PERBANDINGAN KINERJA ALGORITMA DATA

MINING YANG BERBASIS TEKNIK FEATURE

SELECTION DALAM MENDETEKSI

PENYAKIT GINJAL KRONIS

SKRIPSI

Diajukan Guna Memenuhi Persyaratan Memperoleh

Gelar Sarjana Komputer (S.Kom)

Michelle Prizcillya

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PROGRAM STUDI SISTEM INFORMASI

FAKULTAS TEKNIK DAN INFORMATIKA

UNIVERSITAS MULTIMEDIA NUSANTARA

TANGERANG

2021

i

PERNYATAAN

ii

HALAMAN PERSETUJUAN

Skripsi Dengan Judul

“Perbandingan Kinerja Algoritma Data Mining berbasis teknik Feature selection

dalam Mendeteksi Penyakit Ginjal Kronis”

Oleh

Michelle Prizcillya

Telah disetujui untuk diajukan pada

Sidang Ujian Skripsi Universitas Multimedia Nusantara

Tangerang, 04 Januari 2021

Menyetujui,

iii

HALAMAN PENGESAHAN

Skripsi Dengan Judul

“Perbandingan Kinerja Algoritma Data Mining berbasis teknik Feature selection

dalam Mendeteksi Penyakit Ginjal Kronis”

Oleh

Michelle Prizcillya

Telah diujikan pada hari Senin, 11 Januari 2021,

pukul 11.00 sd 12.30 dan dinyatakan lulus

dengan susunan penguji sebagai berikut

Ketua Sidang Penguji

Ririn Ikana Desanti, S.Kom., M.Kom. Monika Evelin Johan, S.Kom., M.M.S.I

Dosen Pembimbing

Ir. Raymond Sunardi Oetama, M.C.I.S.

Disahkan oleh

Ketua Program Studi Sistem Informasi – UMN

Ririn Ikana Desanti, S.Kom., M.Kom.

25 Januari 2021

iv

PERBANDINGAN KINERJA ALGORITMA DATA MINING

BERBASIS TEKNIK FEATURE SELECTION DALAM

MENDETEKSI PENYAKIT GINJAL KRONIS

ABSTRAK Oleh : Michelle Prizcillya

Penyakit ginjal kronis atau PGK adalah hilangnya fungsi ginjal secara bertahap dari

waktu ke waktu. PGK disebut sebagai "silent killer" sebab pasien yang terkena PGK

tidak menyadari bahwa mereka terkena PGK karena PGK tidak mempunyai gejala

di tahapan awal. Atribut yang diperlukan untuk melakukan tes kesehatan untuk

PGK juga cukup banyak sehingga membutuhkan biaya yang lumayan mahal. oleh

karena itu PGK dapat dicegah, ditanggulangi dan kemungkinan mendapatkan

perawatan yang efektif akan lebih besar jika sudah diketahui sedari awal serta dapat

menghemat biaya pengobatan sehingga dibuatnya model untuk mempermudah

dalam melakukan deteksi PGK.

Proses penelitian ini akan menggunakan data science tools Rapid Miner dan proses

data mining dengan kerangka kerja CRISP-DM untuk melakukan perbandingan

penggunaan yaitu K-Nearest Neighbour(K-NN), decision tree serta logistic

regression berbasis teknik feature selection sebagai teknik untuk pemilihan atribut

yang relevan dalam sebuah dataset dan untuk melakukan pemilihan dan reduksi

data maka teknik feature selection yang digunakan adalah forward selection dan

backward elimination yang akan dibandingkan hasilnya.

Hasil dari penelitian ini menyimpulkan bahwa teknik feature selection terbukti

meningkatkan akurasi pada penggunaan algoritma K-NN, decision tree dan logistic

regression sedangkan algoritma terbaik untuk forward selection itu adalah decision

tree dan backward elimination adalah logistic regression.

Kata kunci : backward elimination, decision tree, forward selection, k-nearest

neighbour , logistic regression.

v

PERFORMANCE COMPARSION OF DATA MINING ALGORITHM

BASED ON FEATURE SELECTION TECHNIQUES IN DETECTING

CHRONIC KIDNEY DISEASES

ABSTRACT By : Michelle Prizcillya

Chronic kidney disease or CKD is a gradual loss of renal function over time. CKD

is referred to as a "silent killer" because patients affected by PGK do not realize

that they have CKD because CKD has no symptoms in the early stages. The

attributes needed to do a health test for CKD are also quite a lot so it costs quite a

lot. therefore PGK can be prevented, overcome and the possibility of getting

effective treatment will be greater if it is known from the beginning and can save

on medical costs so that the model is created to facilitate CKD detection.

