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Taufik Akbar
Universitas Hamzanwadi
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

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Analisa Komparatif Klasifikasi Citra Sayuran dengan Algoritma Support Vector Machine dan Convolutional Neural Network Ida Wahidah; Hadian Mandala Putra; Suhartini; Taufik Akbar
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.32791

Abstract

This study presents a comparative analysis between the Support Vector Machine (SVM) algorithm, representing machine learning techniques, and the Convolutional Neural Network (CNN) algorithm, representing deep learning techniques, for vegetable image classification. The research adopts a quantitative approach by conducting multiple experimental schemes involving various SVM feature extraction methods and CNN architectures. The objective of this study is to evaluate and compare the performance, effectiveness, and computational efficiency of SVM and CNN algorithms. The dataset used in this study is a publicly available vegetable image dataset obtained from the Kaggle platform, consisting of 21.000 images categorized into 15 classes. The experimental results indicate that CNN significantly outperforms SVM in terms of accuracy, precision, recall, and F1-score. Moreover, CNN demonstrates superior generalization capability in predicting unseen image data. The best performance of the SVM algorithm was achieved using the Color Histogram feature extraction method, yielding an accuracy of 93%. In contrast, CNN models employing pre-trained architectures achieved higher accuracy, with VGG16 and MobileNetV2 obtaining accuracies of 98% and 100%, respectively. Based on the comparative results, CNN provides higher classification accuracy than SVM; therefore, this study can serve as a scientific reference for the development of image classification systems in digital agriculture and other applications requiring high accuracy and efficient computational performance.
Co-Authors 'Alimudin, 'Alimudin Ahmad Sony Alfathani Ahmad, Ramli Ahwan Ahmadi Alimudin Alimudin Ari Satriadi Arnila Sandi Dwi Rahayu Gede Indrawan Gede Suweken Hadian Mandala Putra Hafifan Triadi Putra, Ahmad Haris, M.Sabirin Hidayat, Firman Rizki Hizbul Izzi Ida Wahidah Ihwan Ahmadi Iman Darmawan, Iman Indra Gunawan Intan Komala Dewi Intan Komala Dewi Patwari Jagat Sugiantara Kadek Yota Ernanda Aryanto Komala Dewi P, Intan M.Julianto Maulana Putra Martua Hamonangan Nasution Maulidi, Dedi Mu'Tashim, Aan Muhammad Nuzuluddin Nuzuluddin, M. Nuzuluddin, Muhammad Patwari, Intan Koma Dewi Pranata, Aditya Ramdan, Muh. Rais Saputri, Fahmy Rinanda Sholihah, Hikmatun SIGIT, PUTRA ARI Suhartini Suhartini, Suhartini Ulul Azmi