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Perbandingan Algoritma SVM Dan CNN Untuk Klasifikasi Citra Batik Nitik
https://doi.org/10.22146/ijeis.105658 Imam Fatrawijaya(1), Dyah Aruming Tyas(2*)
(1) Program Studi Elektronika dan Instrumentasi, FMIPA, UGM, Yogyakarta
(2) Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author
Abstract
Di Indonesia, berbagai motif batik tersebar di seluruh daerah sehingga menyulitkan untuk mengidentifikasi motif-motif tersebut. Kesalahan dalam klasifikasi motif batik akan menyebabkan misinformasi sehingga informasi tentang motif batik tidak tersampaikan dengan baik. Hal ini bisa menjadi penghambat dalam pengenalan berbagai motif batik secara digital. Motif batik dapat diklasifikasikan dengan metode machine learning atau deep learning. Algoritma yang banyak digunakan pada masing-masing metode tersebut adalah Support Vector Machine (SVM) dan Convolutional Neural Network (CNN) yang dapat digunakan untuk membantu mengenali dan mengidentifikasi motif batik. SVM dan CNN akan melakukan pemodelan dengan menggunakan dataset batik nitik 960 kemudian membandingkan performa kedua model. Dataset batik nitik terdiri dari 960 data citra yang terbagi kedalam 60 kelas. SVM dibangun dengan menggunakan ekstraksi fitur color moment dan MTCD, sedangkan CNN menggunakan arsitektur VGG16 pretrained. Berdasarkan hasil analisis matriks evaluasi seperti akurasi, presisi, recall, dan F1-score model CNN dengan arsitektur VGG16 mendapatkan akurasi konsisten di angka 100% pada data testing dengan nilai learning rate = 0,001. Sedangkan SVM mendapatkan akurasi yang bervariasi pada beberapa pengujian berdasarkan pembagian jumlah dataset.
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DOI: https://doi.org/10.22146/ijeis.105658
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