KLASIFIKASI DATA MINING UNTUK MENDIAGNOSA PENYAKIT ISPA PADA ANAK DAN BALITA MENGGUNAKAN ALGORITMA NAIVE BAYES DAN DECISION TREE - Hozana Aulia, (2023)
Acute Respiratory Infection (ARI) is still the most widely transmitted respiratory disease with high mortality in the world, with most deaths occurring in children and toddlers. In Indonesia, ARI is one of the highest diseases for transmission. Research in the health sector is to diagnose the severity of patients with ISPA based on the type of ISPA disease they suffer, symptoms, age, sex, and duration of illness, so it is necessary to conduct earlier treatment, in order to prevent death in children and toddlers due to delays in treatment. Research related to disease prediction using classification data mining techniques has been widely used. For this research, the Naïve Bayes and Decision Tree algorithms will be used and comparisons will be made to get the best model among these algorithms. The first test uses the Naïve Bayes algorithm with three trials by dividing the training data and test data. The first experiment with 80% training data and 20% test data resulted in an accuracy of 92.30%, the second experiment with 50% training data and 50% test data resulted in an accuracy of 88.88%, and the third experiment with 90% training data and 10% test data resulted in an accuracy of 92.30 %. The second test uses the Decision Tree algorithm with three trials. The first experiment with 80% training data and 20% test data produced an accuracy of 88.46%, the second experiment with 50% training data and 50% test data produced an accuracy of 90.47%, and the third experiment with 90% training data and 10% test data produced an accuracy of 92.30%.