IMPLEMENTASI ALGORITMA NAÏVE BAYES DAN CERTAINTY FACTOR DALAM MENDETEKSI PENYAKIT MULUT & KUKU (PMK) PADA HEWAN TERNAK BERBASIS WEB DI PETERNAKAN PROVINSI RIAU

SIREGAR, STIVEN ALFONSO (2023) IMPLEMENTASI ALGORITMA NAÏVE BAYES DAN CERTAINTY FACTOR DALAM MENDETEKSI PENYAKIT MULUT & KUKU (PMK) PADA HEWAN TERNAK BERBASIS WEB DI PETERNAKAN PROVINSI RIAU. S1 thesis, Universitas Mercu Buana - Menteng.

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Abstract

Berdasarkan informasi dari Dinas Peternakan dan Kesehatan Hewan Provinsi Riau, beberapa kabupaten di Provinsi Riau saat ini banyak ditemukan puluhan ekor sapi terjangkit PMK (Penyakit Mulut dan Kuku). Wabah ini tentunya sangat memberikan suatu pukulan terhadap peternak terlebih peternak pada pedesaan yang belum memiliki edukasi terkait penyakit ini. Oleh karena itu dibuat sistem yang dapat mendeteksi atau gejala-gejala sebagai tanda ternak terjangkit PMK (Penyakit Mulut dan Kuku) berdasarkan gejala-gejala yang dialami ternak. Dan juga sistem ini dapat memberikan solusi penanganan kepada peternak berdasarkan gejala yang dialami yaitu gejala umum dan gejala serius. Dari pengujian nya diperoleh hasil akurasi untuk algoritma Naïve Bayes senilai 87,6 % terjangkit gejala umum, dan untuk algoritma Certainty Factor hasil akurasi nya 61% terjangkit gejala umum. Berdasarkan ini tentunya dapat memberikan peternak suatu jawaban atas wabah PMK ini sehingga dapat memberikan pertolongan pertama dan solusi agar wabah ini tidak cepat menyebar ke ternak lain nya yang berkuku belah genap, dan juga dapat menghindari kematian. Kata Kunci : Sistem Pakar, Naïve Bayes, Certainty Factor, PMK (Penyakit Mulut & Kuku) Based on information from the Riau Province Animal Husbandry and Animal Health Service, several districts in Riau Province are currently finding dozens of cows infected with FMD (Foot and Mouth Disease). Of course, this epidemic has dealt a huge blow to breeders, especially breeders in rural areas who have not had education regarding this disease. Therefore, a system is created that can detect symptoms or symptoms as a sign of livestock infected with FMD (Foot and Mouth Disease) based on the symptoms experienced by livestock. And also this system can provide handling solutions to breeders based on the symptoms experienced, namely general symptoms and serious symptoms. From the test, the accuracy results for the Naïve Bayes algorithm were 87.6% infected with common symptoms, and for the Certainty Factor algorithm the accuracy results were 61% infected with common symptoms. Based on this, of course, it can provide farmers with an answer to this FMD outbreak so that they can provide first aid so that this outbreak does not quickly spread to other livestock with even-numbered nails, and can also avoid death. Keywords : Expert Systems, Naïve Bayes, Certainty Factor, Foot and Mouth Disease

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