segmentasi pelanggan perusahaan perhotelan … awal.pdf · such competitive environment of hotel...

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SEGMENTASI PELANGGAN PERUSAHAAN PERHOTELAN MENGGUNAKAN METODE CLUSTERING DBSCAN DAN MODEL RFM TUGAS AKHIR Diajukan Guna Memenuhi Sebagian Persyaratan Dalam Rangka Menyelesaikan Pendidikan Sarjana Strata Satu (S1) Program Studi Teknologi Informasi NI MADE ANINDYA SANTIKA DEVI NIM. 1104505107 JURUSAN TEKNOLOGI INFORMASI FAKULTAS TEKNIK UNIVERSITAS UDAYANA 2015

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Page 1: SEGMENTASI PELANGGAN PERUSAHAAN PERHOTELAN … AWAL.pdf · Such competitive environment of hotel business in Bali makes the companies ... KATA PENGANTAR ... 2.3 Profil Aston Inn Tuban

SEGMENTASI PELANGGAN PERUSAHAAN PERHOTELAN

MENGGUNAKAN METODE CLUSTERING DBSCAN DAN MODEL RFM

TUGAS AKHIR

Diajukan Guna Memenuhi Sebagian Persyaratan

Dalam Rangka Menyelesaikan Pendidikan Sarjana Strata Satu (S1)

Program Studi Teknologi Informasi

NI MADE ANINDYA SANTIKA DEVI

NIM. 1104505107

JURUSAN TEKNOLOGI INFORMASI

FAKULTAS TEKNIK UNIVERSITAS UDAYANA

2015

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PERNYATAAN

Dengan ini saya menyatakan bahwa dalam Tugas Akhir ini tidak terdapat karya

yang pernah diajukan untuk memperoleh gelar kesarjanaan di suatu perguruan tinggi,

dan sepanjang pengetahuan saya juga tidak terdapat karya atau pendapat yang pernah

ditulis atau diterbitkan oleh orang lain, kecuali yang secara tertulis diacu dalam naskah

ini dan disebutkan dalam daftar pustaka.

Denpasar, Juli 2015

Ni Made Anindya Santika Devi

Page 3: SEGMENTASI PELANGGAN PERUSAHAAN PERHOTELAN … AWAL.pdf · Such competitive environment of hotel business in Bali makes the companies ... KATA PENGANTAR ... 2.3 Profil Aston Inn Tuban
Page 4: SEGMENTASI PELANGGAN PERUSAHAAN PERHOTELAN … AWAL.pdf · Such competitive environment of hotel business in Bali makes the companies ... KATA PENGANTAR ... 2.3 Profil Aston Inn Tuban

KEMENTERIAN PENDIDIKAN DAN KEBUDAYAANLTNIVERSITAS UDAYANA

FAKULTAS TEKNIKJURUSAN TEKNOLOGI INFORMASI

GedungTeknologilnformasi, Kampus Bukit Jimbaran - BaliTelepon: +62 361 7853533email:,

JUDUL

NAMANIMJURUSANFAKULTASTANGGAL UJIAN

BERITA ACARA TUGAS AKIIIR

SEGMENTASI PELANGGAN PERUSAHAANPERHOTELAN MENGGTINAKAN METODEDBSCAN DAN MODEL RFMNI MADE ANINDYA SANTIKA DEVIi 104505 107

TEKNOLOGI INFORMASITEKNIK03 JULI2015

Ni Made Ika Marini Mandenni. ST..M.KomNIDN.0017038007

vu Wirdiani- S.T.- M.T.

Ketua,?Prof. Dr. I Ketut Gede Darma Putra.S.Kom.. M.TNIDN. 0424047406

Sekretaris,

Telah dipertahankan di hadapan Dewan Penguji dan diterima sebagai bagianpersyaratan yang diperlukan untuk memperoleh gelar Sarjana TeknologiInformasi pada Program Studi Teknologi Informasi, Fakultas Teknik, UniversitasUdayana dengan nilai A.

