Sistem Deteksi Penyakit Ikan Koi Menggunakan Metode YOLOv5
DOI:
https://doi.org/10.15575/telka.v11n3.302-314Kata Kunci:
penyakit ikan, ikan koi, pembelajaran mesin, YOLOv5, evaluasi modelAbstrak
Penyebab utama peningkatan kematian di budidaya perikanan adalah penyakit ikan. Salah satu ikan yang mudah terserang hama dan penyakit adalah ikan koi. Identifikasi penyakit ikan koi secara otomatis pada tahap awal merupakan langkah penting untuk mencegah penyebaran penyakit. Deteksi penyakit ikan koi dapat dilakukan melalui berbagai cara yaitu pemeriksaan visual, penggunaan sensor fisik, analisis genetik, teknologi citra dan pengolahan citra, biosensor dan biochips, teknologi saringan molekuler, jaringan saraf tiruan (artificial neural networks) dan pembelajaran mesin. Sistem deteksi penyakit ikan koi pada penelitian ini menggunakan YOLOv5. Hal ini dikarenakan YOLOv5 memiliki beberapa kelebihan antara lain memiliki tingkat akurasi yang tinggi, dapat mendeteksi secara real time, model ringan, sederhana dalam training, dan open source. Penelitian ini melalui beberapa tahap, yaitu pengumpulan dan penyiapan data, pelatihan model dengan algoritma YOLOv5, serta proses evaluasi terhadap performa model. Pada tahap ini, model dievaluasi berdasarkan nilai accuracy, recall, precision, dan mean Average Precision (mAP). Nilai accuracy sebesar 90% didapatkan sebagai hasil evaluasi model, nilai precision untuk ikan sehat (healthy-fish) sebesar 83,33% sedangkan untuk ikan sakit (sick-fish) sebesar 80%, recall sebesar 100%, dan mean Average Percision (mAP) sebesar 81,67%. Hal ini menunjukan bahwa model mampu mengklasifikasikan secara akurat ikan sehat dan ikan sakit.
The main reason for higher mortality in aquaculture is fish-related diseases. One type of fish that is highly susceptible to pests and diseases is the koi fish. Early-stage automatic identification of koi fish diseases is an essential step in preventing the spread of infection. Koi fish disease detection can be conducted through various methods, including visual inspection, physical sensors, genetic analysis, image technology and image processing, biosensors and biochips, molecular screening technology, artificial neural networks, and machine learning. The disease detection system in this study uses YOLOv5, due to its several advantages: high accuracy, real-time detection capability, lightweight model, simplicity in training, and being open-source. This research comprises a series of steps, starting from data preparation and model training using YOLOv5, to the evaluation process which measures accuracy, precision, recall, and mean Average Precision (mAP). A 90% accuracy was achieved through the evaluation of the model, precision scores were 83.33% for healthy fish and 80% for sick fish. The model achieved a recall score was 100%, with mAP score was 81.67%. This model evaluation confirms the accurate detection of both healthy and sick fish.
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