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All Journal Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON)
Muhammad Fakhri Pratama
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Journal : Journal of Computer System and Informatics (JoSYC)

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Penerapan Metode Long Short-Term Memory Pada Pendataan Warga Berbasis Android Sri Lestari; Muhammad Fakhri Pratama
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.1951

Abstract

Along with technological developments, utilization in data processing both input and output become very important. Starting from inputting data manually, it proves that the use of technology is still not fully used. Data that has been processed conventionally sometimes encounters problems caused by human error. Not only that, when data must be entered into a computer, it requires additional time and when natural disasters such as floods, earthquakes, landslides occur. Census files that are still in the conventional form are likely to be damaged and will take time to re-data. This is the main reason for RT 004 RW 008 to need an efficient data collection system. The application of Optical Character Recognition (OCR) technology in this study is expected to be a solution in increasing the efficiency of the data input process. In this study, the input process is carried out in real time using the Long Short-Term Memory (LSTM) method to scan data on e-KTP. The results of testing OCR technology with the LSTM method in this study for the three experiments carried out were 1) The e-KTP detection experiment resulted in an accuracy of 100%, 2) The trial of reading 19 attributes on the e-KTP resulted in an accuracy of 98.42%, 3) Testing the error reading of the e-KTP attribute resulted in an accuracy of 93.56%. Overall, the accuracy of the LSTM method in word detection on e-KTP results in an accuracy of 92.1%. These results indicate that the application of OCR technology with the LSTM method is one of the right solutions for the data input process.
Co-Authors Dadang Iskandar Mulyana` Rasiban Sri Lestari Yansen Yansen