Implementasi Teks Mining Pada Website Kemenkes Dengan Metode LDA Menggunakan Algoritma K-Means

Ari Setiawan; Deden Wahiddin; Cici Emilia Sukmawati

doi:10.32493/informatika.v9i2.38971

Authors

Ari Setiawan Universitas Buana Perjuangan Karawang
Deden Wahiddin Universitas Buana Perjuangan Karawang
Cici Emilia Sukmawati Universitas Buana Perjuangan Karawang

DOI:

https://doi.org/10.32493/informatika.v9i2.38971

Keywords:

Text Mining, Topic Modeling, LDA Model, K-Means

Abstract

This research aims to improving the accessibility and management of health information on the Ministry of Health (Kemenkes) website. Before this research was conducted, content on the Ministry of Health's website was scattered without a clear structure, making it difficult for users to find the health information they needed quickly and efficiently. This results in a decrease in the quality of the user experience and a potential decrease in trust in official health information sources. With the aim of making it easier for users to find relevant information, this research uses the K-Means algorithm to group website content based on themes. Through the text mining method, five main clusters were identified, covering topics such as emergency health, certain diseases, and innovations in handling COVID-19. The results show an increase in navigation efficiency with clustering accuracy reaching 72%. The conclusion of this research is that this grouping succeeded in improving the structure and quality of information on the Ministry of Health's website, supporting data-based decision making, and improving public health services.

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