EVALUASI EFEKTIVITAS ALGORITMA KLASIFIKASI BEBAN PENGGUNAAN LISTRIK PADA MESIN PABRIK BAJA
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Abstract
A factory’s load type on electrical usage is important in asserting how one has to manage its production. By identifying a factory’s load type, one could better ensure the stable energy for core machines and appliances, prevent machine overload, as well as saving costs by aligning high production schedules with off-peak electricity hours. This research aimed to use K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM) algorithms to classify load types in a steel industry factory. The research concluded that the Decision Tree algorithm is by far the most consistently accurate classifier at an accuracy of 92% in all ratios of training and testing dataset split, reaching 92.8% in a ratio of 90% training set and 10% testing set. While the KNN and SVM algorithms lag behind at an accuracy of 90.2% and 82.8% respectively.
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