DEWAN PENGUJI

Penguji II,

APenguji III,

MNi Kadek A

LMade5ukirsa. S.T.NrDN.0024107505

NIDN.0827038102

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vi

KATA PENGANTAR

Puji dan syukur penulis panjatkan kehadapan Ida Sang Hyang Widhi

Wasa/Tuhan Yang Maha Esa, karena atas Asung Kerta Wara Nugraha-Nya, akhirnya

penulis dapat menyelesaikan tugas akhir dengan judul “Segmentasi Pelanggan

Menggunakan Metode DBSCAN dan Model RFM”. Penulis mendapatkan banyak

bimbingan dari berbagai pihak. Ucapan terima kasih penulis sampaikan kepada:

1. Bapak Prof. Ir. Ngakan Putu Gede Suardana, M.T., Ph.D. selaku Dekan Fakultas

Teknik Universitas Udayana.

2. Bapak Dr. Eng. I Putu Agung Bayupati, ST., MT, selaku Ketua Jurusan Teknologi

Informasi Universitas Udayana.

3. Bapak Prof. Dr. I Ketut Gede Darma Putra, S.Kom., M.T., selaku dosen

pembimbing I yang telah banyak memberikan bimbingan dan masukan dalam

penyusunan tugas akhir ini.

4. Bapak I Made Sukarsa, S.T., M.T., selaku dosen pembimbing II yang telah banyak

memberikan petunjuk dan bimbingan selama penyusunan tugas akhir ini.

5. Bapak Ir. Antonius Ibi Weking, MT, selaku dosen pembimbing akademik, yang

telah memberikan bimbingan selama menempuh bimbingan di Jurusan Teknologi

Informasi Fakultas Teknik Universitas Udayana.

6. Kedua orang tua dan keluarga yang telah memberikan dukungan dan motivasi

dalam pembuatan tugas akhir ini.

7. Teman-teman seperjuangan dan segenap civitas di Jurusan Teknologi Informasi

Universitas Udayana yang telah memberikan sumbangan ide, pemikiran dan

dukungan dalam penyusunan tugas akhir ini.

Denpasar, Juli 2015

Penulis

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ABSTRAK

Kondisi persaingan perusahaan perhotelan yang sangat kompetitif di Bali

membuat perusahaan menyadari betapa sentralnya peran pelanggan sehingga banyak

perusahaan yang mempertahankan pasarnya melalui program pengembangan

segmentasi pelanggan. Segmentasi pelanggan merupakan salah satu penerapan proses

clustering pada Data Mining. Segmentasi pelanggan membagi pelanggan ke dalam

kelas-kelas tertentu untuk membantu sebuah perusahaan mengenali pelanggan

potensialnya. Penelitian ini membahas segmentasi pelanggan dengan menggunakan

teknik clustering dan pengukuran jarak Euclidean Distance. Metode clustering yang

digunakan adalah Metode Density Based Spatial Clustering of Application with Noise

(DBSCAN). Proses clustering dilakukan berdasarkan nilai yang didapat dari hasil

transformasi atribut menjadi nilai RFM. Hasil clustering yang diperoleh kemudian

akan diuji validitasnya untuk menentukan cluster optimal menggunakan Indeks

Validitas Silhouette. Hasil clustering yang diperoleh kemudian dicari nilai rata-ratanya

untuk menentukan kelas masing-masing cluster. Uji coba pada penelitian ini dilakukan

terhadap 31.019 row data transaksi. Data transaksi tersebut ditransformasi menjadi

nilai RFM dan menghasilkan 338 row data. Proses clustering dilakukan dengan

membentuk 2 hingga 5 cluster, yang kemudian dilakukan uji validitas untuk

menentukan jumlah cluster yang paling optimal. Hasil uji coba menunjukkan

pembentukan jumlah cluster yang paling optimal adalah 2 cluster dengan nilai indeks

silhouette sebesar 0,988366. Nilai indeks silhouette dari pembentukan 2 hingga 5

cluster menghasilkan nilai lebih besar dari 0, hal ini menunjukkan bahwa Metode

DBSCAN telah dapat melakukan proses segmentasi dengan baik.

