Klasifikasi Status Mikrosatelit Pada Sel Kanker ...

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Klasifikasi Status Mikrosatelit Pada Sel Kanker Gastrointestinal

Menggunakan Algoritma Convolutional Neural Networks

Laporan Tugas Akhir

Diajukan Untuk Memenuhi

Persyaratan Guna Meraih Gelar Sarjana

Informatika Universitas Muhammadiyah Malang

Muhammad Rifal Alfarizy

201710370311219

Bidang Minat

Data Sains

PROGRAM STUDI INFORMATIKA

FAKULTAS TEKNIK

UNIVERSITAS MUHAMMADIYAH MALANG

2021

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LEMBAR PERSETUJUAN

Klasifikasi Status Mikrosatelit Pada Sel Kanker Gastrointestinal

Menggunakan Algoritma Convolutional Neural Networks

TUGAS AKHIR

Sebagai Persyaratan Guna Meraih Gelar Sarjana Strata Ⅰ

Informatika Universitas Muhammadiyah Malang

Menyetujui,

Malang, 26 Juni 2021

Pembimbing Ⅰ Pembimbing Ⅱ

Agus Eko Minarno, S.Kom., M.Kom.

NIDN: 0729118203

Yufis Azhar, S.Kom.,M.Kom.

NIDN: 0728088701

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KATA PENGANTAR

Dengan memanjatkan puji syukur kehadirat Allah SWT. Atas limpahan rahmat

dan hidayah-NYA sehingga peneliti dapat menyelesaikan tugas akhir yang berjudul

“KLASIFIKASI STATUS MIKROSATELIT PADA SEL KANKER

GASTROINTESTINAL MENGGUNAKAN ALGORITMA Convolutional

Neural Networks”

Di dalam tulisan ini disajikan pokok-pokok bahasan yang meliputi pengaruh

model yang diusulkan, teknik augmentasi yang diusulkan dan modifikasi

penempatan dan jumlah layer dropout terhadap klasifikasi data status mikrosatelit

sel kanker gastrointestinal dengan menggunakan algoritma CNN.

Peneliti menyadari sepenuhnya bahwa dalam penulisan tugas akhir ini masih

banyak kekurangan dan keterbatasan. Oleh sebab itu peneliti mengharapkan saran

yang membangun agar tulisan ini bermanfaat bagi perkembangan ilmu

pengetahuan.

Malang, 26 Juni 2021

Muhammad Rifal Alfarizy

ix

DAFTAR ISI

HALAMAN JUDUL

LEMBAR PERSETUJUAN ................................................................................... ii

LEMBAR PENGESAHAN ................................................................................... iii

LEMBAR PERNYATAAN ................................................................................... iv

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

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

LEMBAR PERSEMBAHAN ............................................................................... vii

KATA PENGANTAR ......................................................................................... viii

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

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

DAFTAR TABEL ................................................................................................ xiv

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

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

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

1.3. Tujuan Penelitian .......................................................................................4

1.4. Batasan Masalah ........................................................................................4

BAB Ⅱ TINJAUAN PUSTAKA ..............................................................................6

2.1. Studi Literatur ...........................................................................................6

2.2. Microsatellite Instability ...........................................................................7

2.3. Convolutional Neural Networks ................................................................7

2.6.1. Input Layer .........................................................................................8

2.6.2. Convolutional Layer ..........................................................................8

2.6.3. Batch Normalization Layer ................................................................9

2.6.4. Pooling Layer .....................................................................................9

2.6.5. Dropout Layer ..................................................................................10

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2.6.6. Fully Connected Layer .....................................................................10

