Komparasi Tingkat Akurasi Random Forest dan Decision Tree C4.5 Pada Klasifikasi Data Penyakit Infertilitas - Agung Prabowo; Sumita Wardani; Rico Wijaya Dewantoro; Wilfredo Wesly; Leonardo (KLIK: Kajian Ilmiah Informatika dan Komputer, 2023)
Male fertility has declined over the past two decades. The decrease is due to environmental factors, such as lifestyle habits that can affect the quality of a man's sperm. Artificial intelligence technology is currently developing as a methodology for health decision support systems. In the process of predicting infertility can be done by applying Machine Learning technology. This study focuses on comparing the Random Forest classification method with Decision Tree C4.5 to see the level of accuracy in predicting the success of infertility data classification. Data for the Fertility Dataset was obtained from the UCI Machine Learning Repository with a total of 100 data records, 10 attributes and 2 attribute classes, namely Normal and Altered. The parameters used are age, childhood diseases, accidents or trauma, surgical operations, alcohol consumption and smoking habits. Then evaluate the testing of the two methods, namely by using 10fold Cross Validation. Based on the results of Random Forest and Decision Tree C4.5 testing, the average accuracy of Random Forest is 87.20% and Decision Tree C4.5 with an accuracy rate of 85.90%. From the results obtained, it can be concluded that Random Forest is a superior method by 1.3% when compared to Decision Tree C4.5 in predicting accuracy in the Fertility Dataset.