Pemodelan K-Nearest Neighbor Untuk Identifikasi Pola Kepuasan Mahasiswa Terhadap Pelayanan Kampus (Studi Kasus : STMIK Kaputama) - Muhammad Rizky R Ritonga; Marto Sihombing; Selfira Selfira (Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia, 2024)
This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as