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All Journal BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Teknomatika: Jurnal Informatika dan Komputer Journal of Informatics and Advanced Computing
Ninuk Wiliani
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Computer Science & IT

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Journal : Teknomatika: Jurnal Informatika dan Komputer

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Perbandingan Model CNN dan SVM untuk Klasifikasi Jenis Footwear pada Dataset Alas Kaki Berbasis Citra Gina Annisa; Ninuk Wiliani
Jurnal Teknomatika Vol 18 No 1 (2025): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v18i1.1564

Abstract

The classification of footwear types, such as boots, sandals, and shoes, is a significant challenge in the development of image recognition systems powered by artificial intelligence. This study aims to compare the performance of two popular classification models, namely Convolutional Neural Network (CNN) and Support Vector Machine (SVM), in recognizing footwear types. The dataset used is the Footwear-Shoe vs Sandal vs Boot Image Dataset, consisting of 3000 images for each category with a resolution of 136x102 pixels in RGB format. The methodology includes training and testing both models using optimized parameters to measure accuracy, precision, and computational efficiency. The results show that CNN achieves an accuracy of 98%, while SVM reaches an accuracy of 96%. The findings indicate that CNN is more suitable for applications requiring high accuracy, while SVM is an effective alternative in resource-constrained scenarios. This study offers significant contributions to understanding model performance in image-based footwear classification using machine learning.
Co-Authors Alfi Prabowo Anang Martoyo Angel Tiarma Sipahutar Eksa Manda Pramaswari Gina Annisa Latifah Novita Maharani Suparta Rizki Hesananda