Pendeteksi Penyakit Daun Padi Menggunakan Algoritma YOLOv8 di Desa Jangan-Jangan Kecamatan Pujananting Kabupaten Barru - Suandi Aritmawijaya; Fahrim Irhamna Rachman; Rizki Yusliana Bakti (Universitas Muhammadiyah Makassar, 2025)
ABSTRAKProduksi padi di Indonesia memiliki peran penting dalam menjaga ketahanan pangan nasional, namun produktivitasnya sering mengalami penurunan akibat serangan penyakit pada daun padi. Penyakit seperti blast, bercak coklat, dan hawar daun bakteri merupakan penyakit utama yang dapat menimbulkan kerugian signifikan jika tidak terdeteksi sejak dini. Identifikasi penyakit daun padi secara konvensional umumnya masih dilakukan secara manual dan bergantung pada pengalaman petani, sehingga berpotensi menimbulkan kesalahan diagnosis. Oleh karena itu, penelitian ini bertujuan mengembangkan sistem pendeteksi otomatis penyakit daun padi berbasis deep learning menggunakan algoritma YOLOv8. Dataset diperoleh dari pengambilan citra langsung di lahan pertanian Desa Jangan-Jangan, Kabupaten Barru, yang merepresentasikan kondisi lapangan nyata dan mencakup tiga jenis penyakit utama. Tahapan penelitian meliputi anotasi data menggunakan Roboflow, pelatihan model dengan Google Collab, serta evaluasi performa menggunakan confusion matrix, precision, recall, F1-score, dan mean Average Precision. Hasil pengujian menunjukkan bahwa model YOLOv8 mampu mendeteksi penyakit daun padi dengan akurasi tinggi dan waktu inferensi cepat, sehingga berpotensi diterapkan sebagai solusi deteksi dini penyakit padi secara real-time. Kata Kunci: YOLOv8, Deteksi Penyakit Padi, Deep learning, Citra Digital, Pertanian Presisi, Roboflow,CNN. ABSTRACTRice production in Indonesia plays a crucial role in maintaining national food security, but productivity often declines due to leaf disease attacks. Diseases such as blast, brown spot, and bacterial leaf blight are major diseases that can cause significant losses if not detected early. Conventional rice leaf disease identification is generally still done manually and relies on farmer experience, potentially leading to misdiagnosis. Therefore, this study aims to develop an automatic rice leaf disease detection system based on deep learning using the YOLOv8 algorithm. The dataset was obtained from direct imagery captured in agricultural fields in Jangan-Jangan Village, Barru Regency, which represents real-world conditions and includes three main types of diseases. The research stages include data annotation using Roboflow, model training with Google Colab, and performance evaluation using a confusion matrix, precision, recall, F1-score, and mean Average precision. The test results show that the YOLOv8 model is capable of detecting rice leaf diseases with high accuracy and fast inference time, thus potentially being implemented as a real-time early detection solution for rice diseases. Keyworsds: YOLOv8, Rice Disease Detection, Deep learning, Digital Imagery, Precision Farming, Roboflow,CNN.