Klasifikasi Kepuasan Masyarakat terhadap Kinerja Petugas Sensus menggunakan K-Nearest Neighbours - Auji, Muhammad Fathah; Fuadi, Wahyu; Razi, Ar (Jurnal Algoritma, 2025)
This study aims to analyze the level of public satisfaction with the performance of census officers using the K-Nearest Neighbors (KNN) algorithm and the Chebyshev Distance measurement method. In this study, data were collected through field interviews with 239 respondents who were grouped into five satisfaction categories: Very Satisfied, Satisfied, Fairly Satisfied, Dissatisfied, and Very Dissatisfied. The KNN model applied using Python produced an accuracy of 75%, precision of 44%, and recall of 71% with k = 3. The results of the study show that KNN can classify the level of public satisfaction quite well, although the accuracy obtained still shows potential for improvement. This study suggests that further research be conducted using more complex methods to improve classification results.