KLASIFIKASI PNEUMONIA PADA ANAK DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK
Laporan Tugas Akhir
Diajukan Untuk Memenuhi Persyaratan Guna Meraih Gelar Sarjana Informatika Universitas Muhammadiyah Malang
Feranandah Firdausi 201710370311052
Data Science
PROGRAM STUDI INFORMATIKA FAKULTAS TEKNIK
UNIVERSITAS MUHAMMADIYAH MALANG 2021
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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 PNEUMONIA PADA ANAK DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK”
Di dalam tulisan ini disajikan pokok – pokok bahasan yang meliputi latar belakang, metode penelitian, dan hasil dan pembahasan yang telah didapat dari penelitian ini dan telah disimpulkan berdasarkan hasil yang telah didapat oleh peneliti.
Peneliti menyadari sepenuhnya bahwa dalam penulisan tugas akhir ini masih banyak kekurangan dan keterbatasan. Oleh karena itu peneliti mengharapkan saran yang membangun agar tulisan ini bermanfaat bagi perkembangan ilmu pengetahuan.
Malang, 25 Juni 2021
Penulis
ix DAFTAR ISI
LEMBAR PERSETUJUAN ... i
LEMBAR PENGESAHAN ... ii
LEMBAR PERNYATAAN ... iii
ABSTRAK ... iv
ABSTRACT ... v
LEMBAR PERSEMBAHAN ... vi
KATA PENGANTAR ... viii
DAFTAR ISI ... ix
DAFTAR GAMBAR ... xii
DAFTAR TABEL ... xiv
BAB I ... 1
1.1 Latar Belakang ... 1
1.2 Rumusan Masalah ... 3
1.3 Tujuan Penelitian ... 3
1.4 Batasan Masalah ... 3
BAB II ... 4
2.1 Studi Literatur ... 4
2.2 Pneumonia ... 4
2.3 Convolutional Neural Network ... 5
2.3.1 Convolutional Layer ... 5
2.3.2 Pooling Layer ... 5
2.3.3 Batch Normalization ... 6
2.3.4 Dropout Layer ... 6
2.3.5 Fully Connected Layer ... 7
2.4 Uji Klasifikasi ... 7
x
2.5 Arsitektur CNN pada Penelitian Sebelumnya ... 8
Arsitektur CNN Model 1 – Jain et al ... 8
Arsitektur CNN Model 2 – Jain et al ... 9
Arsitektur CNN – Raheel Siddiqi ... 10
BAB III ... 12
3.1 Identifikasi Masalah ... 13
3.2 Dataset ... 13
3.3 Implementasi dan Pengujian CNN ... 14
Data Preprocessing ... 15
Build Model ... 16
Pengujian ... 21
3.4 Evaluasi Model ... 21
BAB IV ... 22
4.1 Implementasi ... 22
4.2 Load Dataset ... 22
4.3 Data Augmentasi ... 23
4.4 Perancangan Model CNN ... 24
4.5 Pelatihan Model CNN ... 25
4.6 Grafik dan Performa Model CNN ... 25
4.7 Evaluasi Model ... 26
4.8 Skenario Pengujian ... 26
4.8.1 Skenario Pengujian 1 ... 27
4.8.2 Skenario Pengujian 2 ... 29
4.8.3 Skenario Pengujian 3 ... 32
4.8.4 Skenario Pengujian 4 ... 35
4.9 Perbandingan Performa Model CNN ... 38
4.10 Perbandingan Performa Model dengan Penelitian Rujukan ... 40
xi
4.11 Analisa Output ... 44
BAB V ... 46
5.1 Kesimpulan ... 46
5.2 Saran ... 46
DAFTAR PUSTAKA ... 47
xii
DAFTAR GAMBAR
Gambar 1. Convolutional Layer ... 5
Gambar 2. Max-pooling Layer ... 6
Gambar 3. (a) Proses training tanpa dropout layer dan (b) Proses training dengan dropout layer ... 7
Gambar 4. Tahap Penelitian ... 12
Gambar 5. Sampel X-ray Pneumonia ... 13
Gambar 6. Sampel X-ray Normal ... 14
Gambar 7. Perbedaan kondisi pulmonary pada pneumonia dan normal ... 14
Gambar 8. Arsitektur Sistem ... 15
Gambar 9. Source code untuk mendefinisikan path dan membuat array ... 22
Gambar 10. Source code untuk memuat dataset ... 23
Gambar 11. Hasil load dataset ... 23
Gambar 12. Source code augmentasi data pada data train ... 24
Gambar 13. Perancangan model awal ... 24
Gambar 14. Source code pelatihan model CNN ... 25
Gambar 15. Source code akurasi dan loss model ... 26
Gambar 16. Source code confusion matrix ... 26
Gambar 17. Proposed Model 1 ... 27
Gambar 18. Grafik Akurasi Model 1 ... 28
Gambar 19. Grafik Loss Model 1 ... 28
Gambar 20. Confusion Matrix Model 1 ... 29
Gambar 21. Proposed Model 2 ... 30
Gambar 22. Akurasi Model 2 ... 30
Gambar 23. Loss Model 2 ... 31
Gambar 24. Confusion Matrix Model 2 ... 32
Gambar 25. Proposed Model 3 ... 33
Gambar 26. Akurasi Model 3 ... 33
Gambar 27. Loss Model 3 ... 34
Gambar 28. Confusion Matrix Model 3 ... 35
Gambar 29. Proposed Model 4 ... 36
Gambar 30. Akurasi Model 4 ... 37
xiii
Gambar 31. Loss Model 4 ... 37 Gambar 32. Confusion Matrix Model 4 ... 38
xiv
DAFTAR TABEL
Tabel 1. Confusion Matrix ... 7
Tabel 2. Arsitektur CNN model 1 – Jain et al ... 9
Tabel 3. Arsitektur CNN Model 2 – Jain et al ... 9
Tabel 4. Arsitektur CNN model 2 – Raheel Siddiqi ... 10
Tabel 5. Augmentasi data yang digunakan ... 16
Tabel 6. Arsitektur model 1 yang diusulkan ... 17
Tabel 7. Arsitektur model 2 yang diusulkan ... 17
Tabel 8. Arsitektur model 3 yang diusulkan ... 18
Tabel 9. Arsitektur model 4 yang diusulkan ... 19
Tabel 10. Ringkasan perbedaan arsitektur model yang diusulkan ... 21
Tabel 11. Perbandingan Performa Model CNN ... 39
Tabel 12. Perbandingan Hasil Penelitian dengan Jain et al antara model 1 dengan proposed model 1 dan proposed model 2... 41
Tabel 13. Perbandingan Hasil Penelitian dengan Jain et al antara model 2 dengan proposed model 3 ... 41
Tabel 14. Perbandingan Hasil Penelitian dengan Jain et al ... 42
Tabel 15. Perbandingan Hasil Penelitian dengan Raheel Siddiqi ... 43
Tabel 16. Perbandingan Performa Model dengan Penelitian Rujukan ... 43
47
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