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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Siti Mariyah
Politeknik Statistika STIS
Computer Science & IT

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Study of Handwriting Recognition Implementation in Data Entry of Survei Angkatan Kerja Nasional (SAKERNAS) using CNN Yusron Farid Mustafa; Farid Ridho; Siti Mariyah
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.32

Abstract

The use of Paper and Pencil Interviewing (PAPI) at BPS requires manual data entry that cannot be separated from the human ability to recognize handwriting. For computers, handwriting recognition is complex work that requires complex algorithms. Convolutional Neural Network (CNN) is an algorithm that can accommodate the complexity of handwriting recognition. This research intends to conduct a study on the implementation of the handwriting recognition model using CNN in recognizing handwriting on the PAPI questionnaire in data entry activities. Handwriting recognition model was built using the EMNIST dataset separately according to its character type and provides 89% accuracy for characters in the form of letters and numbers, 95% for characters in the form of letters, and 99% for characters in the form of numbers. Implementation of the handwriting recognition on the questionnaire image shows good results with 83.33% accuracy. However, there are problems found in the process of character segmentation where characters are not segmented correctly because the line of writing continues on the character that should be separated and disconnected characters when they should be joined. The result obtained in this study is expected to be a consideration regarding the entry method data used by BPS later.
What We Know from Telemedicine Data in Indonesia? Study case using Alodokter, Dokter.id, and Honestdocs Faza Nur Fuadina; Nucke Widowati Kusumo Projo; Siti Mariyah
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.53

Abstract

The internet and technology development arise in various aspects of life in Indonesia, including in the health sector with e-health. Telemedicine utilization as a form of e-health was still rare among Indonesians because its existence is not as much as e-commerce that is more related to the economic sector. The COVID-19 pandemic has limited people's movement to get health care, but it made people use telemedicine in Indonesia. This research aims to analyze telemedicine utilization in Indonesia and see the health phenomena captured in the data. This research uses descriptive analysis and text mining to determine the utilization of telemedicine with the Named Entity Recognition (NER) and Latent Dirichlet Allocation (LDA) methods. In addition, a literature review is also used to identify the potential use of telemedicine data in collecting health statistics in Indonesia. The results show that telemedicine has been widely used in Indonesia. The clinical teleconsultation data and article titles on telemedicine produce various health topics. Therefore, telemedicine data can potentially be used as a source for collecting health statistics.
Co-Authors Farid Ridho Faza Nur Fuadina Nucke Widowati Kusumo Projo Yusron Farid Mustafa