Topic Modelling of Disaster Based on Indonesia Tweet Using Latent Dirichlet Allocation

Aninditya Anggari Nuryono, Iswanto Iswanto, Alfian Ma'arif, Rizal Kusuma Putra, Yabes Dwi Nugroho H, Muhammad Iman Nur Hakim

Abstract


Twitter (now X) is a critical social media platform for disseminating information during crises. This study models disaster-related topics from Indonesian-language tweets using Latent Dirichlet Allocation (LDA). From a dataset of 8,718 tweets collected from official sources like BMKG and BNPB, we performed several preprocessing steps, including case folding, stop word removal, stemming, and normalization of slang and abbreviations. The optimal number of topics was determined using coherence scores, with the model achieving a peak coherence value of approximately 0.57. Keywords such as “banjir”, “kecelakaan”, “tanah longsor,” and others were used to collect data from Twitter accounts like "BMKG" (Meteorology, Climatology, and Geophysical Agency) and "BNPB" (National Disaster Management Agency). The results revealed that the most frequently discussed topics with high coherence values were “angin topan” “topan”, “virus corona”, “kecelakaan”, “tenggelam”, “badai”, “angin puting.” A word cloud was used to visualize these disaster-related topics.

Keywords


Latent Dirichlet Allocation; Twitter, Disaster; Topic Modelling; Word Cloud

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References


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DOI: https://doi.org/10.31763/simple.v7i1.132

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Signal and Image Processing Letters
ISSN Online: 2714-6677 | Print: 2714-6669
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