Prediction Analysis of Laboratory Equipment Depreciation Using Supervised Learning Methods

Abstract

Asset management in Indonesia still poses problems in terms of securing state-owned property. These concerns make it difficult for analysts to predict laboratory equipment depreciation. Therefore, this research aims to create a new model to address this issue. Additionally, to support laboratory managers in gaining insights, a technology-based framework in the form of a laboratory equipment depreciation prediction model has been developed. A new model has been created in this research, which integrates supervised learning models with linear regression algorithms, and subsequently employs a waterfall system development approach. The testing results of the model for predicting laboratory equipment depreciation showed a high level of accuracy, reaching 93%. Furthermore, the comparison between the prediction model and the laboratory equipment data tested directly by technicians demonstrated an accuracy rate of 100%. Finally, the numerical results demonstrate that our framework provides a valuable solution to the difficulties in predicting laboratory equipment depreciation, offering an innovative and practical approach to laboratory equipment maintenance.