Kata kunci: Segmentasi Pelanggan, Data Mining, Clustering, DBSCAN, RFM

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ABSTRACT

Such competitive environment of hotel business in Bali makes the companies

aware about the importance of customer’s role in their business. Thus, there are many

companies try to hold their market through the development of customer segmentation.

Customer segmentation divides the customer into certain classes in order to help a

certain company to identify its potential customer. This study discusses customer

segmentation using clustering techniques and distance measurements Euclidean

Distance. Clustering method used is Density Based Spatial Clustering of Application

with Noise (DBSCAN) Method. Clustering process is carried out based on the value

obtained from the transformation of attributes into RFM value. Clustering results

obtained will then be tested for its validity to determine the optimal cluster using

Silhouette validation index. Clustering results obtained then be searched for its

average value to determine the class of each cluster. The experiments were conducted

on 31.019 transactions. The raw data are then transformed into RFM Model and it is

able to produce 338 rows of data. Clustering process were done by forming 2 to 5

clusters, which then be tested using Silhouette validation index to determine the optimal

cluster. The experiment results show the attempt in creating 2 clusters contains the

highest value of silhouette index which is 0,988366. Clustering process which formed

2 to 5 clusters have a value of silhouette index more than 0, and such situation signifies

that the DBSCAN clustering method is able in doing the clustering process well.

Keywords: Customer Segmentation, Data Mining, Clustering, DBSCAN, RFM

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DAFTAR ISI

HALAMAN SAMPUL ........................................................................................ i

HALAMAN JUDUL ........................................................................................... ii

LEMBAR PERNYATAAN ................................................................................ iii

LEMBAR PENGESAHAN TUGAS AKHIR ................................................... iv

BERITA ACARA TUGAS AKHIR ................................................................... v

KATA PENGANTAR ......................................................................................... vi

ABSTRAK ........................................................................................................... vii

ABSTRACT .......................................................................................................... viii

DAFTAR ISI ........................................................................................................ ix

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

DAFTAR TABEL ............................................................................................... xiii

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

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

1.2 Rumusan Masalah ........................................................................................... 2

1.3 Tujuan Penelitian ............................................................................................ 3

1.4 Manfaat Penelitian .......................................................................................... 3

1.5 Batasan Masalah.............................................................................................. 3

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

BAB II TINJAUAN PUSTAKA ......................................................................... 5

2.1 State of The Art ............................................................................................... 5

2.2 Data Mining .................................................................................................... 6

2.3 Profil Aston Inn Tuban.................................................................................... 7

2.4 Customer Relationship Management .............................................................. 8

2.5 Data Mining dalam Kerangka Kerja CRM ..................................................... 9

2.6 Model RFM ..................................................................................................... 10

2.7 Analisis Cluster ............................................................................................... 11

2.7.1 Konsep Clustering ................................................................................ 11

2.7.2 Tujuan Clustering ................................................................................. 12

2.7.3 Jenis-jenis Clustering ............................................................................ 13

2.7.4 Density Based Spatial Clustering of Application with Noise ................ 14

2.8 Validasi Cluster ............................................................................................... 17

BAB III METODE DAN PERANCANGAN SISTEM .................................... 19

3.1 Tempat dan Waktu Penelitian ......................................................................... 19

3.2 Alur Analisis ................................................................................................... 19

3.3 Sumber Data .................................................................................................... 20

3.4 Metode Pengumpulan Data ............................................................................. 20

3.5 Instrumen Pembuatan Sistem .......................................................................... 20

3.6 Perancangan Sistem ........................................................................................ 21

3.6.1 Gambaran Umum Sistem ...................................................................... 21

3.6.2 Algoritma Perancangan Sistem ............................................................. 21

3.6.3 Pemilihan Data ...................................................................................... 24

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3.6.4 Transformasi Data ................................................................................. 28

