Data Mining Menggunakan Algoritma Naive Bayes Untuk Klasifikasi Penyakit Babi Di Sumba Timur

Abstract

Pig livestock has become an inseparable part of the socio-cultural life of the people of East Sumba, NTT Province. The purpose of this research is to identify and classify pig diseases based on pig disease data. The pig disease data classification method used is the Naive Bayes method to identify diseases in pigs based on their symptoms. The focus of this research is the diseases that often occur in East Sumba, namely Colibacillosis, African Swine Fever (ASF) and Helminthiasis in pigs. This research has benefits for pig farmers, local government, research and academia, society, and environmental sustainability. Better management of swine diseases supports productivity, government policy and research contributions. Disease prevention also has a positive impact on human health and the environment, addressing potential transmission and the impact of unsustainable farming practices. Performance/Confusion Matrix classification results, the accuracy obtained is 87.18%. Class P1 which has 65 samples and is predicted to be class P1 65, class P2 0 and P3 0 so that class precision and class recall 100.00%. Class P2 has 65 samples and is predicted to be class P1 0, class P2 46 and P3 6 so that class precision is 88.46% and class recall is 70.77%. Class P3 has 65 samples and is predicted to be class P1 0, P2 19 and P3 59 so class precision 75.64% and class recall 90.77%.