This research process will use Rapid Miner data science tools and data mining

process with CRISP-DM framework to compare the use of K-Nearest Neighbour

(K-NN), decision tree and logistic regression based feature selection technique as

a technique for selecting relevant attributes in a dataset and to do data selection

and reduction, the feature selection technique used is forward selection and

backward elimination that will be compared to the result.

The results of this study concluded that feature selection techniques are proven to

improve accuracy in the use of K-NN algorithms, decision trees and logistic

regression while the best algorithm for forward selection is decision tree and

backward elimination is logistic regression.

Keywords : backward elimination, decision tree, forward selection, k-nearest

neighbour , logistic regression.

vi

KATA PENGANTAR

Puji syukur kepada Tuhan Yang Maha Esa sehingga telah terselesaikannya

laporan skrispi yang berjudul “Perbandingan Kinerja Algortima Data Mining

berbasis teknik feature selection dalam Mendeteksi Penyakit Ginjal Kronis” ini

pada waktunya. Laporan skripsi ini disusun untuk memenuhi salah satu prasyarat

kelulusan di Universitas Multimedia Nusantara dan juga merupakan bukti bahwa

telah menyelesaikan kuliah jenjang program Strata-1 guna memperoleh gelar

Sarjana Komputer .

Dalam penyusunan laporan skripsi ini, banyak sekali hambatan dan

rintangan yang telah hadapi namun pada akhirnya dapat dilalui karena adanya

arahan dan bimbingan dari berbagai pihak terkait baik secara moral maupun

spiritual. Maka diucapkan rasa hormat serta terima kasih kepada seluruh pihak

yang telah membantu. Pihak – pihak yang ada berkaitan dengan skripsi ini

diantaranya sebagai berikut :

1. Bapak Ir. Raymond Sunardi Oetama, M.C.I.S. selaku dosen pembimbing

yang telah memberikan bimbingan dan saran-saran yang diberikan selama

pengerjaan laporan skripsi.

2. Bapak Iwan Prasetiawan, S.Kom.,M.M. yang sudah mau berdiskusi serta

memberikan saran dalam penulisan laporan skripi.

3. Orangtua beserta keluarga yang telah memberikan doa dan dukungan

selama proses pembuatan laporan skripsi.

4. Teman-teman dikampus yaitu Lambe Negara serta Cristi ,Okta, Hansen,

Sandro dan Jenny yang selalu berbagi suka dan duka selama perkuliahan

vii

yang serta memberikan saran dan dukungan dalam proses pengerjaan

laporan skripsi.

5. Teman-teman di Sungai Pinyuh yaitu Ampit Girls yang selalu menemani

dan memberikan dukungan pembuatan laporan skripsi selama pandemi

COVID-19.

6. Kepada idola yaitu Hyungwon dan MONSTA X serta masih banyak lagi

yang selalu memberikan semangat dan dukungan dalam proses pengerjaan

laporan skripsi .

7. Serta kepada semua pihak yang tidak dapat diucapkan satu per satu yang

telah membantu serta memberikan dukungan dan semangat dalam

pembuatan laporan skripsi ini.

Disadari bahwa laporan skripsi ini memang masih jauh dari kata

kesempurnaan, tetapi penulisan skripsi ini telah dilakukan dan telah berusaha

semaksimal mungkin. Oleh karena itu, diharapkannya segala bentuk saran serta

masukan bahkan kritik yang membangun dari berbagai pihak. Sekali lagi

mengucapkan banyak terima kasih, semoga laporan ini bermanfaat bagi kita semua

dan terkhususnya bagi pembuat skripsi ini.

Tangerang, 05 Desember 2020

Michelle Prizcillya

viii

DAFTAR ISI

PERNYATAAN ....................................................................................................... i

HALAMAN PERSETUJUAN ................................................................................ ii

HALAMAN PENGESAHAN ................................................................................ iii

ABSTRAK ............................................................................................................. iv

ABSTRACT ............................................................................................................ v

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

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

DAFTAR TABEL .................................................................................................. xi

DAFTAR GAMBAR ............................................................................................ xii