3.6.5 Clustering .............................................................................................. 31

3.6.6 Pemodelan Data .................................................................................... 59

3.6.7 Cluster Validation ................................................................................. 64

3.8 Perancangan Basis Data .................................................................................. 70

3.9 Antar Muka Aplikasi ....................................................................................... 83

3.9.1 Form Standar .......................................................................................... 83

3.9.2 Form RFM .............................................................................................. 83

3.9.3 Form Clustering DBSCAN .................................................................... 84

3.9.4 Form Segmentasi .................................................................................... 85

3.9.5 Form Chart Customer ............................................................................. 86

BAB IV HASIL DAN PEMBAHASAN ............................................................ 87

4.1 Proses Pemilihan Data..................................................................................... 87

4.2 Proses Transformasi Data ............................................................................... 88

4.3 Hasil Uji Coba Clustering Algoritma DBSCAN ............................................ 88

4.4 Analisis Cluster ............................................................................................... 111

4.5 Pengaruh Inisialisasi Minimal Points (Minpts) dan Epsilon (Eps) ................. 112

BAB V PENUTUP ............................................................................................... 114

5.1 Simpulan ......................................................................................................... 114

5.2 Saran ................................................................................................................ 114

DAFTAR PUSTAKA .......................................................................................... 115

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DAFTAR GAMBAR

Gambar 2.1 Hubungan KDD dengan Proses Data Mining (Fayyad, 2000).......... 6

Gambar 2.2 Directly Density-Reachable .............................................................. 14

Gambar 2.3 Kasus Asimetris................................................................................. 15

Gambar 2.4 Density Reachable ............................................................................. 15

Gambar 2.5 Density Connected............................................................................. 16

Gambar 3.1 Gambaran Umum Sistem .................................................................. 21

Gambar 3.2 Flowchart Algoritma Perancangan Sistem ....................................... 21

Gambar 3.3 Relasi Antar Tabel ............................................................................. 27

Gambar 3.4 Skema Penentuan Nilai tbStandar ..................................................... 29

Gambar 3.5 Skema Penentuan Nilai tbRFM ......................................................... 30

Gambar 3.6 Flowchart Komputasi Algoritma DBSCAN ..................................... 32

Gambar 3.7 Pembentukan Cluster Algoritma DBSCAN ...................................... 59

Gambar 3.8 Flowchart Indeks Validitas Silhouette .............................................. 65

Gambar 3.9 Hasil Perhitungan Validitas Silhouette ............................................. 69

Gambar 3.10 Perbandingan Nilai Validitas Silhouette ......................................... 70

Gambar 3.11 Rancangan Basis Data ..................................................................... 70

Gambar 3.12 Form Standar ................................................................................... 83

Gambar 3.13 Form RFM ...................................................................................... 84

Gambar 3.14 Form Clustering .............................................................................. 85

Gambar 3.15 Form Segmentasi............................................................................. 85

Gambar 3.16 Form Chart Customer ..................................................................... 86

Gambar 4.1 Hasil Pemilihan Data ......................................................................... 87

Gambar 4.2 Hasil Transformasi Data.................................................................... 88

Gambar 4.3 Hasil Segmentasi Minpts 4 dan Eps 2000000 dengan 2 Cluster....... 89

Gambar 4.4 Grafik Segmentasi Minpts 4 dan Eps 2000000 dengan 2 Cluster..... 90

Gambar 4.5 Hasil Perbandingan Kelas ................................................................. 90

Gambar 4.6 Hasil Segmentasi Minpts 4 dan Eps 2500000 dengan 2 Cluster....... 91

Gambar 4.7 Grafik Segmentasi Minpts 4 dan Eps 2500000 dengan 2 Cluster..... 92

Gambar 4.8 Hasil Perbandingan Kelas ................................................................. 92

Gambar 4.9 Hasil Segmentasi Minpts 2 dan Eps 3000000 dengan 3 Cluster....... 94

Gambar 4.10 Grafik Segmentasi Minpts 2 dan Eps 3000000 dengan 3 Cluster... 95

Gambar 4.11 Hasil Perbandingan Kelas ............................................................... 95

Gambar 4.12 Hasil Segmentasi Minpts 3 dan Eps 2000000 dengan 3 Cluster..... 96

Gambar 4.13 Grafik Segmentasi Minpts 3 dan Eps 2000000 dengan 3 Cluster... 97

Gambar 4.14 Hasil Perbandingan Kelas ............................................................... 97

