Optimizing Asset Management by using Double Declining Balance and The KNN Algorithm

Abstract

Satker PTN is a ministry work unit whose entire income goes into the state account and is not given ownership of its assets. One of the Satker PTN in Indonesia is the Bacharuddin Jusuf Habibie Institute of Technology (ITH). ITH's operational procedures follow the rules of Satker PTN: not given asset ownership rights. The management of BMN ITH assets has not been optimal due to limited human resources which cause difficulties in the maintenance process, checking asset conditions and procurement of goods so that digitalization tools are needed that can be used to increase the efficiency of decision making and asset data analysis. This study optimizes asset management at ITH by using the depreciation method, namely the Double Declining Balance Method to determine asset depreciation. The Multilabel Classification Method uses the K-Nearest Neighbor Algorithm to classify good asset conditions, checking needs improvement. This study evaluates the depreciation of Chromebook, HDMI Cable, Electronic Plug, LCD Projector and Microphone assets over a 5-year period. Based on the results of the study, the DDB method produces a lower Final Book Value with a faster depreciation rate. The DDB method is effective in accelerating the depreciation of high-tech assets that tend to have shorter economic lives. The kNN algorithm classifies asset conditions based on historical asset lending data that includes features related to asset depreciation and usage. The results of the comparison of the kNN and Random Forest models in asset data classification are evaluated in Cross Validation, Confusion Matrix and ROC Analysis. Evaluation of the kNN Cross Validation Model with a value of k = 27 with 5 and 10 folds with an AUC value of 0.982, accuracy of 0.981, F1 score of 0.980, precision of 0.984, recall of 0.981, and MCC of 0.871. Evaluation of the Random Forest Cross Validation Model using 50 decision trees and 5 folds with an Auc value of 0.979, accuracy, F1 score, precision, recall and MCC are the same as the kNN model. The results of the kNN and Random Forest Confusion Matrices provide similar results and are a more detailed picture of prediction errors, including false positives and false negatives, which helps in understanding and improving the model. The results of the ROC Analysis evaluation show the Threshold values of the Checking, Good, Repair categories, namely 0.794, 0.259, 0.083 for kNN and 0.692, 0.247, 0.102 for Random Forest. Based on the evaluation results, this study shows that the kNN model is able to distinguish asset categories, is accurate in predicting asset status and can reduce false positives so that only assets that really need attention can be followed up. This study combines the DDB and KNN methods can be easily implemented, accelerate asset depreciation, classify asset conditions effectively, reduce repair operational costs and can optimize asset management by implementing models in asset applications.