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Implementasi Optical Character Recognition (OCR) untuk Otomasi Penghitungan Tagihan Listrik

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With the advancement of technology, automation in various aspects of life has become a necessity, including in the electricity billing system. The use of Optical Character Recognition (OCR) for automating electricity bill calculation can be an effective solution to improve efficiency and accuracy in administrative processes. This study aims to implement OCR in the automatic calculation of electricity bills, starting with scanning the numbers printed on electricity meters. The OCR system developed in this research converts the numbers printed on the meter into digital data that can be further processed to calculate the bill based on the applicable rates. The OCR algorithm used is combined with machine learning technology to enhance the accuracy of character recognition, minimizing errors in reading the printed data. The implementation of this OCR system shows that the use of this technology can reduce human errors, accelerate the billing process, and provide more efficient access to billing information. Moreover, this system also improves transparency and accuracy in determining the cost to be paid by the customers. Therefore, the OCR-based automation system can serve as an effective and practical alternative for electricity billing management in utility companies.
Title: Implementasi Optical Character Recognition (OCR) untuk Otomasi Penghitungan Tagihan Listrik
Description:
With the advancement of technology, automation in various aspects of life has become a necessity, including in the electricity billing system.
The use of Optical Character Recognition (OCR) for automating electricity bill calculation can be an effective solution to improve efficiency and accuracy in administrative processes.
This study aims to implement OCR in the automatic calculation of electricity bills, starting with scanning the numbers printed on electricity meters.
The OCR system developed in this research converts the numbers printed on the meter into digital data that can be further processed to calculate the bill based on the applicable rates.
The OCR algorithm used is combined with machine learning technology to enhance the accuracy of character recognition, minimizing errors in reading the printed data.
The implementation of this OCR system shows that the use of this technology can reduce human errors, accelerate the billing process, and provide more efficient access to billing information.
Moreover, this system also improves transparency and accuracy in determining the cost to be paid by the customers.
Therefore, the OCR-based automation system can serve as an effective and practical alternative for electricity billing management in utility companies.

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