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All Journal CICES (Cyberpreneurship Innovative and Creative Exact and Social Science)
Yunizar, Sri Fatmawati
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice

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Pemanfaatan Model ResNet50 dan SVM untuk Klasifikasi Penyakit Daun Tebu Yunizar, Sri Fatmawati; Sari, Anggraini Puspita; Aditiawan, Firza Prima
CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Vol 11 No 1 (2025): CICES
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cices.v11i1.3506

Abstract

Indonesia is an agrarian country with an economy that heavily relies on the agricultural sector, including the sugarcane plantation sub-sector for sugar production. Although domestic sugar production continues to increase, the demand for sugar consumption also grows, leading to dependency on imports and fluctuating sugar prices in the domestic market. Therefore, efforts to maintain and enhance the productivity of sugarcane crops are crucial. One of the main challenges in sugarcane cultivation is the attack of pests and diseases such as yellow disease, redrot, mosaic, and rust, which often affect sugarcane plants and reduce their productivity. These diseases must be detected promptly as they significantly impact the quality and quantity of the sugarcane to be harvested. However, manual identification processes are prone to human error and are inefficient for large-scale plantations. To address this, machine learning technology using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) was employed. This approach uses CNN for feature extraction and SVM for classification. Through a series of experiments, the study shows that the CNN and SVM models can achieve high accuracy of 90.32% with a computational time of 181.53 seconds.
Co-Authors Anggraini Puspita Sari Firza Prima Aditiawan