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All Journal Indonesian of Health Information Management Journal (INOHIM) Eduvest - Journal of Universal Studies
Dewi Krismawati
Badan Pusat Statistik
Aerospace Engineering Computer Science & IT Health Professions Neuroscience Social Sciences

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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
ICD Coding Automation Model of Retinal Detachment Case Using Support Vector Machine and Random Forest Dyah Kurniawati; Mieke Nurmalasari; Hosizah Markam; Dewi Krismawati
Eduvest - Journal of Universal Studies Vol. 6 No. 5 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i5.53072

Abstract

Health Information Management (HIM) professionals are responsible for maintaining the consistency of ICD-based clinical codes for the health reimbursement and health analytics through the review of medical documentation. The complexity of coding rules and clinical pathways increases the risk of miscoding, but the implementation of Electronic Medical Record (EMR) opens opportunities for the development of automation of ICD coding. This study aims to build an ICD code automation model for retinal detachment cases from eye referral hospital using artificial intelligence through clinical text classification with Natural Language Processing (NLP) and Machine Learning (ML) algorithms. The dataset includes disease resumes, physical examinations, diagnoses, medical procedures, surgical records, and therapies from 300 inpatients. Text preprocessing uses the NLTK library through sentence splitting, abbreviation expansion, case folding, stop word removal, and tokenization functions. Data preparation involves splitting data (80:20 ratio), feature extraction with TF-IDF Vectorizer, and 5-fold cross validation. Classification modeling uses Support Vector Machine (SVM) and Random Forest (RF). Evaluation of the SVM model showed an accuracy of 0.82 (precision 0.84; recall 0.82; F1-Score 0.82), while the RF model achieved an accuracy of 0.87 (precision 0.88; recall 0.87; F1-Score 0.87). Based on confusion metrics, the correct predictions for classes H33.0, H33.2, and H33.4 on SVM are 79, 87, and 80, while RF reaches 83, 88, and 91. The development of this automation requires HIM professional’s role in ensuring the quality of EMR data and accuracy of ICD code as well as intensive model training to handle the complexity of clinical data.
Co-Authors Dyah Kurniawati Erni Tri Astuti Hosizah Hosizah Nurmalasari, Mieke Pramana, Setia Satria Bagus Panuntun