Klasifikasi Tingkat Keparahan Korban Kecelakaan Lalu Lintas Di Kota Samarinda Menggunakan Algoritma K-Nearest Neighbor dan Naive Bayes - Salsabila, Nabila Abda (EKSPONENSIAL, 2023)
Classification is the process of evaluating data objects to be included in a particular class from a number of available classes. The K-Nearest Neighbor algorithm is one of the algorithms used to classify an object against a new object based on its K nearest neighbors. Naive Bayes is a classification of data using probability based on the Bayes theorem with strong independence assumptions. This study aims to compare the accuracy of the classification results on traffic accident victim data in Samarinda City using the K-Nearest Neighbor algorithm and the Naive Bayes algorithm. The data used is data on the severity of traffic accident victims in Samarinda City from 2020 to 2021 with death and non-death classes and uses 6 independent variables, namely age, gender, victim's role, victim's vehicle, road status, and condition weather. The measurement of accuracy in classifying the K-Nearest Neighbor algorithm and the Naive Bayes algorithm uses a classification performance matrix. Based on the results of the study, the accuracy of the classification results of the K-Nearest Neighbor algorithm was obtained at 75.86%, while the Naive Bayes algorithm obtained an accuracy rate of 79.31%. From the results of this analysis, it can be concluded that the Naive Bayes algorithm works better than the K-Nearest Neighbor algorithm in classifying the severity of traffic accident victims in Samarinda City.