Random Forest Implementation in Prepaid Electric Meter Recognition

  • Komang Jaya Bhaskara Mahatya Tel-U
  • Fathoni Waseso Jati
  • Budhi Irawan
  • Faisal Candrasyah Hasibuan
DOI: https://doi.org/10.25124/cepat.v1i02.5228

Abstract

While prepaid electricity services provide better flexibility, it comes with an additional step for the customer. Instead of paying a monthly bill based on electric usage, a prepaid system requires customers to actively predict their electricity usage before they pay for the correct electricity value. This presents a challenge because Underestimating electricity usage may lead to a power outage. Therefore, a system that monitors electricity can be developed to address this issue. There are two approaches to developing an electric monitoring system: designing the electric meter equipped with monitoring features or designing an external capturing device to work with the current electric meter. The first approach is costly and requires a meter disassembly. Thus, in this paper, the second approach is used. By utilizing image processing and a Random Forest machine learning algorithm, a monitoring device can be developed to read the digital meter's display. Although it may affect performance due to the low-power device, Raspberry Pi 3 and Raspberry Camera are used to provide automation. This method yields an accuracy of 97% using 375 images.

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References

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Published
2022-08-30
How to Cite
MAHATYA, Komang Jaya Bhaskara et al. Random Forest Implementation in Prepaid Electric Meter Recognition. [CEPAT] Journal of Computer Engineering: Progress, Application and Technology, [S.l.], v. 1, n. 02, p. 33-40, aug. 2022. ISSN 2963-6728. Available at: <//journals.telkomuniversity.ac.id/cepat/article/view/5228>. Date accessed: 03 may 2024. doi: https://doi.org/10.25124/cepat.v1i02.5228.
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Vol 1 No 02 (2022): August 2022
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Articles
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