Classification of Production Machine Spare Part Stock Data Request Needs Using The K-Nearest Neighbor Method

Abstract

Spare parts encompass various items that are offered, owned, utilized, or consumed to fulfill consumer desires and requirements. This research implements the K-Nearest Neighbor algorithm on a test dataset consisting of 100 data objects, resulting in a novel classification perspective. The study includes a comprehensive model evaluation process involving Cross Validation on both training and testing datasets, comprising 1000 records with 36 critical and 64 non-critical outcomes. Performance assessment and testing utilizing the RapidMiner Studio application yield optimal results under various modeled scenarios. The accuracy of this algorithm model stands at 98.00%, with a standard deviation of +/- 4.00%.