TEKNIK DATA MINING PADA PREDIKSI

KLASIFIKASI JENIS PENYAKIT ANEMIA

SKRIPSI

Diajukan Guna Memenuhi Persyaratan Memperoleh Gelar Sarjana Komputer (S.Kom.)

Dita Amalia

00000025849

PROGRAM STUDI SISTEM INFORMASI

FAKULTAS TEKNIK DAN INFORMATIKA

UNIVERSITAS MULTIMEDIA NUSANTARA

TANGERANG

2021

ii

PERNYATAAN

iii

HALAMAN PERSETUJUAN

Skripsi dengan judul

“Teknik Data Mining Pada Prediksi Klasifikasi Jenis Penyakit Anemia”

oleh

Dita Amalia

telah disetujui untuk diajukan pada

Sidang Ujian Skripsi Universitas Multimedia Nusantara

Tangerang, 7 Juni 2021

Menyetujui,

Dosen Pembimbing 1 Dosen Pembimbing 2

Johan Setiawan, S.Kom., M.M., M.B.A. Iwan Prasetiawan, S.Kom., M.M.

Ketua Program Studi

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

iv

HALAMAN PENGESAHAN

Skripsi dengan judul

“Teknik Data mining Pada Prediksi Klasifikasi Jenis Penyakit Anemia”

oleh

Dita Amalia

telah diujikan pada hari Rabu, 16 Juni 2021,

pukul 08.30 s.d 10.00 dan dinyatakan lulus

dengan susunan penguji sebagai berikut.

Ketua Sidang Penguji

Wella, S.Kom., M.MSI. Friska Natalia, Ph.D.

Dosen Pembimbing 1 Dosen Pembimbing 2

Johan Setiawan, S.Kom., M.M., M.B.A. Iwan Prasetiawan, S.Kom., M.M.

Ketua Program Studi

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

v

TEKNIK DATA MINING PADA PREDIKSI

KLASIFIKASI JENIS PENYAKIT ANEMIA

ABSTRAK

Oleh : Dita Amalia

Anemia dapat dicegah dan ditangani berdasarkan jenis-jenis penyakit anemia

untuk menyesuaikan kebutuhan dan perawatan nya. Anemia juga dapat diprediksi

berdasarkan faktor utama yang dapat mendeteksi penyakit ini yaitu data Complete

Blood Count. Penderita anemia dapat dianalisa dengan memanfaatkan data

demografi untuk melakukan mitigasi lebih lanjut terkait data penderitanya.

Untuk menanggulangi masalah tersebut, maka dilakukan pembuatan model

prediksi klasifikasi data mining dengan memanfaatkan framework CRISP-DM

yang terdiri dari 6 tahapan. Penelitian ini membandingkan tiga algoritma

supervised learning yaitu Naïve Bayes, J48 Decision Tree, dan Random Forest

dengan menggunakan tools RapidMiner. Penilaian performa algoritma paling

optimal dilihat berdasarkan hasil akurasi, rata-rata sensitivitas dan rata-rata

presisi.

Hasil yang didapatkan setelah melakukan perbandingan pembuatan model

menyimpulkan bahwa J48 Decision Tree mempunyai hasil yang paling optimal,

dilanjutkan dengan Random Forest dan Naïve Bayes. Hasil analisis jumlah jenis

anemia tertinggi ada pada jenis anemia of reneal disease dan anemia kronis

dengan persentase terbesar ada pada jenis kelamin wanita.

Kata Kunci : Anemia, Data mining, J48 Decision Tree, Naïve Bayes, Random

Forest

vi

DATA MINING TECHNIQUES IN PREDICTION

CLASSIFICATION TYPES OF ANEMIA DISEASE

ABSTRACT

By : Dita Amalia

Anemia can be prevented and treated based on the types of anemia to suit their

needs and treatments. Anemia can also be predicted based on the main factor that

can detect this disease, namely Complete Blood Count data. Anemia sufferers can

be analyzed by utilizing demographic data to further mitigate the patient's data.

