Perbandingan CNN dan SVM untuk Klasifikasi Citra Rempah Indonesia

Abstract

The purpose of this study is to evaluate how well Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) perform while using digital image data to classify different kinds of Indonesian spices. This study's dataset, which includes a variety of spice images with different shapes, textures, and colors, was sourced from the Kaggle platform. CNN is frequently used in difficult picture classification problems and is well known for its capacity to automatically extract visual information. SVM, on the other hand, is a traditional machine learning method that has demonstrated reliable results in a range of classification tasks, especially when dealing with sparse data and well-organized features. There are several processes in the study technique, such as gathering data, preprocessing images, training the model, evaluating the model, and comparing performance. The models abilities and flaws in categorizing spice photos are thoroughly examined by evaluating them using important performance measures such as accuracy, precision, recall, and F1-score. The findings are anticipated to aid in the development of intelligent agricultural systems, particularly in automating the process of classifying and identifying spice items. Furthermore, this comparison can be used as a reference to choose suitable machine learning techniques for comparable picture classification problems including datasets linked to agriculture or food.