Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods

Authors

  • P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Potensi Utama University, Medan, Indonesia
  • Fazli Nugraha Tambunan Potensi Utama University, Medan, Indonesia
  • Rika Rosnelly Potensi Utama University, Medan, Indonesia
  • Wanayumini Potensi Utama University, Medan, Indonesia

DOI:

10.33395/sinkron.v8i2.12195

Keywords:

Support Vector Machine, K-Nearest Neighbor, Tomato, Tomato Lead Disease, Classification

Abstract

Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affected by disease and 1 healthy leaf. We evaluate and analyze all models using 5-Fold, 10-Fold, and 20-Fold Cross Validation with accuracy, precision, and recall for the best accuracy. The best results of this study are accuracy in the SVM method of 0.953 or 95.3%, Precision of 0.953 or 95.3%, and Recall of 0.953 or 95.3% with 10-Fold Cross-Validation. Compared to the K-NN method, it only obtained an accuracy of 0.907 or 90.7%, a Precision of 0.908 or 90.8%, and a Recall of 0.907 or 90.7% with 10-Fold Cross-Validation.

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How to Cite

Zer, P. F. I. R. ., Tambunan, F. N. ., Rosnelly, R. ., & Wanayumini, W. (2023). Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(2), 939-947. https://doi.org/10.33395/sinkron.v8i2.12195

Issue

Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023

Section

Articles

License

Copyright (c) 2023 P.P.P.A.N.W. Fikrul Ilmi R.H. Zer, Fazli Nugraha Tambunan, Rika Rosnelly, Wanayumini

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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