Vol.12, No.3, August 2023.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



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.
Accepted: 16 August 2023.
Published: 28 August 2023.




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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