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Vol.12, No.3, August 2023. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Prediction Analysis of Laboratory Equipment Depreciation Using Supervised Learning Methods
Geovanne Farell, Nizwardi Jalinus, Asmar Yulastri, Sandi Rahmadika, Rido Wahyudi
© 2023 Geovanne Farell, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 12, Issue 3, Pages 1525-1532, ISSN 2217-8309, DOI: 10.18421/TEM123-33, August 2023.
Received: 30 May 2023. Revised: 28 July 2023.
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.
Keywords –Machine learning, supervised learning, linear regression, laboratory equipment. |
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