Gambar 4.15 Hasil Segmentasi Minpts 2 dan Eps 4000000 dengan 4 Cluster..... 99

Gambar 4.16 Grafik Segmentasi Minpts 2 dan Eps 4000000 dengan 4 Cluster... 100

Gambar 4.17 Hasil Perbandingan Kelas ............................................................... 100

Gambar 4.18 Hasil Segmentasi Minpts 2 dan Eps 5500000 dengan 4 Cluster..... 101

Gambar 4.19 Grafik Segmentasi Minpts 2 dan Eps 5500000 dengan 4 Cluster... 102

Gambar 4.20 Hasil Perbandingan Kelas ............................................................... 102

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Gambar 4.21 Hasil Segmentasi Minpts 2 dan Eps 5000000 dengan 5 Cluster..... 105

Gambar 4.22 Grafik Segmentasi Minpts 2 dan Eps 5000000 dengan 5 Cluster... 106

Gambar 4.23 Hasil Perbandingan Kelas ............................................................... 106

Gambar 4.24 Hasil Segmentasi Minpts 2 dan Eps 4500000 dengan 5 Cluster..... 107

Gambar 4.25 Grafik Segmentasi Minpts 2 dan Eps 4500000 dengan 5 Cluster... 108

Gambar 4.26 Hasil Perbandingan Kelas ............................................................... 108

Gambar 4.27 Grafik Indeks Silhouette.................................................................. 111

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DAFTAR TABEL

Tabel 2.1 Pembagian Customer dengan Model RFM ........................................... 11

Tabel 3.1 Tabel Customer ..................................................................................... 24

Tabel 3.2 Contoh Data pada Tabel Customer ....................................................... 25

Tabel 3.3 Tabel Room ........................................................................................... 25

Tabel 3.4 Contoh Data pada Tabel Room ............................................................. 26

Tabel 3.5 Tabel Transaksi ..................................................................................... 26

Tabel 3.6 Contoh Data pada Tabel Transaksi ....................................................... 26

Tabel 3.7 Tabel Detail Transaksi .......................................................................... 27

Tabel 3.8 Contoh data pada Tabel Detail Transaksi ............................................. 27

Tabel 3.9 Data Pemilihan Atribut sesuai Model RFM .......................................... 28

Tabel 3.10 Raw Data pada tbTransaksi................................................................. 28

Tabel 3.11 Data pada tbStandar ............................................................................ 29

Tabel 3.12 Data pada tbRFM ................................................................................ 31

Tabel 3.13 Tabel Contoh Data RFM ..................................................................... 33

Tabel 3.14 Hasil Perhitungan Iterasi Pertama ....................................................... 34

Tabel 3.15 Hasil Perhitungan Iterasi Kedua ......................................................... 35

Tabel 3.16 Hasil Perhitungan Iterasi Ketiga ......................................................... 36

Tabel 3.17 Hasil Perhitungan Iterasi Keempat ..................................................... 37

Tabel 3.18 Hasil Perhitungan Iterasi Kelima ........................................................ 38

Tabel 3.19 Hasil Perhitungan Iterasi Keenam....................................................... 39

Tabel 3.20 Hasil Perhitungan Iterasi Ketujuh ....................................................... 40

Tabel 3.21 Hasil Perhitungan Iterasi Kedelapan ................................................... 41

Tabel 3.22 Hasil Perhitungan Iterasi Kesembilan ................................................. 42

Tabel 3.23 Hasil Perhitungan Iterasi Kesepuluh ................................................... 43

Tabel 3.24 Hasil Perhitungan Iterasi Kesebelas .................................................... 44

Tabel 3.25 Hasil Perhitungan Iterasi Dua Belas ................................................... 45

Tabel 3.26 Hasil Perhitungan Iterasi Tiga Belas ................................................... 46

Tabel 3.27 Hasil Perhitungan Iterasi Empat Belas ............................................... 47

Tabel 3.28 Hasil Perhitungan Iterasi Lima Belas ................................................. 48

Tabel 3.29 Hasil Perhitungan Iterasi Enam Belas ................................................. 49

Tabel 3.30 Hasil Perhitungan Iterasi Tujuh Belas ................................................ 50

Tabel 3.31 Hasil Perhitungan Iterasi Delapan Belas ............................................. 51

