DETEKSI DINI PENYAKIT DAUN PADI MENGGUNAKAN INTEGRASI GLCM DAN KNN
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https://doi.org/10.23960/jitet.v13i3.6543Abstract Views: 149 File Views: 140
Abstract
Produktivitas panen padi di Indonesia masih terganggu oleh penyakit daun yang sulit dikenali secara visual. Penelitian ini bertujuan untuk mengembangkan sistem deteksi dini berbasis citra digital menggunakan ekstraksi fitur Gray Level Co-occurrence Matrix (GLCM) dan klasifikasi K-Nearest Neighbor (KNN). Dataset terdiri dari 4684 citra tiga kelas penyakit daun padi. Proses mencakup konversi RGB ke HSV, segmentasi, ekstraksi enam fitur tekstur, dan klasifikasi. Hasil terbaik diperoleh pada k senilai 5 dengan akurasi, presisi, recall, dan F1-score sebesar 94,2%. Metode ini efektif membedakan daun sehat dan terinfeksi, serta dapat meningkatkan kualitas hasil panen padi sehingga dapat mendukung program pemerintah melalui Makan Bergizi Gratis (MBG). Penelitian ini juga membuka peluang pengembangan sistem berbasis mobile dengan integrasi kecerdasan buatan untuk kemudahan penggunaan di lapangan.Downloads
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