Sistem Pakar Diagnosa Penyakit Gigi Menggunakan Metode Naive Bayes - Yuliyana Yuliyana; Anita Sindar Ros Maryana Sinaga (Fountain of Informatics Journal, 2019)
AbstrakPenyakit yang sering dianggap sepele namun sangat mengganggu adalah penyakit gigi. Umumnya gigi rentan terhadap makanan dan cuaca bila gigi mengalami permasalahan. Dari survey diperoleh sangat minim keinginan penderita sakit gigi berobat ke rumah sakit atau dokter spesialis. Sebuah sistem pakar memperkenalkan implementasi diagnosa penyakit gigi. Sipenderita dapat mengobati sakit gigi dengan arahan dari kommputer (pakar). Pakar sebagai sumber data basis pengetahuan diwakilkan komputer mendiagnosa penyakit. Menurut pakar gigi ada 7 jenis penyakit: Erosi Gigi, Ginggi-vitis, Pulpi-tis, Abses Gigi, Periodo-ntitis, Karies Gigi, Hali-tosis, dan Sindrom Gigi Retak. dengan 37 gejala (dikodekan sesuai kriteria). Dalam Naïve Bayes, pengklasifikasian menggunakan metode probabilitas dan statistik. Perhitungan Naïve Bayes berdasarkan data penyakit dan data gejala dengan variable Data, Hipotesa dan Probabilitas. Hasil dari penelitian ini adalah sebuah diagnosa terhadap penyakit gigi dengan hasil nilai probabilitas tertinggi. Nilai probabilitas dari gejala penyakit gigi diperoleh berdasarkan pengalaman seorang pakar atau dokter gigi. Dari data yang diuji sesuai kasus diketahui probabilitas Penyakit Halitosis adalah yang tertinggi dari penyakit lain yaitu 0.29646 atau 29.64%.Kata kunci: Penyakit Gigi, Diagnosa, Sistem Pakar, Probabilitas, Naïve Bayes Abstract[Expert System for Diagnosing Dental Disease Using Naive Bayes Method] Diseases that are often considered trivial but very disturbing are dental diseases. Generally, teeth are susceptible to food and weather when teeth experience problems. From the survey, it was obtained that there was very little desire for dental pain sufferers to go to hospitals or specialists. An expert system introduces the implementation of dental disease diagnoses. Patients can treat toothache with direction from a computer expert. Experts as knowledge base data sources are represented by computers diagnosing disease. According to dental experts, there are seven types of diseases: Dental Erosion, High-Vitis, Pulpitis, Dental Abscess, Periodonitisitis, Dental Caries, Halitosis, and Cracked Tooth Syndrome. with 37 symptoms (encoded according to criteria). In Naïve Bayes, the classification uses probability and statistical methods. Naïve Bayes calculations are based on disease data and symptom data with variable Data, Hypothesis and Probability. The results of this study are a diagnosis of dental disease with the highest probability value. The probability value of symptoms of dental disease is obtained based on the experience of an expert or dentist. From the data tested according to the case, it is known that the probability of Halitosis is the highest of other diseases, namely 0.29646 or 29.64%.Keywords: Dental Disease, Diagnosis, Expert System, Probability, Naïve Bayes