DAFTAR RUMUS ............................................................................................... xv

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

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

1.2. Rumusan Masalah .................................................................................... 6

1.3. Batasan Masalah ....................................................................................... 6

1.4. Tujuan Penelitian ...................................................................................... 7

1.5. Manfaat Penelitian .................................................................................... 7

BAB II TINJAUAN PUSTAKA ............................................................................. 8

2.1. Penyakit Ginjal Kronis ............................................................................. 8

2.2. CRISP-DM ............................................................................................. 10

2.3. Feature selection .................................................................................... 13

2.3.1. Forward selection ........................................................................... 14

2.3.2. Backward elimination ..................................................................... 15

2.4. Algoritma K-Nearest Neighbour ............................................................ 16

ix

2.5. Algoritma Decision-Tree ........................................................................ 19

2.6. Algoritma Logistic regression ................................................................ 21

2.7. Tools / Alat Bantu Software ................................................................... 22

2.8. Penelitian Terdahulu ............................................................................... 22

BAB III METODOLOGI PENELITIAN.............................................................. 26

3.1 Gambaran Umum Objek Penelitian ....................................................... 26

3.2 Metode Penelitian ................................................................................... 26

3.2.1. Data Collection ............................................................................... 26

3.2.2. Variabel Independen ....................................................................... 28

3.2.3. Variabel Dependen .......................................................................... 28

3.3. Alur Penelitian ........................................................................................ 29

3.3.1. Business Understanding .................................................................. 30

3.3.2. Data Understanding ........................................................................ 30

3.3.3. Data Preparation ............................................................................ 31

3.3.4. Modeling ......................................................................................... 31

3.3.5. Evaluation ....................................................................................... 34

3.3.6. Deployment ..................................................................................... 34

3.3 Validasi Hasil ......................................................................................... 34

BAB IV ANALISIS DAN HASIL PENELITIAN ............................................... 36

4.1. Business Understanding Phase .............................................................. 36

4.2. Data Understanding Phase .................................................................... 36

4.3. Data Preparation Phase ......................................................................... 38

4.4. Modelling Phase ..................................................................................... 40

4.4.1. Membaca dataset PGK, melakukan data preparation dan melakukan

data cleansing. ............................................................................................... 42

x

4.4.2. Set role untuk memilih atribut yang akan digunakan dan splitting the

data. ..........................................................................................................44

4.4.3. Pembuatan Model............................................................................ 46

4.5. Evaluation Phase .................................................................................... 67

BAB V KESIMPULAN DAN SARAN ................................................................ 70

5.1 Kesimpulan ............................................................................................. 70

5.2 Saran ....................................................................................................... 72

DAFTAR PUSTAKA ........................................................................................... 73

LAMPIRAN .......................................................................................................... 78

xi

DAFTAR TABEL

Tabel 1.1. Tabel Perbandingan Penelitian Sebelumnya .......................................... 4

Tabel 1.1. Tabel Perbandingan Penelitian Sebelumnya .......................................... 5

Tabel 2.1. Tahapan Penyakit Ginjal Kronis ............................................................ 9

Tabel 2.2. Penelitian Sebelumnya ......................................................................... 23

Tabel 2.2. Penelitian Sebelumnya ......................................................................... 24

Tabel 2.2. Penelitian Sebelumnya ......................................................................... 25

Tabel 3.1. Tabel Dataset PGK ..............................................................................27

Tabel 4.1. Perbandingan Algoritma menggunakan teknik Feature selection ......67

Tabel 4.2. Tabel Akurasi PGK .............................................................................. 69

xii

DAFTAR GAMBAR

Gambar 2.1. Proses CRISP-DM ........................................................................... 11

Gambar 2.2. Flowchart Forward selection ........................................................... 14

Gambar 2.3. Flowchart Backward elimination. .................................................... 15

Gambar 2.4. flowchart K-NN ............................................................................... 18

Gambar 2.5. Rule Decision tree ............................................................................ 20

Gambar 3.1. Flowchart CRISP-DM ....................................................................29

Gambar 3.2. Flowchart Fase Modelling ............................................................... 32

Gambar 4.1. Syntax Perhitungan Flowchart ........................................................37

Gambar 4.2. Screenshot hasil perhitungan ............................................................ 38

Gambar 4.3. Syntax menghitung missing values ................................................... 38

Gambar 4.4. Screenshot hasil missing values ....................................................... 39

Gambar 4.5. Syntax mencari K di K-NN .............................................................. 40

Gambar 4. 6 Screenshot hasil pencarian K di K-NN ............................................ 41

Gambar 4.7. Flowchart Penelitian ........................................................................ 41

Gambar 4.8. Screenshot Retrieve Operator Rapid Miner .................................... 42

Gambar 4.9. Screenshot isi Operator Rapid Miner ............................................... 43

