PERFORMANCE ANALYSIS OF K-NN AND SVM IN DIGITAL IMAGE-BASED TEA LEAF DISEASE CLASSIFICATION
DOI:
https://doi.org/10.33480/js2snb70Keywords:
Classification, Digital Image, K-Nearest Neighbor, Support Vector Machine, Tea Leaf DiseaseAbstract
Tea is a commodity with high economic value, but it is susceptible to diseases such as Brown Blight, Red Rust, and Red Spider Mite. The manual identification process currently relies on visual observation, which is time-consuming and prone to error. This research aims to analyze the performance of K-NN and SVM algorithms in classifying tea leaf diseases based on digital images. This research utilized a perfectly balanced dataset of 5,000 images. The research methodology involves image preprocessing and classification using 5-Fold, 10-Fold, and 20-Fold Cross-Validation. The results demonstrate that the SVM algorithm consistently outperforms K-NN across all testing scenarios. Specifically, SVM achieved its highest accuracy of 96.6% using 20-Fold Cross-Validation, whereas the highest accuracy for K-NN was 96.1%. The research concludes that SVM provides superior sensitivity and accuracy for identifying tea leaf diseases, offering a viable solution for automated detection systems in the plantation sector
References
Assegie, T. A. (2021). Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model. Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, 3(1), 9–14. https://doi.org/10.35882/ijeeemi.v3i1.2
Harahap, R. I. J., Khairani, S., & Rismayanti. (2024). Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun Implementation Of The K-Nearest Neighbor Method For Classification Of Cucumber Diseases On Leaf Images. Jurnal Ilmu Komputer Dan Sistem Informasi (JIRSI), 3(2), 135–145. Retrieved from https://jurnal.unity-academy.sch.id/index.php/jirsi/index%0Ahttp://creativecommons.org/licenses/by-sa/4.0/
Harakannanavar, S. S., Rudagi, J. M., Puranikmath, V. I., Siddiqua, A., & Pramodhini, R. (2022). Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings, 3(1), 305–310. https://doi.org/10.1016/j.gltp.2022.03.016
Heng, Q., Yu, S., & Zhang, Y. (2024). A new AI-based approach for automatic identification of tea leaf disease using deep neural network based on hybrid pooling. Heliyon, 10(5), e26465. https://doi.org/10.1016/j.heliyon.2024.e26465
Hilmi, A. N., Puspaningrum, E. Y., & Wahanani, H. E. (2024). Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Identifikasi Penyakit pada Tanaman Jeruk Berdasarkan Citra Daun. Router : Jurnal Teknik Informatika Dan Terapan, 2(2), 107–117.
Li, Y., Lu, Y., Liu, H., Bai, J., Yang, C., Yuan, H., … Xiao, Q. (2024). Tea leaf disease and insect identification based on improved MobileNetV3. Frontiers in Plant Science, 15(September), 1–12. https://doi.org/10.3389/fpls.2024.1459292
Madhurya, M. J., Gururaj, H. L., Soundarya, B. C., Vidyashree, K. P., & Rajendra, A. B. (2022). Exploratory analysis of credit card fraud detection using machine learning techniques. Global Transitions Proceedings, 3(1), 31–37. https://doi.org/10.1016/j.gltp.2022.04.006
Marpaung, F. (2023). Klasifikasi Daun Teh Menggunakan Metode CNN Dan Arsi Mobilenetv2.
Mustikasari, M., Ramli, A. R., & Gibran, A. K. (2023). Analisis Kinerja Algoritma Machine Learning Untuk Deteksi Penyakit Daun Teh Dengan Particle Swarm Optimization. Journal of Embedded Systems, Security and Intelligent Systems, 4(2), 97–106. https://doi.org/10.59562/jessi.v4i2.708
Purba, S. A. A., & Sriani, S. (2024). Klasifikasi Hasil Produksi Dauh Teh Basah (DTB) Menggunakan Algoritma K-Nearest Neighbor (KNN). Informatika, 16(2), 313–319.
Salsabila, A. P. B., Rozikin, C., & Adam, R. I. (2023). Klasifikasi Motif Batik Karawang Berbasis Citra Digital dengan Principal Component Analysis dan K-Nearest Neighbor. Jurnal Sistem Dan Teknologi Informasi (JustIN), 11(1), 20. https://doi.org/10.26418/justin.v11i1.46936
Somesh, K., Sai, C. S. S., Mude, N., Surendiran, B., & Dhakshayani, J. (2024). Enhancing Tea Leaf Disease Classification: Leveraging Data Augmentation, Diverse Feature Extraction Techniques, and Ensemble Stacking with Machine Learning Models. ICDLAIR, (Icdlair 2024), 149–168. https://doi.org/10.2991/978-94-6463-740-3_14
Srg, S. A. R., Zarlis, M., & Wanayumini, W. (2022). Klasifikasi Citra Daun dengan GLCM (Gray Level Co-Occurence) dan K-NN (K-Nearest Neighbor). MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 477–488. https://doi.org/10.30812/matrik.v21i2.1572
Syaputra, A. (2024). Klasifikasi Penyakit Daun pada Tebu dengan Pendekatan Algoritma K-Nearest Neighbors, Multilayer Perceptron dan Support Vector Machine. Jurnal Ilmiah Informatika Global, 15(3), 131–138. https://doi.org/10.36982/jiig.v15i3.4856
Ulfa, M. (2023). PERBANDINGAN METODE LEARNING VECTOR QUANTIZATION DAN SUPPORT VECTOR MACHINE DALAM MENGKLASIFIKASI PENYAKIT DAUN TEH MENGGUNAKAN EKSTRAKSI FITUR SIFT. Retrieved from https://repositori.uma.ac.id/handle/123456789/20323%0Ahttps://repositori.uma.ac.id/jspui/bitstream/123456789/20323/1/178160063 - Mutia Ulfa - Fulltext.pdf
Downloads
Published
Issue
Section
License
Copyright (c) 2026 P.P.P.A.N.W.Fikrul Ilmi R.H Zer, Abdi Rahim Damanik, P.A.M. Zidane R.W.P.P Zer
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license. The copyright in each article belongs to the author. Authors retain all their rights to published works, not limited to the rights set out on this page. The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC). Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri); The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author. If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.
