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All Journal Indonesian of Health Information Management Journal (INOHIM) PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Erni Tri Astuti
Politeknik Statistika STIS
Computer Science & IT Health Professions

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Analisis Teks Pemberitaan Telemedicine di Indonesia: Pendekatan Sentimen, NER, Topic Modeling, dan Social Network dalam Memahami Isu dan Persepsi Satria Bagus Panuntun; Dewi Krismawati; Setia Pramana; Erni Tri Astuti
Indonesian of Health Information Management Journal (INOHIM) Vol 11, No 1 (2023): INOHIM
Publisher : Lembaga Penerbitan Universitas Esa Unggul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47007/inohim.v11i1.500

Abstract

AbstractTelemedicine is becoming an increasingly relevant phenomenon in the health sector in Indonesia, especially with the emergence of the COVID-19 Pandemic. This study examines text analysis of telemedicine news coverage during the COVID-19 pandemic in Indonesia using sentiment analysis, Named Entity Recognition (NER), topic modeling, and Social Network Analysis (SNA). This research aims to gain an in-depth understanding of issues, public perceptions, social networks, and topics related to the use of telemedicine in dealing with a pandemic. This study provides a comprehensive understanding of telemedicine coverage during the COVID-19 pandemic in Indonesia by combining four methods. The findings of this research can provide valuable insights for stakeholders in optimizing the use of telemedicine, understanding public perceptions, and building effective collaborations in handling pandemics.Keywords: telemedicine, sentiment analysis, Named Entity Recognition (NER), topic modeling, social network analysis, COVID-19 AbstrakTelemedicine menjadi fenomena yang semakin relevan dalam sektor kesehatan di Indonesia, terutama dengan munculnya Pandemi COVID-19. Penelitian ini mengkaji analisis teks pemberitaan telemedicine selama pandemi COVID-19 di Indonesia dengan menggunakan analisis sentimen, Named Entity Recognition (NER), Topic Modeling, dan Social Network Analysis (SNA). Tujuan penelitian ini adalah untuk memperoleh pemahaman yang mendalam tentang isu-isu, persepsi masyarakat, jaringan sosial, dan topik-topik yang terkait dengan pemanfaatan telemedicine dalam menghadapi masalah kesehatan di masa pandemi. Penggunaan gabungan empat metode analisis agar dapat menyajikan pemahaman yang komprehensif tentang pemberitaan telemedicine selama pandemi COVID-19 di Indonesia. Hasil penelitian menunjukkan adanya kecenderungan sentimen positif dan netral terhadap telemedicine dan keberadaannya sangat membantu masalah kesehatan di masa Pandemi COVID-19. Selain itu pejabat pemerintah adalah nama yang paling sering muncul dalam pemberitaan telemedicine  yang memiliki makna peranan sentral pemerintah dalam masalah kesehatan sangat dibutuhkan. Penelitian ini diharapkan dapat memberikan wawasan berharga bagi para pemangku kepentingan dalam mengoptimalkan pemanfaatan telemedicine, memahami persepsi masyarakat, dan membangun kolaborasi yang efektif dalam penanganan pandemi.Kata Kunci: telemedicine, analisis sentimen, Named Entity Recognition (NER), social network analysis, topic modelling, COVID-19
Comparison of Kernel Smoothing and Local Polynomial Smoothing Method in Overcoming Age Heaping Nadia Arsyta Putri; Erni Tri Astuti; Lalu Moh Arsal Fadila; Salsabil Syadza Hafizhah
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 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.v2023i1.312

Abstract

Age data plays an important role in every aspect yet there are found age misreporting. It involves digit preference that causes build up in a certain age. Digit preference in demography is called age heaping that often happens at age with 0 and 5 as the last digit. Age heaping induces poor data quality and data bias that could influence government policy making. Two indicators used to detect age heaping are Whipple Index (WI) and Myers Blended Index (MBI). Methods to cope with age heaping are nonparametric regression approaches which are Kernel Smoothing and Local Polynomial Smoothing. The objective of this research is to measure and elevate the quality of population age data and population mortality data in Sensus Penduduk (SP) 2020 as well as comparing methods between Kernel Smoothing and Local Polynomial Smoothing. The data being used in this paper is SP2020 which the research variables are age population, age of death, and total population. The result shows that the data quality of total population death is inaccurate compared to total population thus needs a smoothing process to improve age data to population data accuration. The method that has better accuracy is the Local Polynomial Smoothing method.
Co-Authors Dewi Krismawati Lalu Moh Arsal Fadila Nadia Arsyta Putri Pramana, Setia Salsabil Syadza Hafizhah Satria Bagus Panuntun