Penerapan Algoritma Gradient Boosting dalam Mendiagnosa Penyakit Kucing dan Anjing
Keywords:
diagnosis diseases, gradient boosting, pet, zoonotic, diagnosa penyakit, hewanAbstract
Royal Canin selaku lembaga riset hewan domestik mengungkapkan bahwa hewan peliharaan di Indonesia jarang sekali melakukan pemeriksaan rutin ke klinik hewan, jika dipersentasekan hanya berada di angka 29,5%. Dengan persentase tersebut, semakin khawatir hewan dapat menularkan penyakit ke manusia atau disebut sebagai zoonosis, jika hewan sama sekali tidak mendapatkan perawatan dan identifikasi dini penyakit yang dialami. Pada penelitian ini menggunakan metode gradient boosting sebagai fokus utama untuk memprediksi penyakit berdasarkan gejala-gejala yang dialami hewan peliharaan. Melalui proses hyperparameter tuning menggunakan gridsearch, diperoleh model terbaik dengan kombinasi parameter: learning_rate 0,05, max_depth 7, min_samples_leaf 1, min_samples_split 2, n_estimators 200, dan subsample 0,9. Dari hasil hyperparameter tuning, model tersebut menunjukkan performa terbaik dengan accuracy 88%, precision 97%, recall 96%, f1-score 96%, dan hamming loss 0,29%. Hasil tersebut menunjukkan bahwa model memiliki kemampuan memprediksi multilabel yang akurat.
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