To solve the problem, the data mining classification prediction model is done by

utilizing CRISP-DM framework consisting of 6 phases. This study compared three

supervised learning algorithms namely Naïve Bayes, J48 Decision Tree, and

Random Forest using RapidMiner tools. The most optimal algorithm performance

assessment is based on accuracy, mean recall, and mean precision results.

The results obtained after comparing the creation of the model concluded that J48

Decision Tree had the most optimal results, followed by Random Forest and

Naïve Bayes. The results of the analysis of the highest number of types of anemia

are in the type of anemia of reneal disease and chronic anemia with the largest

percentage present in the female sex.

Keywords: Anemia, Data mining, J48 Decision Tree, Naïve Bayes, Random

Forest

vii

KATA PENGANTAR

Puji dan syukur kepada Tuhan Yang Masa Esa atas segala rahmat-Nya

sehingga skripsi yang berjudul “Teknik Data mining Pada Prediksi Klasifikasi

Jenis Penyakit Anemia” dapat selesai tepat pada waktunya. Skripsi ini penulis

ajukan kepada Program Strata 1, Program Studi Sistem Informasi, Fakultas

Teknik dan Informatika, Universitas Multimedia Nusantara. Setelah melewati

proses penyusunan skripsi ini dengan segala hambatan dan rintangan yang

dihadapi, penulis menyadari dalam penyusunan skripsi ini tidak akan selesai tanpa

bantuan dari berbagai pihak. Maka dari itu pada kesempatan ini penulis ingin

mengucapkan rasa terima kasih sebesar-besarnya kepada :

1. Bapak Johan Setiawan, S.Kom., M.M., M.B.A. yang telah memberikan

bimbingan, pedoman penulisan skipsi, dan saran-saran yang diberikan

kepada penulis selama pengerjaan skripsi,

2. Bapak Iwan Prasetiawan, S.Kom., M.M. yang telah memberikan

bimbingan, berdiskusi, dan senantiasa membantu penulis dalam

mengarahkan terkait materi skipsi,

3. Bapak Yanwira, Ibu Lilis, Kakak Nadira, Adik Adi, Adik Zui, keluarga,

dan saudara semua yang senantiasa mendoakan daniimendukung penulis

dalam menyelesaikaniiskripsi,

4. Sahabat tercinta dan teman-teman penulis yang selalu memberikan

semangat, dukungan, saran, bantuan, serta doa selama penyusunan skripsi

ini berlangsung.

viii

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

telah terlibat dalam pembuatan laporan skripsi ini.

Penulis sudah berusaha sebaik mungkin dalam menyusun skripsi ini, namun

penulis jugaooomenyadari bahwa skripsi ini masihiiiada kekurangan dalam

penulisannya. Maka dari itu diharapkan kritik serta saran yang membangun dari

berbagai pihak. Semoga skripsi yang dibuat oleh penulis dapat memberikan

wawasan dan inspirasi yang bermanfaatiibagi para pembaca.

Tangerang, 7 Juni 2021

Dita Amalia

ix

DAFTAR ISI

PERNYATAAN...................................................................................................... ii

HALAMAN PERSETUJUAN ............................................................................... iii

HALAMAN PENGESAHAN................................................................................ iv

ABSTRAK ...............................................................................................................v

ABSTRACT ............................................................................................................. vi

KATA PENGANTAR .......................................................................................... vii

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

DAFTAR TABEL ................................................................................................. xii

DAFTAR GAMBAR ........................................................................................... xiii

