Klasifikasi Jenis Batik Menggunakan Algoritma...

Klasifikasi Jenis Batik Menggunakan Algoritma Convolutional

Neural Network (CNN)

Tugas Akhir

Diajukan Untuk Memenuhi

Persyaratan Guna Meraih Gelar Sarjana

Informatika Universitas Muhammadiyah Malang

Yesicha Amilia Putri

(201510370311144)

Data Science

PROGRAM STUDI INFORMATIKA

FAKULTAS TEKNIK

UNIVERSITAS MUHAMMADIYAH MALANG

2020

vii

KATA PENGANTAR

Puji dan syukur kehadirat Allah SWT yang telah memberikan rahmat dan

karuniaNya kepada penulis, sehingga penulis dapat menyelesaikan Tugas Akhir

yang berjudul:

“KLASIFIKASI JENIS BATIK MENGGUNAKAN ALGORITMA

CONVOLUTIONAL NEURAL NETWORK (CNN)”

Tidak lupa shalawat dan salam senantiasa tercurah kepada Rasulullah SAW

yang mengantarkan manusia dari jaman kegelapan ke jaman yang terang

benderang. Penyusunan Tugas Akhir ini dimaksudkan untuk memenuhi sebagian

syarat-syarat guna mencapai gelar Sarjana Informatika di Universitas

Muhammadiyah Malang.

Dalam kesempatan ini, penulis menghaturkan banyak terima kasih kepada

semua pihak yang telah membantu menyumbangkan ide dan pikirannya demi

terwujudnya makalah ini.

Penulis menyadari bahwa Tugas Akhir ini masih jauh dari sempurna

dikarenakan terbatasnya pengalaman dan pengetahuan yang dimiliki penulis. Oleh

karena itu, penulis mengharapkan segala bentuk saran serta masukan bahkan kritik

yang membangun dari berbagai pihak. Semoga Tugas Akhir ini dapat bermanfaat

bagi pembaca dan semua pihak khususnya dalam bidang data science.

Malang, 14 Januari 2020

Penulis

Yesicha Amilia Putri

viii

DAFTAR ISI

HALAMAN JUDUL ........................................................................................... i

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

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

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

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

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

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

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

DAFTAR GAMBAR .......................................................................................... x

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

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

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

1.2 Rumusan Masalah ..................................................................................... 3

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

1.4 Cakupan Masalah ...................................................................................... 4

BAB II TINJAUAN PUSTAKA ........................................................................ 5

2.1 Studi Literatur ........................................................................................... 5

2.2 Batik .......................................................................................................... 7

2.3 Klasifikasi ................................................................................................. 8

2.4 Algoritma Convolutional Neural Network ................................................. 8

2.4.1 Convolutional Layer ............................................................................ 8

2.4.2 Pooling Layer ...................................................................................... 9

2.4.3 Fully Connected Layer ...................................................................... 10

2.5 Keras ....................................................................................................... 10

2.6 Model VGG16 ......................................................................................... 11

2.7 Uji Klasifikasi ......................................................................................... 11

BAB III METODE PENELITIAN .................................................................. 13

3.1 Identifikasi Masalah ................................................................................ 13

3.2 Analisa Sistem ......................................................................................... 13

3.2.1 Analisa Kebutuhan user ..................................................................... 13

3.2.2 Analisa Kebutuhan Perangkat Lunak ................................................. 13

3.2.3 Lingkungan Pengembangan............................................................... 13

3.3 Dataset .................................................................................................... 14

3.4 Deskripsi Sistem ...................................................................................... 14

ix

3.5 Skenario Pengujian .................................................................................. 15

3.5.1 Skenario 1 ......................................................................................... 15

3.5.2 Skenario 2 ......................................................................................... 16

3.5.3 Skenario 3 ......................................................................................... 16

3.6 Perancangan Model ................................................................................. 17

BAB IV HASIL DAN PEMBAHASAN........................................................... 20

4.1 Implementasi ........................................................................................... 20

4.2 Data Train dan Data Test ......................................................................... 20

4.3 Algoritma CNN ....................................................................................... 20

4.3.1 Ekstraksi Layer ................................................................................. 20

4.3.2 Fully Connected Layer ...................................................................... 21

4.4 Evaluasi dan Pengujian............................................................................ 22

4.4.1 Pengujian 1 ....................................................................................... 23

4.4.2 Pengujian 2 ....................................................................................... 23

4.4.3 Pengujian 3 ....................................................................................... 24

BAB V PENUTUP ........................................................................................... 28

5.1 Kesimpulan ............................................................................................. 28

5.2 Saran ....................................................................................................... 28

DAFTAR PUSTAKA ....................................................................................... 29

x

DAFTAR GAMBAR

Gambar 2.1 Convolutional Layer ........................................................................ 9

Gambar 2.2 Pooling Layer .................................................................................. 9

Gambar 2.3 Fully Connected Layer ................................................................... 10

Gambar 3.1 Dataset Batik ................................................................................. 14

Gambar 3.2 Arsitektur Sistem ........................................................................... 15

Gambar 3.3 Skenario Pengujian 1 ..................................................................... 16



Gambar 3.6 Rancangan Model .......................................................................... 18

Gambar 3.7 Sebelum Dropout ........................................................................... 19

Gambar 3.8 Setelah Dropout ............................................................................. 19

Gambar 4.1 Ekstraksi Layer .............................................................................. 20

Gambar 4.2 Fully Connected Layer ................................................................... 21

Gambar 4.3 Output Model ................................................................................ 22

Gambar 4.4 Proses Train Model ........................................................................ 22

Gambar 4.5 Metode Optimizer .......................................................................... 23

Gambar 4.6 Nilai Loss Uji 4 Tanpa Dropout ..................................................... 25

Gambar 4.7 Nilai Performa ............................................................................... 25

Gambar 4.8 Output Nilai Performa.................................................................... 26

xi

DAFTAR TABEL

Tabel 2.1 Perbedaan Penelitian ........................................................................... 6

Tabel 2.2 Confusion Matrix .............................................................................. 11

Tabel 4.1 Hasil Pengujian Skenario 1 ................................................................ 23



29

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