PERBANDINGAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR PADA KLASIFIKASI MORFOLOGI GEN SEL DARAH PUTIH - Muhammad Nur'adli Hasbi Gumay (2022)
In the health sector, the diagnosis of leukemia is a difficult thing because it is still diagnosed manually with the help of a doctor. The diagnosis manual may suffer from errors caused by human negligence. From these problems, it is necessary to diagnose the type of leukemia using advanced technology, namely Machine learning to overcome these problems. In this study, the machine learning processes data from the types of leukemia, namely Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) based on the morphological characteristics of the white blood cell genes. The data classification methods used for this research are K-Nearest Neighbor (K-NN) and Naïve Bayes, then the two classification methods are compared to see the best classification method. This study uses preprocessing of data cleaning, feature selection, and scaling to increase the accuracy value. The results of this study are the K-Nearest Neighbors (K-NN) classification method is the best classification with an accuracy value using the ROC/AUC curve worth 0.952 when compared to the Naïve Bayes classification method, which is 0.912.