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Journal of Software Engineering and Information System (SEIS)
Published by Universitas Muhammadiyah Riau
ISSN : -     EISSN : 28090950     DOI : https://doi.org/10.37859/seis.v3i1
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Journal of Software Engineering and Information System (SEIS) is a peer-reviewed journal published twice a year (January and August) by the Department of Information System - Faculty of Computer Science, Universitas Muhammadiyah Riau. The scope of the journal is: Artificial Intelligent Business Intelligence and Knowledge Management Data Mining E-Bussiness IT Governance Enterprise System System Design Information Design & Development Database System Expert System Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 61 Documents
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KLASIFIKASI BUAH JERUK LEMON BERDASARKAN TINGKAT KEMATANGAN MENGGUNAKAN METODE SVM DAN NAIVE BAYES Mualfah, Desti; Rivaldi, Hardi; Januar Al Amin; Sunanto
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9952

Abstract

This study aims to develop a classification model for determining the ripeness level of lemons (Citrus limon) using digital image analysis. Two methods, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC), were compared to evaluate their performance in terms of accuracy and prediction consistency. The results show that SVM outperformed NBC with an accuracy of 97%, along with precision, recall, and F1-Score of 97% each. The model consistently determined lemon ripeness levels in percentage terms, such as 85% or 95%. In contrast, NBC achieved an accuracy of 82%, with precision, recall, and F1-Score of 83%, 82%, and 83%, respectively. However, NBC was more prone to classification errors, especially in distinguishing between ripe and unripe lemons. In conclusion, the SVM method proved superior to NBC in determining lemon ripeness levels, particularly in handling complex data. SVM's ability to provide accurate and consistent predictions makes it a more effective choice for helping farmers optimize the quality and quantity of lemon production. This study contributes significantly to the application of image processing technology in the agricultural sector.

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