In............................................. 112

Gambar 4.11 Implementasi Desain Menu Register ........................................... 112

Gambar 4.12 Implementasi Desain Menu Console ........................................... 113

Gambar 4.13 Implementasi Desain Halaman Admin ........................................ 113


xiii

Gambar 4.14 Implementasi Data JSON Failure ................................................ 114

Gambar 4.15 Implementasi Data JSON Metode Generate Key User ................. 115

Gambar 4.16 Implementasi Data JSON Metode Remove Key User .................. 115

Gambar 4.17 Implementasi Data JSON Metode List User ................................ 115

Gambar 4.18 Implementasi Data JSON Metode List Dataset ............................ 116

Gambar 4.19 Implementasi Data JSON Metode List Dataset ............................ 117

Gambar 4.20 Implementasi Data JSON Metode Add Dataset ........................... 117

Gambar 4.21 Implementasi Data JSON Metode Check Spam ........................... 117

Gambar 4.22 Arsitektur Implementasi Web service .......................................... 118

Gambar 4.23 Potongan Kode Koneksi Gmail ................................................... 119

Gambar 4.24 Potongan Kode Pengambilan Email ............................................ 119

Gambar 4.25 Potongan Kode Request Web Service Spam Filter ...................... 120

Gambar 4.26 Potongan Kode Respond Web Service ........................................ 121

Gambar 4.27 Screenshot Implementasi Web Service ........................................ 121

Gambar 4.28 Screenshot Hasil Klasifikasi Menggunakan Spam Filter .............. 128

Gambar 4.29 Grafik Akurasi Spam Filter ......................................................... 134

Gambar 4.30 Grafik Recall Spam Filter ........................................................... 135

Gambar 4.31 Grafik Precision Spam Filter ....................................................... 136

Gambar 4.32 Grafik F-measure Spam Filter ..................................................... 137

Gambar 4.33 Grafik Hasil Uji Spam Filter ....................................................... 139


xiv

DAFTAR RUMUS

Rumus 2.1 Perhitungan Algoritma Naive Bayes ................................................. 19

Rumus 2.2 Perhitungan Naive Bayes Classifier ................................................. 19

Rumus 2.3 Probabilitas Kata Terhadap Kelas ..................................................... 20

Rumus 2.4 Probabilitas Kelas............................................................................. 20

Rumus 2.5 Perhitungan Akurasi ......................................................................... 23

Rumus 2.6 Perhitungan Recall ........................................................................... 24

Rumus 2.7 Perhitungan Precision ....................................................................... 24

Rumus 2.8 Perhitungan F-measure ..................................................................... 24


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