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KNOWLEDGE DISCOVERY BERBASIS TOPIC MINING TERHADAP KEBUTUHAN INFORMASI KESEHATAN PENGGUNA

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Understanding the user’s need for health information has become increasingly important as the use of digital health services has grown. However, the unstructured data of user-generated questions presents challenges in capturing and analyzing these needs. This study contributes to addressing SDG 3 (Good Health and Well-Being) by using topic mining-based knowledge discovery to identify the main topics arising from user questions submitted through the “Tanya Dokter” feature on the Alodokter platform. A total of 8,550 questions were obtained through web scraping over the period from July 2024 to June 2025. The collected data were preprocessed and subsequently analyzed using seven topic modeling approaches: Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Latent Semantic Analysis (LSA), Non-negative Matrix Factorization (NMF), BERTopic, Top2Vec, and ProdLDA. To assess model performance, the coherence metric (c_v) was employed to identify the most effective method. Among these techniques, NMF achieved the best results, producing the highest coherence score of 0.67 with six well-defined topics. The findings show six primary areas of concern: pregnancy; menstruation and contraceptive management; general health and minor ailments; infant care; dermatological conditions; and musculoskeletal and other physical complaints. General health-related issues occurred most frequently, particularly during seasonal transitions, while menstruation and contraceptive management received the least attention despite menstruation contributing to women’s health risks and the use of contraceptives helping to reduce maternal mortality in Indonesia. These findings provide useful insights for health platforms like Alodokter to improve information delivery and health literacy, thereby enhancing online health services and supporting the achievement of SDG 3. Keywords: Topic Mining, Topic Modeling, Knowledge Discovery, Online Health Communities, Alodokter

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Detail Information

Series Title

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Call Number

T1893712025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 99 hlm.; ilus.; tab, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.310 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Informasi
Penemuan Kesehatan--Informasi Kesehatan

Specific Detail Info

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Statement of Responsibility

MI

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