Perbandingan Random Forest dan Support Vector Machine untuk Klasifikasi Penyakit Daun Tomat Berbasis Citra Digital
DOI:
https://doi.org/10.62712/juktisi.v5i2.1390Keywords:
Klasifikasi Penyakit Daun Tomat, Citra Digital, Random Forest, Support Vector Machine, Machine Learning.Abstract
Penyakit pada daun tomat merupakan salah satu penyebab utama menurunnya kualitas dan produktivitas hasil panen, sehingga diperlukan metode deteksi yang cepat dan akurat. Perkembangan teknologi pengolahan citra digital dan machine learning dapat dimanfaatkan untuk membantu proses identifikasi penyakit daun tomat secara otomatis. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Random Forest dan Support Vector Machine (SVM) dalam mengklasifikasikan penyakit daun tomat berbasis citra digital. Data dikumpulkan melalui studi pustaka dan penggunaan dataset Tomato Leaf Disease Detection yang diperoleh dari Kaggle. Tahapan penelitian meliputi pengumpulan dataset, pra-pemrosesan citra, ekstraksi fitur menggunakan Histogram HSV, Histogram of Oriented Gradients (HOG), dan Gray Level Co-occurrence Matrix (GLCM), pembagian data latih dan data uji dengan rasio 80:20, pelatihan model menggunakan algoritma Random Forest dan SVM, serta evaluasi menggunakan confusion matrix, akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki performa lebih baik dibandingkan SVM, dengan akurasi sebesar 86,33%, presisi 87,00%, recall 86,00%, dan F1-score 86,00%, sedangkan SVM memperoleh akurasi 74,67%, presisi 75,00%, recall 75,00%, dan F1-score 74,00%. Berdasarkan hasil tersebut, Random Forest terbukti lebih efektif dalam mengklasifikasikan penyakit daun tomat berbasis citra digital. Penelitian ini diharapkan dapat menjadi referensi dalam pengembangan sistem deteksi penyakit tanaman berbasis machine learning.
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Copyright (c) 2026 Pelagia Melati, Windi Irmayani, Muhammad Ifan Rifani Ihsan
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