Article Per Year (5 Year)
p-Index From 2020 - 2025
1.945
P-Index
This Author published in this journals
All Journal Jurnal SPEKTRUM JURNAL INSTEK (Informatika Sains dan Teknologi) Journal of Energy and Electrical Engineering (JEEE) Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Automotive Experiences Berkala Forum Studi Transportasi antar Perguruan Tinggi Signal and Image Processing Letters
Muhammad Iman Nur Hakim
Politeknik Keselamatan Transportasi Jalan
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Signal and Image Processing Letters

 1 
Topic Modelling of Disaster Based on Indonesia Tweet Using Latent Dirichlet Allocation Nuryono, Aninditya Anggari; Iswanto, Iswanto; Ma'arif, Alfian; Putra, Rizal Kusuma; Nugroho H, Yabes Dwi; Hakim, Muhammad Iman Nur
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.132

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.
Co-Authors Aditya Prima F Ainun Rahmawati Alfian Ma’arif Ambarita, Sihar Anggari Nuryono, Aninditya Astiti Astiti Awaludin Febrianto Humami, Faris I G. A. K. Diafari Djuni Hartawan Iswanto Iswanto Joko Siswanto Mariadi Kaharmen, Herman Miftahul Hidayat, Aziz Miftahul Hidayat, Aziz Mohamad Yoga S Mokhammad Rifqi Tsani Murtofik , Khafid Ni Made Ary Esta Dewi Wirastuti Nugroho H, Yabes Dwi Nurul Fitriani Nuryono, Aninditya Anggari Nyoman Pramaita Pranoto, Ethys Prasetyo, Ilham Bagus Putra, Nanda Dwi Putra, Rizal Kusuma Rizky Abrar, Alridho Shafa Nadhifah M Siti Shofiah Tito Pinandita Untung Rahardja