2.4. VGG19 ....................................................................................................11

2.5. Pengujian Klasifikasi Model ...................................................................11

BAB Ⅲ METODE PENELITIAN .........................................................................14

3.1. Tahapan Penelitian ..................................................................................14

3.2. Lingkungan Kerja ....................................................................................15

3.1. Dataset .....................................................................................................15

3.3.1. Pembagian Dataset ...........................................................................16

3.4. Preprocessing ..........................................................................................16

3.4.1. Augmentasi Data ..............................................................................16

3.5. Hyperparameter Tuning ..........................................................................17

3.6. Model Arsitektur .....................................................................................17

3.7. Skenario Pengujian ..................................................................................19

BAB Ⅵ HASIL DAN PEMBAHASAN ...............................................................20

4.1. Augmentasi Data .....................................................................................20

4.2. Hyperparameter Tuning ..........................................................................21

4.3. Pengujian Data Sel Kanker Usus ............................................................24

4.3.1. Skenario 1 Model Usulan .................................................................25

4.3.2. Skenario 2 Model Usulan + Augmentasi .........................................27

4.3.3. Skenario 3 Model Usulan + Augmentasi + Dropout APL ...............28

4.3.4. Evaluasi Hasil ..................................................................................30

4.4. Pengujian Data Sel Kanker Lambung .....................................................36

4.4.1. Evaluasi Hasil ..................................................................................38

4.5. Perbandingan Hasil .................................................................................40

BAB Ⅴ KESIMPULAN .........................................................................................43

5.1. Kesimpulan ..............................................................................................43

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5.2. Saran ........................................................................................................44

DAFTAR PUSTAKA ............................................................................................45

LAMPIRAN ...........................................................................................................50

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DAFTAR GAMBAR

Gambar 1. Proses Konvolusi ....................................................................................8

Gambar 2. Max Pooling ...........................................................................................9

Gambar 3. Average Pooling .....................................................................................9

Gambar 4. Proses Dropout .....................................................................................10

Gambar 5. Struktur Model VGG19 .......................................................................11

Gambar 6. Grafik AUC-ROC ................................................................................13

Gambar 7. Diagram Alur Penelitian ......................................................................14

Gambar 8. Sample Data Sel Kanker Usus .............................................................15

Gambar 9. Sample Data Sel Kanker Lambung ......................................................15

Gambar 10. Source Code Augmetnasi Data ..........................................................20

Gambar 11. Hasil Augmentasi Data ......................................................................20

Gambar 12. Source Code Hyperparameter Tuning ................................................23

Gambar 13. Source Code Parameter Pengujian Model COAD .............................25

Gambar 14. Source Code Struktur Model Skenario 1 COAD ...............................26



Gambar 17. Source Code Grafik Akurasi dan Loss COAD ..................................30

Gambar 18. Grafik Skenario 1 COAD, (a) Grafik Akurasi dan (b) Grafik Loss ...31



Gambar 21. Source Code Model Evaluate COAD ................................................32

Gambar 22. Source Code Confusion Matrix COAD .............................................33

Gambar 23. Hasil Confusion Matrix COAD .........................................................33

Gambar 24. Source Code Classification Report COAD ........................................33

Gambar 25. Source Code Grafik Nilai AUCROC COAD .....................................34

Gambar 26. Grafik AUC Skenario Dataset COAD. (a) Skenario 1, (b) Skenario 2

dan (c) Skenario 3 ..................................................................................................35

Gambar 27. Source Code List Callbacks ...............................................................37

Gambar 28. Source Code Grafik Akurasi dan Loss STAD ...................................38

Gambar 29. Grafik Skenario STAD, (a) Grafik Akurasi dan (b) Grafik Loss .......39

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Gambar 30. Hasil Confusion Matrix STAD ..........................................................39

Gambar 31. Grafik Nilai AUCROC STAD ...........................................................39

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DAFTAR TABEL

Tabel 1. Penelitian Terdahulu yang Sejenis ............................................................ 6

Tabel 2. Confusion Matrix .................................................................................... 12

Tabel 3. Detail Pembagian Dataset ....................................................................... 16

Tabel 4. Parameter Teknik Augmentasi ................................................................ 17

Tabel 5. Parameter pembanding untuk Hyperparameter Tuning .......................... 17

Tabel 6. Rancangan Arsitektur Model .................................................................. 18

Tabel 7. Hasil Hyperparameter Tuning ............................................................... 23

Tabel 8. Rangkuman Hasil Klasifikasi Dataset COAD ........................................ 35

Tabel 9. Rangkuman Hasil Klasifikasi Dataset STAD ......................................... 40

Tabel 10. Perbandingan Penelitian Data COAD ................................................... 40

Tabel 11. Perbandingan Penelitian Data STAD ................................................... 41

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