Tabel 3.32 Hasil Perhitungan Iterasi Sembilan Belas ........................................... 52

Tabel 3.33 Hasil Perhitungan Iterasi Dua Puluh ................................................... 53

Tabel 3.34 Hasil Perhitungan Iterasi Dua Puluh Satu ........................................... 54

Tabel 3.35 Hasil Perhitungan Iterasi Dua Puluh Dua ........................................... 55

Tabel 3.36 Hasil Perhitungan Iterasi Dua Puluh Tiga........................................... 56

Tabel 3.37 Hasil Perhitungan Iterasi Dua Puluh Empat ....................................... 57

Tabel 3.38 Hasil Perhitungan Iterasi Dua Puluh Lima ......................................... 58

Tabel 3.39 Domain Nilai untuk Variabel RFM .................................................... 60

Tabel 3.40 Deskripsi Variabel Linguistik dan Label Konsumen .......................... 61

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Tabel 3.41 Input Validasi Silhouette ..................................................................... 62

Tabel 3.42 Rentang Nilai Domain Value .............................................................. 63

Tabel 3.43 Penentuan Kelas Cluster ..................................................................... 63

Tabel 3.44 Input Validasi Silhouette ..................................................................... 66

Tabel 3.45 Hasil Perhitungan S(i) ......................................................................... 68

Tabel 3.46 Tabel tbStandar ................................................................................... 71

Tabel 3.47 Contoh Data Tabel tbStandar .............................................................. 71

Tabel 3.48 Tabel tbMasterStandar ........................................................................ 72

Tabel 3.49 Contoh Data Tabel tbMasterStandar ................................................... 72

Tabel 3.50 Tabel tbRFM ....................................................................................... 73

Tabel 3.51 Contoh Data Tabel tbRFM .................................................................. 73

Tabel 3.52 Tabel tbHasil ....................................................................................... 74

Tabel 3.53 Contoh Data Tabel tbHasil .................................................................. 74

Tabel 3.54 Tabel tbHasilSilhouette ....................................................................... 75

Tabel 3.55 Contoh Data Tabel tbHasilSilhouette ................................................. 75

Tabel 3.56 Tabel tbHasilGSMax........................................................................... 76

Tabel 3.57 Contoh Data Tabel tbHasilGSMax ..................................................... 76

Tabel 3.58 Tabel tbRangeR................................................................................... 77

Tabel 3.59 Contoh Data Tabel tbRangeR ............................................................. 77

Tabel 3.60 Tabel tbRangeF ................................................................................... 78

Tabel 3.61 Contoh Data Tabel tbRangeF .............................................................. 78

Tabel 3.62 Tabel tbRangeM .................................................................................. 79

Tabel 3.63 Contoh Data Tabel tbRangeM ............................................................ 79

Tabel 3.64 Tabel tbMasterKelasReg ..................................................................... 80

Tabel 3.65 Contoh Data Tabel tbMasterKelasReg ............................................... 80

Tabel 3.66 Tabel tbMasterKelasDet ..................................................................... 80

Tabel 3.67 Contoh Data Tabel tbMasterKelasDet ................................................ 81

Tabel 3.68 Tabel tbMasterSegmentasi .................................................................. 81

Tabel 3.69 Tabel tbMasterSegmentasi .................................................................. 81

Tabel 3.70 Tabel tbHasilSegmentasi..................................................................... 82

Tabel 3.71 Contoh Data Tabel tbHasilSegmentasi ............................................... 82

Tabel 4.1 Summary Hasil Pembentukan 2 Cluster ............................................... 93

Tabel 4.2 Summary Hasil Pembentukan 3 Cluster ............................................... 98

Tabel 4.3 Summary Hasil Pembentukan 4 Cluster ............................................... 103

Tabel 4.4 Summary Hasil Pembentukan 5 Cluster ............................................... 109

Tabel 4.5 Pengaruh Nilai Minpts Terhadap Jumlah Noise yang Terbentuk ......... 112

Tabel 4.6 Pengaruh Nilai Eps Terhadap Jumlah Noise yang Terbentuk ............... 113