Gambar 4.10. Operator Filter Examples ............................................................... 43

Gambar 4.11. Operator set role dan split data ...................................................... 44

Gambar 4.12. Operator Set role ............................................................................ 45

Gambar 4.13. Operator Split data ......................................................................... 45

Gambar 4.14. Operator forward selection K-NN.................................................. 47

Gambar 4.15. Cross-validation K-NN .................................................................. 47

Gambar 4.16. isi dalam Cross-validation K-NN .................................................. 47

Gambar 4.17. Jumlah K dalam K-NN ................................................................... 48

Gambar 4.18. Tabel gambar hasil Atribut K-NN .................................................. 48

Gambar 4.19. Akurasi K-NN forward selection ................................................... 49

Gambar 4.20. Model K-NN forward selection ..................................................... 49

Gambar 4.21. Forward selection Decision tree .................................................... 49

Gambar 4.22. Cross-validation Decision tree ...................................................... 50

xiii

Gambar 4.23. isi cross-validation decision tree.................................................... 50

Gambar 4.24. Hasil forward selection decision tree ............................................. 51

Gambar 4.25. Hasil Akurasi forward selection decision tree ............................... 51

Gambar 4.26. Model forward selection decision tree ........................................... 52

Gambar 4.27. Operator Forward selection logistic regression............................. 52

Gambar 4.28. Cross-validation logistic regression .............................................. 52

Gambar 4.29. Isi cross-validation logistic regression .......................................... 53

Gambar 4.30. Hasil atribut forward selection logistic regression ........................ 53

Gambar 4.31. Hasil Akurasi forward selection logistic regression ...................... 54

Gambar 4.32. Model forward selection logistic regression .................................. 54

Gambar 4.33. Operator Backward elimination K-NN .......................................... 55

Gambar 4.34. Cross-validation K-NN .................................................................. 55

Gambar 4.35. Isi cross-validation K-NN .............................................................. 55

Gambar 4.36. parameter nilai K ............................................................................ 56

Gambar 4.37. Hasil atribut backward elimination K-NN ..................................... 56

Gambar 4.38. Hasil Akurasi backward elimination K-NN ................................... 57

Gambar 4.39. Model backward elimination K-NN .............................................. 57

Gambar 4.40. Backward elimination decision tree ............................................... 57

Gambar 4.41. Cross-validation Decision tree ...................................................... 58

Gambar 4.42. Isi cross-validation decision tree ................................................... 58

Gambar 4.43. Hasil atribut backward elimination decision tree .......................... 59

Gambar 4. 44. Hasil akurasi backward elimination decision tree ........................ 59

Gambar 4.45. Model backward elimination decision tree ................................... 60

Gambar 4.46. Backward elimination logistic regression ...................................... 60

Gambar 4.47. Cross-validation logistic regression .............................................. 61

Gambar 4.48. Isi dalam cross-validation logistic regression ............................... 61

Gambar 4.49. Hasil atribut backward ellimination logistic regression ................ 62

Gambar 4.50. Hasil Akurasi backward elimination logistic regression ............... 62

Gambar 4.51. Model backward elimination logistic regression ........................... 62

Gambar 4.52. Cross-validation K-NN .................................................................. 63

Gambar 4.53. Isi dari cross-validation K-NN ....................................................... 63

xiv

Gambar 4.54. Hasil Akurasi K-NN ....................................................................... 63

Gambar 4.55. Model K-NN .................................................................................. 64

Gambar 4.56. Cross-validation decision tree ....................................................... 64

Gambar 4.57. Isi dalam cross-validation decision tree ........................................ 65

Gambar 4.58. Hasil akurasi decision tree ............................................................. 65

Gambar 4.59. Model decision tree ........................................................................ 65

Gambar 4.60. Cross-validation logistic regression .............................................. 66

Gambar 4.61. Isi dalam logistic regression .......................................................... 66

Gambar 4.62. Hasil Akurasi logistic regression ................................................... 66

Gambar 4.63. Model logistic regression ............................................................... 67

xv

DAFTAR RUMUS

Rumus 2.1. Menghitung e-GFR .............................................................................. 9

Rumus 2.2. Menghitung euclidean distance ......................................................... 17

Rumus 2.3. Menghitung Gain Ratio ..................................................................... 20

Rumus 2.4. Rule Model Tree ................................................................................ 20

Rumus 2.5. Fungsi Classifier pada Logistic Regression ....................................... 21

Rumus 2.6. Model Persamaan Logistic Regression .............................................. 22