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

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

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

1.2. Rumusan Masalah .................................................................................... 4

1.3. Batasan Masalah ....................................................................................... 5

1.4. Tujuan Penelitian ...................................................................................... 5

1.5. Manfaat Penelitian .................................................................................... 6

BAB II TINJAUAN PUSTAKA..............................................................................7

2.1. Anemia ..................................................................................................... 7

2.2. Jenis-jenis Anemia ................................................................................... 7

2.2.1. Anemia Aplastik ................................................................................ 7

2.2.2. Anemia Kronis ................................................................................... 8

2.2.3. Iron Deficiency Anemia..................................................................... 8

2.2.4. Thalassemia ...................................................................................... 9

x

2.2.5. Anemia of Renal Disease .................................................................. 9

2.3. Data mining .............................................................................................. 9

2.3.1. Teknik Data mining ........................................................................ 10

2.3.2. CRISP-DM ...................................................................................... 12

2.4. Naïve Bayes ............................................................................................ 14

2.5. J48 Decision Tree ................................................................................... 14

2.6. Random Forest ....................................................................................... 15

2.7. Akurasi ................................................................................................... 16

2.8. Sensitivitas .............................................................................................. 16

2.9. Presisi ..................................................................................................... 17

2.10. RapidMiner ......................................................................................... 17

2.11. Penelitian Terdahulu ........................................................................... 18

BAB III METODOLOGI PENELITIAN ..............................................................20

3.1. Gambaran Umum Objek Penelitian ....................................................... 20

3.2. Metode Penelitian ................................................................................... 20

3.3. Teknik Pengumpulan Data ..................................................................... 25

3.3.1. Data Collection ............................................................................... 25

3.3.2. Variabel Penelitian .......................................................................... 27

3.4. Teknik Analisis Data .............................................................................. 28

3.4.1. Business Undertanding ................................................................... 28

3.4.2. Data Understanding ........................................................................ 29

3.4.3. Data Preparation ............................................................................ 29

3.4.4. Data Modeling ................................................................................ 30

3.4.5. Evaluation ....................................................................................... 32

3.4.6. Deployment ..................................................................................... 32

xi

BAB IV ANALISIS DAN HASIL PENELITIAN ...............................................33

4.1. Business Understanding Phase .............................................................. 33

4.2. Data Understanding Phase .................................................................... 34

4.3. Data Preparation Phase ......................................................................... 36

4.3.1. Data Cleansing ............................................................................... 41

4.3.2. Set Parameter .................................................................................. 42

4.3.3. Split Data ........................................................................................ 44

4.4. Modeling Phase ...................................................................................... 44

4.4.1. Modeling Naïve Bayes..................................................................... 45

4.4.2. .Modeling J48 Decision Tree .......................................................... 49

4.4.3. Modeling Random Forest ................................................................ 52

4.5. Evaluation Phase .................................................................................... 57

4.6. Result and Discussion ............................................................................ 60

BAB V KESIMPULAN DAN SARAN.................................................................66

5.1. Kesimpulan ............................................................................................. 66

5.2. Saran ....................................................................................................... 68

DAFTAR PUSTAKA ............................................................................................69

DAFTAR LAMPIRAN ..........................................................................................73

xii

DAFTAR TABEL

Tabel 2.1. Deskripsi Phase CRISP-DM ................................................................ 13

Tabel 2.2. Perbandingan Penelitian Terdahulu ..................................................... 18

Tabel 3.1. Perbandingan Teknik data mining ....................................................... 21

Tabel 3.2. Perbandingan Tools Data mining [25], [26] ........................................ 22

Tabel 3.3. Tabel Perbandingan Penggunaan Algoritma........................................ 24

Tabel 3.4. Atribut data Complete Blood Count NHANES.................................... 26

Tabel 4.1. Deskripsi Statistik Atribut ................................................................... 39

Tabel 4.2. Confusion Matrix Naïve Bayes ............................................................ 57

Tabel 4.3. Confusion Matrix J48 Decision Tree ................................................... 58

Tabel 4.4. Confusion Matrix Random Forest........................................................ 58

Tabel 4.5. Perbandingan Hasil Performance Algoritma ....................................... 60

