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Adlian, Adlian Jefiza
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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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
Technology Sciences Insights Journal Vol. 2 No. 2 (2025): Technology Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/zb0dph85

Abstract

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. 
Co-Authors Indra, Indra Mora