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All Journal Media Pustakawan Prosiding Seminar Nasional Official Statistics
Jelita, Mutia
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Humanities Computer Science & IT Economics, Econometrics & Finance Library & Information Science Social Sciences

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Text Mining dengan Topic Modelling LDA dari Pertanyaan Gelar Wicara Literasi Perpustakaan Nasional RI Jelita, Mutia
Media Pustakawan Vol. 31 No. 3 (2024): Desember
Publisher : Perpustakaan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37014/medpus.v31i3.5237

Abstract

In 2023, the National Library of Indonesia, through its Library Analysis and Reading Culture Development Centre, organised several literacy talk shows. Each event was documented in minutes (.doc or .pdf format) recording the speakers' material, questions, and answers. As events increased, so did the volume of minutes. This research aimed to identify frequently discussed topics using Text Mining with a Topic Modelling approach. Latent Dirichlet Allocation was applied and evaluated by perplexity values (a measure of model quality). Results showed the optimal number of topics to represent the dataset was three, with the lowest perplexity value of 470.922 at the 30th iteration. The three main topics identified were reading interest and the need for books in schools and regions, libraries’ role in improving children’s literacy, and librarians' role in inclusive literacy programmes for both young and old, including health literacy. Frequent words were literacy, library, reading, books, and children.
Analisis Clustering Menggunakan Metode K-Means untuk Mengelompokkan Kabupaten/Kota di Indonesia berdasarkan UnsurUnsur Pembangun Literasi Masyarakat (UPLM) Jelita, Mutia
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2188

Abstract

The government needs to identify which districts/cities require more guidance in the library sector by utilizing the data obtained. This study aims to conduct clustering analysis using the K-Means method to categorize districts/cities in Indonesia based on the Elements of Community Literacy Development (UPLM) data in 2023. The Elbow method is applied to determine the optimal number of clusters. The results of the study reveal four clusters: Cluster I consists of 62 districts/cities with characteristics of having four high-value UPLMs; Cluster II includes 84 districts/cities with no high-value UPLMs; Cluster III encompasses 222 districts/cities with one high-value UPLM; and Cluster IV includes 146 districts/cities with two highvalue UPLMs. Based on these clusters, the government, particularly the National Library of Indonesia, can focus on providing more targeted guidance, especially in Cluster II, which includes districts like Bener Meriah, Indragiri Hilir, Bogor, Sikka, and Yahukimo.
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