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All Journal Indonesian Journal of Applied Statistics Indonesian of Health Information Management Journal (INOHIM) PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Erni Tri Astuti
Politeknik Statistika STIS
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science 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
Keterkaitan Indeks Harga Konsumen (IHK) Kelompok Bahan Makanan dengan Kelompok Makanan Jadi, Minuman, Rokok, dan Tembakau di Indonesia Tahun 2014-2019 (Pendekatan Vector Error Correction Model) Lira Azima; Erni Tri Astuti
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.54988

Abstract

Pricing a commodity depends on the price of other commodities. As the largest contributor to inflation, the pattern of price movements in CPI of prepared food, beverages, cigarette, and tobacco group is inseparable from CPI of foodstuff group as the raw material for that group. This condition indicates that in analyzing the pattern of price movements of a commodity, it cannot be separated from the influence of other commodities. The aims of the study is to examine the linkages between CPI of foodstuff group and CPI of prepared food, beverages, cigarette, and tobacco group, also its response and contribution when there is shock during January 2014 until December 2019 in Indonesia using Vector Error Correction Model (VECM). The results suggest that in long-term CPI of prepared food, beverages, cigarette, and tobacco group has positive effect on CPI of foodstuff group. Impulse Response Function (IRF) shows that shocks to CPI of foodstuff group is positively responded by CPI of prepared food, beverages, cigarette, and tobacco group, and vice versa. In addition, Forecast Error Variance Decomposition (FEVD) show that the variation of CPI of prepared food, beverages, cigarette, and tobacco group are dominated by contribution of CPI of foodstuff group.Keywords : consumer price index, VECM, impulse response function, forecast error variance decomposition
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 Lira Azima Nadia Arsyta Putri Pramana, Setia Salsabil Syadza Hafizhah Satria Bagus Panuntun