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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
AKHIRSebagai 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
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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 15. Source Code Struktur Model Skenario 2 COAD ...28
Gambar 16. Source Code Struktur Model Skenario 3 COAD ...30
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 19. Grafik Skenario 2 COAD, (a) Grafik Akurasi dan (b) Grafik Loss ...31
Gambar 20. Grafik Skenario 3 COAD, (a) Grafik Akurasi dan (b) Grafik Loss ...32
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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