Tabel 4.6. Perbandingan Hasil Penelitian dengan Penelitian Terdahulu .............. 61

xiii

DAFTAR GAMBAR

Gambar 2.1. Tahapan Proses CRISP-DM [19] ..................................................... 12

Gambar 3.1. Logo CDC (Centers For Disease Control anad Prevention) [28]..... 25

Gambar 3.2 Alur Penelitian CRISP-DM [19] ....................................................... 28

Gambar 3.3. Flowchart Modeling Phase .............................................................. 31

Gambar 4.1. Tiga Kategori Data NHANES .......................................................... 35

Gambar 4.2. Merge Data TurboPrep .................................................................... 36

Gambar 4.3. Tampilan Stat/Trasnfer .................................................................... 37

Gambar 4.4. Operator Retrieve ............................................................................. 37

Gambar 4.5. Data Preparation Operator .............................................................. 38

Gambar 4.6. Select Attributes................................................................................ 38

Gambar 4.7. Visualisasi Distribusi Data Atribut Numerik ................................... 40

Gambar 4.8.Visualisasi Distribusi Data Atribut Kategorial.................................. 41

Gambar 4.9. Missing Value Atribut ...................................................................... 41

Gambar 4.10. Operator Filter Examples ............................................................... 42

Gambar 4.11. Operator Generate Attribute .......................................................... 43

Gambar 4.12. Operator Set Role ........................................................................... 43

Gambar 4.13. Screenshot Data Training dan Data Testing .................................. 44

Gambar 4.14. Operator Modeling Proses ............................................................. 45

Gambar 4.15. Proses Cross-validation Naïve Bayes ............................................ 46

Gambar 4.16. Parameter Operator Model Naïve Bayes ....................................... 46

Gambar 4.17. Hasil Confidence Naïve Bayes ...................................................... 47

Gambar 4.18. Hasil Performance Akurasi Naïve Bayes ...................................... 47

Gambar 4.19. Grafik Simple Distribusi Atribut Naïve Bayes ............................... 48

Gambar 4.20. Proses Cross-validation J48 Decision Tree .................................. 49

Gambar 4.21. Parameter Operator Model J48 Decision Tree .............................. 49

Gambar 4.22. Hasil Confidence J48 Decision Tree ............................................. 50

Gambar 4.23. Hasil Performance Akurasi J48 Decision Tree............................. 51

Gambar 4.24. Hasil Tree J48 Decision Tree ......................................................... 52

Gambar 4.25. Deskripsi Tree J48 Decision Tree .................................................. 52

xiv

Gambar 4.26. Proses Cross-validation Random Forest ....................................... 53

Gambar 4.27. Parameter Operator Model Random Forest .................................. 54

Gambar 4.28. Hasil Confidence Random Forest ................................................. 54

Gambar 4.29. Hasil Performance Akurasi Random Forest ................................. 55

Gambar 4.30. Hasil Pohon Random Forest .......................................................... 55

Gambar 4.31. Deskripsi Pohon Random Forest.................................................... 56

Gambar 4.32. ROC Curve Jenis-jenis Anemia...................................................... 59

Gambar 4.33. Visualisasi Analisis Anemia Berdasarkan Gender......................... 62

Gambar 4.34. Visualisasi Analisis Anemia Berdasarkan Usia ............................. 63

Gambar 4.35. Visualisasi Analisis Anemia Berdasarkan Tinggi Badan dan Berat

Badan..................................................................................................................... 64

Gambar 4.36. Visualisasi Analisis Anemia Berdasarkan Race/origin .................. 64

Gambar 4.37. Visualisasi Analisis Anemia Berdasarkan Citizen ......................... 65

xv

DAFTAR RUMUS

Rumus 2.1 Akurasi ................................................................................................ 16

Rumus 2.2. Sensitivitas ......................................................................................... 16

Rumus 2.3. Presisi ................................................................................................. 17