Analisis Topic-Modelling Menggunakan Latent Dirichlet Allocation (LDA) Pada Ulasan Sosial Media Youtube

Vika Alpiana, Abu Salam, Farrikh Alzami, Ifan Rizqa, Diana Aqmala

Abstract


This research explores the role of Micro, Small, and Medium Enterprises (MSMEs) in the Indonesian economy, focusing on sales and marketing challenges in the era of social media, especially YouTube. With millions of individuals using this platform to share product insights, reviews, and experiences, MSMEs need to receive relevant feedback. This study applies text mining, particularly the topic modeling analysis method with Latent Dirichlet Allocation (LDA), to analyze user comments on MSME videos, with an emphasis on Lumpia Gang Lombok Semarang on YouTube. Through the application of LDA, the identification of ten main topics is conducted, with the highest coherence value reaching 0.414027. The visualization of the intertopic distance map provides an understanding of the relationships between topics and dominant words. Comment analysis provides valuable insights into user preferences and perceptions of products, supporting MSMEs in understanding customer satisfaction and enhancing value for those enterprises. These findings also affirm the effectiveness of YouTube as a relevant data source for understanding public preferences for MSME products. This research details text processing methods, including extraction, cleaning, tokenization, normalization, removal of stopwords, and stemming. With this approach, the research not only provides insights into topic analysis in the context of social media but also makes a valuable contribution to the development and marketing of MSMEs through a better understanding of social media data, especially on the YouTube platform.

Keywords


MSMEs; Topic Modeling Analysis; Latent Dirichlet Allocation (LDA); YouTube; User Preferences

Full Text:

PDF

References


M. Sinurat, R. N. Ilham, and W. Cahyadi, “Strategic for Increasing Economic Value Added in the MSME Sector in the North Sumatra Region with the Acceleration Board Method and Initial Public Offering,” in Proceedings of the International Conference on Strategic Issues of Economics, Business and, Education (ICoSIEBE 2020), 2021. doi: 10.2991/aebmr.k.210220.019.

Raden Ariansyah Kamil, “Research on MSMEs in Indonesia : Bibliometric Analysis,” Int J Sci Res Sci Technol, pp. 171–178, Sep. 2022, doi: 10.32628/IJSRST229527.

Agni Arifah et al., “Strengthening Business Management and Simple Accounting Understanding for the Rengginang Simanalagi UMKM in Jambuluwuk Village, Ciawi District,” Jurnal Pengabdian Masyarakat Formosa, vol. 1, no. 5, pp. 557–566, Dec. 2022, doi: 10.55927/jpmf.v1i5.2158.

S. Tinggi, I. Dakwah, and K. Islam, “KOMUNIKASI DAKWAH MELALUI MEDIA PEMBELAJARAN YOUTUBE Helnafri Ankesa,” TABAYYUN: Jurnal Komunikasi dan Penyiaran Islam, vol. 3, no. 2, p. 10, 2022, [Online]. Available: https://ejournal-stidkibogor.ac.id/index.php/tabayyun

R. Kurniawan, F. Lestari, A. S. Batubara, M. Z. A. Nazri, K. Rajab, and R. Munir, “Indonesian Lexicon-Based Sentiment Analysis of Online Religious Lectures Review,” in 2021 International Congress of Advanced Technology and Engineering (ICOTEN), IEEE, Jul. 2021, pp. 1–5. doi: 10.1109/ICOTEN52080.2021.9493530.

A. Adak, B. Pradhan, N. Shukla, and A. Alamri, “Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique,” Foods, vol. 11, no. 14, p. 2019, Jul. 2022, doi: 10.3390/foods11142019.

I. K. Susanto, “Analisis Sentimen dan Topic Modelling Pada Pembelajaran Online di Indonesia Melalui Twitter,” JOINTECS (Journal of Information Technology and Computer Science), vol. 6, no. 2, p. 85, May 2021, doi: 10.31328/jointecs.v6i2.2350.

Della Maulidiya, “Topic Modelling using Latent Dirichlet Allocation (LDA) to Investigate the Latent Topics of Mathematical Creative Thinking Research in Indonesia,” J. Intell. Comput. Health Inform., vol. 3, no. 2, pp. 34–35, 2022, doi: 10.26714/jichi.v3i2.11428.

