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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Telematika : Jurnal Informatika dan Teknologi Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jiko (Jurnal Informatika dan komputer) Jurnal Teknologi Terpadu Nusantara Science and Technology Proceedings International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Muhaimin, Amri
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation

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Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10797

Abstract

Purpose: Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6%, and for the future, the government has set a target of up to 14% in 2024. Rapid technological developments and freedom of expression on the internet produce review text data that can be analyzed for evaluation. This study analyzes the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation.Design/methodology/approach: The methodology used in this study is Latent Dirichlet Allocation. The data was collected from twitter with the keyword 'stunting'. After, the data was cleaned and then modeled using the Latent Dirichlet Allocation.Findings/results: The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'children', 'decrease', 'number', 'prevention', and 'nutrition' are among the words that often appear on stunting.Originality/value/state of the art: This study uses the keyword stunting and analyzes it. Social media analytics show that the people of Indonesia are primarily aware of stunting. Also, the Latent Dirichlet Analysis can be used to create the model.
Co-Authors Damaliana, Aviolla Terza Dwi Arman Prasetya Endah Setyowati Fahrudin, Tresna Maulana Hanif Assalmi, Fityan Heidy Arviani Kusuma, Ade Lisanthoni, Angela Maulida H, Kartika Prabowo, Hendri Rahmawati, Adinda Aulia Regita Putri Permata Sari, Allan Ruhui Fatma Sari, Allan Ruhui Fatmah Selayanti, Nabilah Sri Hidayati Suhartono Syifa Syarifah Alamiyah Taufik, Ikbar Athallah Trimono Trimono, Trimono Wahyu Syaifullah Jauharis Saputra