Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia

Amri Muhaimin, Tresna Maulana Fahrudin, Syifa Syarifah Alamiyah, Heidy Arviani, Ade Kusuma, Allan Ruhui Fatmah Sari, Angela Lisanthoni

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.

Keywords


Stunting; Sentiment Analysis; Topic Modelling

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References


H. Rahman, M. Rahmah and N. Saribulan, "Upaya Penanganan Stunting di Indonesia Analisis Bibliometrik dan Analisis Konten," Jurnal Ilmu Pemerintahan Suara Khatulistiwa (JIPSK), vol. 8, no. 1, Juni 2023.

W. H. Organization, World Health Statistics 2022:Monitoring Health for the SDGs, World Health Organization, 2022.

Novrizaldi, "Kementrian Koordinasi Bidang Pembangunan Manusia dan Kebudayaan Republik Indonesia," KEMENKO PMK, 23 Agustus 2021. [Online]. Available: https://www.kemenkopmk.go.id/menko-pmk-beberkan-kunci-atasi-gizi-buruk-dan-stunting. [Accessed September 2023].

T. A. D. B. (ADB), Asian Development Outlook (ADO) 2020: What Drives Innovation in Asia?, Asian Development Bank, 2020, p. 396.

M. I. Panigoro, A. A. Sudirman and D. Modjo, "Upaya Pencegahan dan Penanggulangan Stunting pada Balita di Wilayah Kerja Puskesmas Tilongkabila," Jurnal Ilmu Kesehatan dan Gizi (JIG), vol. 1, no. 1, pp. 47-60, 2023.

R. D. R. S. Blora, "RSUD Dr. R Soetijono Blora," 15 December 2022. [Online]. Available: https://rsudblora.blorakab.go.id/2022/12/15/mengenal-stunting-penyebab-hingga-cara-pencegahannya/#:~:text=Penyebabnya%2C%20adalah%20karena%20sang%20ibu,ikut%20memengaruhi%20kondisi%20malnutrisi%20janin. [Accessed September 2023].

S. K. Nisa, E. D. Lustiyati and A. Fitriani, "Sanitasi Penyediaan Air Bersih dengan Kejadian Stunting pada Balita," Jurnal Penelitian dan Pengembangan Kesehatan Masyarakat Indonesia, vol. 2, no. 1, pp. 17-25, 2021.

A. P. Yudaa, Z. Septinaa, A. Maharania and Y. Nurdiatami, "Tinjauan Literatur : Perkembangan Program Penanggulangan Stunting di Indonesia," Jurnal Epidemologi Kesehatan Indonesia, vol. 6, no. 2, 2022.

M. Y. Febrianta, S. Widiyanesti and S. R. R. , "Analisis Ulasan Indie Video Game Lokal pada Steam Menggunakan Analisis Sentimen dan Pemodelan Topik Berbasis Laten Dirichlrt Allocation," Journal of Animation & Games Studies, vol. 7, no. 2, 2021.

S. Karmila and V. I. Ardianti, "Metode Latent Dirichlet Allocation untuk Menentukan Topik Teks Suatu Berita," Jurnal Informatika dan Komputasi, vol. 16, no. 1, 2022.

F. F. Roji, N. G. Ginasta, Y. Cahyan, D. Rahayu and D. Ramdani, "Review Analysis of SatuSehat Application Using Support Vector Machine and Latent Dirichlet Allocation Modeling," JURNAL RISTEC : Research in Information Systems and Technology, vol. 4, no. 1, 2023.

Muhaimin, A., . S, S., & Atnanda, P. (2023, November 7). Analisis Topic Modelling pada Ulasan Aplikasi Shopee di PlayStore Menggunakan Latent Direchlet Allocation (LDA). PROSIDING SEMINAR NASIONAL SAINS DATA, 3(1), 122-133. https://doi.org/https://doi.org/10.33005/senada.v3i1.91

Mughni, M., Fahrudin, T., & Kamisutara, M. (2021). Classification of Toddler Nutritional Status Based on Antrophometric Index and Feature Discrimination using Support Vector Machine Hyperparameter Tuning. International Journal of Computer, Network Security and Information System (IJCONSIST), 2(2), 60-65.

Aqila, J. J. Sihombing, R. I. Sitorus and Arnita, "Implementasi Algoritma Support Vector Machine Untuk Analisis Sentimen Aplikasi OLX di Playstore," Journal of Informatics and Data Science (J-IDS), vol. 1, no. 2, 2022.

Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84.




DOI: https://doi.org/10.31315/telematika.v20i3.10797

DOI (PDF): https://doi.org/10.31315/telematika.v20i3.10797.g6212

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