A. Andy and U. Andy, “Understanding Communication in an Online Cancer Forum: Content Analysis Study,” JMIR Cancer, vol. 7, no. 3, p. e29555, Sep. 2021, doi: 10.2196/29555.

M. Rüdiger, D. Antons, A. M. Joshi, and T.-O. Salge, “Topic modeling revisited: New evidence on algorithm performance and quality metrics,” PLoS One, vol. 17, no. 4, p. e0266325, Apr. 2022, doi: 10.1371/journal.pone.0266325.

K. Amaradiena and T. Widarmanti, “SEIKO : Journal of Management & Business LDA-Topic Modeling: Menggunakan Ulasan Pengguna Untuk Meningkatkan User Experience (Studi pada PeduliLindungi),” SEIKO : Journal of Management & Business, vol. 6, no. 1, pp. 943–953, 2023, doi: 10.37531/sejaman.v6i1.3802.

N. L. P. M. Putu, Ahmad Zuli Amrullah, and Ismarmiaty, “Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 123–131, Feb. 2021, doi: 10.29207/resti.v5i1.2587.

D. Z. T. Kannitha, M. Mustafid, and P. Kartikasari, “PEMODELAN TOPIK PADA KELUHAN PELANGGAN MENGGUNAKAN ALGORITMA LATENT DIRICHLET ALLOCATION DALAM MEDIA SOSIAL TWITTER,” Jurnal Gaussian, vol. 11, no. 2, pp. 266–277, Aug. 2022, doi: 10.14710/j.gauss.v11i2.35474.

M. K H, H. Zainuddin, and Y. Wabula, “Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm,” Journal of Applied Engineering and Technological Science (JAETS), vol. 4, no. 1, pp. 390–399, Dec. 2022, doi: 10.37385/jaets.v4i1.1143.

M. Habibi, A. Priadana, A. B. Saputra, and P. W. Cahyo, “Topic Modelling of Germas Related Content on Instagram Using Latent Dirichlet Allocation (LDA),” in Proceedings of the International Conference on Health and Medical Sciences (AHMS 2020), Paris, France: Atlantis Press, 2021. doi: 10.2991/ahsr.k.210127.060.

A. R. Lubis, S. Prayudani, M. Lubis, and O. Nugroho, “Sentiment Analysis on Online Learning During the Covid-19 Pandemic Based on Opinions on Twitter using KNN Method,” in 2022 1st International Conference on Information System & Information Technology (ICISIT), IEEE, Jul. 2022, pp. 106–111. doi: 10.1109/ICISIT54091.2022.9872926.

M. S. Alrajak, I. Ernawati, I. N. Fakultas, and I. Komputer, ANALISIS SENTIMEN TERHADAP PELAYANAN PT PLN DI JAKARTA PADA TWITTER DENGAN ALGORITMA K-NEAREST NEIGHBOR (K-NN). 2020.

W. Wiranto and Mila Rosyida Uswatunnisa, “Topic Modeling for Support Ticket using Latent Dirichlet Allocation,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 6, pp. 998–1005, Dec. 2022, doi: 10.29207/resti.v6i6.4542.

A. D. Cahyani and T. Mardiana, “SENTIMENT ANALYSIS OF DIGITAL WALLET SERVICE USERS USING NAÏVE BAYES CLASSIFIER AND PARTICLE SWARM OPTIMIZATION,” Jurnal Riset Informatika, vol. 2, no. 4, pp. 241–250, Sep. 2020, doi: 10.34288/jri.v2i4.160.

I. Uglanova and E. Gius, “The Order of Things. A Study on Topic Modelling of Literary Texts,” 2020. [Online]. Available: http://ceur-ws.org

S. Mifrah, “Topic Modeling Coherence: A Comparative Study between LDA and NMF Models using COVID’19 Corpus,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 4, pp. 5756–5761, Aug. 2020, doi: 10.30534/ijatcse/2020/231942020.




DOI: https://doi.org/10.30865/mib.v8i1.7127

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
Universitas Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.