Penerapan Algoritma K-Nearest Neighbor (K-NN) untuk Klasifikasi Status Monitoring Automatic Pump Water Machine Studi Kasus: Industri Manufaktur - Indra, Indra Mora; Adlian, Adlian Jefiza (MID Publisher International, 2025)
In the modern industrial world, real-time monitoring of system conditions is crucial to maintain efficiency and prevent equipment damage. This research aims to classify industrial system conditions based on sensor data using the K Nearest Neighbors (KNN) algorithm. The data used consists of four main parameters namely pressure, flow rate, voltage, and engine speed (RPM), which are then classified into three conditions: Alert, Critical, and Normal. Preprocessing is done with Min-Max normalization and division of data into training and test data. The evaluation results show that the KNN method is able to achieve an accuracy of 58% with a mean squared error (MSE) value of 1.06 and an average cross-validation accuracy of 64%. These results show that KNN is effective enough to be used as an initial method for industrial system condition detection, although the classification performance for the Critical category can still be improved.