Komparasi SVM dan Random Forest Berbasis Histogram Warna untuk Deteksi Penyakit Anggur
Abstract
The decline in grape (Vitis vinifera) productivity is often caused by leaf diseases such as Black Rot, which are challenging to detect accurately through manual visual inspection The key point of this research is to compare the performance of two Machine Learning classification algorithms, namely Support Vector Machine (SVM) and Random Forest, to identify the most optimal model for disease detection. The methodology employs digital image processing with Histogram Color (HSV) feature extraction, which is chosen for its efficiency in representing color changes caused by infection. The grape leaf disease image dataset was classified and evaluated. The comparative results demonstrate that Random Forest achieved the highest accuracy of 95.32%, slightly surpassing SVM which reached 94.48%. These findings prove that both algorithms perform excellently, but Random Forest is more recommended for this dataset due to its superior robustness in accurately predicting disease classes.
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References
E. Safitri, R. Heppy Ria Sibarani, Y. SM Sidabutar, and D. Kiswanto, KLASIFIKASI PENYAKIT DAUN ANGGUR BERBASIS CITRA MENGGUNAKAN METODE K-NEAREST NEIGHBORS (KNN), JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 6, pp. 1263312642, Nov. 2024, doi: 10.36040/JATI.V8I6.12004.
K. R. Wardani and L. Leonardi, Klasifikasi Penyakit pada Daun Anggur menggunakan Metode Convolutional Neural Network, Jurnal Tekno Insentif, vol. 17, no. 2, pp. 112126, Oct. 2023, doi: 10.36787/JTI.V17I2.1130.
F. F. Rahman and N. Pratiwi, IDENTIFIKASI PENYAKIT TUMBUHAN TOMAT DAN ANGGUR MENGGUNAKAN CNN DENGAN ARSITEKTUR VGG-16, JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 1, pp. 270279, Jan. 2025, doi: 10.29100/JIPI.V10I1.5663.
P. S. Thakur, T. Sheorey, and A. Ojha, VGG-ICNN: A Lightweight CNN model for crop disease identification, Multimed Tools Appl, vol. 82, no. 1, pp. 497520, Jan. 2023, doi: 10.1007/S11042-022-13144-Z;TAXONOMY:TAXONOMY:ACM-PUBTYPE;PAGEGROUP:STRING:PUBLICATION.
M. I. Abdurrahman, G. Wibowo, and I. Pratama, Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine, Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 4, pp. 715722, Oct. 2024, doi: 10.47233/JTEKSIS.V6I4.1552.
Anissa Ollivia Cahya Pratiwi, Klasifikasi Jenis Anggur Berdasarkan Bentuk Daun Menggunakan Convolutional Neural Network Dan K-Nearest Neighbor, Jurnal Ilmiah Teknik Informatika dan Komunikasi, vol. 3, no. 2, pp. 201224, Jul. 2023, doi: 10.55606/JUITIK.V3I2.535.
S. Agustiani, Y. T. Arifin, A. Junaidi, S. K. Wildah, and A. Mustopa, Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram, Jurnal Komputasi, vol. 10, no. 1, pp. 6574, Apr. 2022, doi: 10.23960/KOMPUTASI.V10I1.2961.
H. P. Hadi and E. H. Rachmawanto, EKSTRAKSI FITUR WARNA DAN GLCM PADA ALGORITMA KNN UNTUK KLASIFIKASI KEMATANGAN RAMBUTAN, Jurnal Informatika Polinema, vol. 8, no. 3, pp. 6368, Jun. 2022, doi: 10.33795/JIP.V8I3.949.
S. K. Wildah, A. Latif, A. Mustopa, S. Suharyanto, M. S. Maulana, and A. Sasongko, Klasifikasi Penyakit Daun Kopi Menggunakan Kombinasi Haralick, Color Histogram dan Random Forest, JUSTIN (Jurnal Sistem dan Teknologi Informasi), vol. 11, no. 1, pp. 3539, Jan. 2023, doi: 10.26418/JUSTIN.V11I1.60985.
M. I. Prasetya, I. Z. Yadi, Y. N. Kunang, and S. D. Permatasari, Prapemrosesan untuk Klasifikasi Gambar Aksara OKU Timur, Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 7, no. 1, pp. 208215, Feb. 2025, doi: 10.47233/JTEKSIS.V7I1.1629.
P. A. Prayesy, STUDI PERBANDINGAN METODE SUPPORT VECTOR MACHINE, RANDOM FOREST, DAN CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT KULIT, Jurnal Kecerdasan Buatan dan Teknologi Informasi, vol. 4, no. 1, pp. 7076, Jan. 2025, doi: 10.69916/JKBTI.V4I1.214.
R. Mardianto, Stefanie Quinevera, and S. Rochimah, Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga, Journal of Applied Computer Science and Technology, vol. 5, no. 1, pp. 6371, May 2024, doi: 10.52158/JACOST.V5I1.742.
A. Salim and A. Ramadhanu, Klasifikasi Citra Alat Musik Marakas, Gitar, dan Drum Menggunakan Metode K-Means dan GLCM, Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 7, no. 4, pp. 534538, Oct. 2025, doi: 10.47233/JTEKSIS.V7I4.2265.
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