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Identify Reviews of Pedulilindungi Applications using Topic Modeling with Latent Dirichlet Allocation Method
https://doi.org/10.22146/ijccs.86025 Layli Hardiyanti(1*), Dina Anggraini(2), Ana Kurniawati(3)
(1) Gunadarma University
(2) Gunadarma University
(3) Gunadarma University
(*) Corresponding Author
Abstract
The emergence of Covid-19 in December 2019 has disrupted life worldwide, including Indonesia. The government has made various efforts to control the pandemic, one of which is the development of an application called PeduliLindungi. This app aims to be a reliable tool for the government and the entire community during the pandemic. As a new regulation, the use of PeduliLindungi has prompted numerous reviews assessing its quality and performance. With the app's emergence and growth, various topics have emerged and become trending among the public. These topics were identified through user reviews of the PeduliLindungi app, using the Latent Dirichlet Allocation (LDA) algorithm. The data, consisting of 15,522 reviews, was collected from the Google Play Store and underwent pre-processing, including dictionary and corpus creation, determining the number of topics, and modeling with LDA. The resulting topic modeling process generated the ten most prominent topics. The outcomes were visualized using word clouds and topic distribution graphs, representing the most discussed aspects of the PeduliLindungi app among users. These topics are considered diverse since each issue has no relation or similarity to one another.
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Doni. (29 Sep 2021). “Lewat Digitalisasi Pemerintah Integrasikan Pelayanan Publik Berbasis Elektronik,” 29 Sept 2021 [Online]. Available: https://www.kominfo.go.id/content/ detail/37259/lewat-digitalisasi-pemerintah-integrasikan-pelayanan-publik-berbasiselektronik/0/ berita [Accessed: 20-Oct-2021] Indonesia.go.id. (19 Agustus 2021). “Kominfo dan Gojek Perluas Akses Pedulilindungi,”. [Online]. Available: https: https://www.indonesia.go.id/ragam/komoditas/ ekonomi/kominfo-gojek-perluas-aksespedulilindungi?lang=1?lang=1?lang=1?lang=1#:~:text=PeduliLindungi% 20memiliki=PeduliLindungi%20memiliki%20fungsi%20tracing%20%28penelusuran%29%2C%20tracking%20%28pelacakan%29%2C%20dan,Terkait%20privasi%2C%20PeduliLindungi%20sangat%20memperhatikan%20kerahasiaan%20pribadi%20pengguna [Accessed:11-Nov-2021] Alfanzar, I, Khalid, and Rozas, I. “Topic Modelling Skripsi Menggunakan Metode Latent Dirichlet Allocation. Jurnal Sistem Informasi,” JSil (Jurnal Sistem Informasi)., vol. 7, no. 1, pp. 7-13, Mar. 2020 [Online]. Available: DOI: 10.30656/jsii.v7i1.2036 [Accesed: 08-Nov-2022] Arianto, Wicaksano and Anuraga, Gangga, “Pemodelan Topik Pengguna Twitter Mengenai Aplikasi “Ruangguru”,” Jurnal ILMU DASAR, vol. 21 no. 2, pp. 149-154, Juli. 2020 [Online]. Available: DOI: 10.30645/j-sakti.v4i1.188 [Accesed: 08-Nov-2022] Aditya, B. (2020). “Topic Modelling pada Data Artikel Peneliti Penerima Dana PDUPT Menggunakan Gensim.” ITS (Institue of sepuluh November, pp. 1-20, Nov. 2020 [Online]. Available: https://123dok.com/document/zllr0wrz-topic-modelling-artikel-peneliti-penerima-pdupt-menggunakan-gensim.html [Accesed: 10-Nov-2022] Suhartono, D. “Probabilistic Latent Semantic Analysis (PLSA) untuk Klasifikasi Dokumen Teks Berbahasa Indonesia,” Dec. 2014 [Online]. Available: http://arxiv.org/abs/1512.00576. [Accesed: 10-Nov-2022] Listari. “Topic Modeling Menggunakan Latent Dirichlet Allocation (Part 2): Topic Modeling with Gensim (Python),” 2019 [Online]. Available: https://medium.com/@listari.tari/topic-modeling-menggunakan-latent-dirchlect-allocation-part-2-topic-modeling-with-gensim-c9ffd196cb87. [Accesed: 15-Nov-2022] J. C. Campbell, A. Hindle and a. E. Stroulia, "Latent Dirichlet Allocation: Extracting Topics," 2014 [Online]. D. M. Blei, "Probabilistic Topic Model," communications of the acm, vol. 55, 2012 [Online]. D. M. Blei, "Latent Dirichlet Allocation," Machine Learning Research 3, pp. 933-1022, 2003 [Online]. Putra, I. M., & Kusumawardani, R. P. (2017). Analisis Topik Informasi Publik Media Sosial di Surabaya Menggunakan Pemodelan Latent Dirichlet Allocation (LDA). JURNAL TEKNIK ITS Vol. 6, No. 2, A312, 2017 [Online]. Available: DOI: 10.12962/j23373539.v6i2.23205 [Accesed: 15-Nov-2022] H. S. Koh and M. Fienup, “Topic modeling as a tool for analyzing library chat transcripts,” Inf. Technol. Libr., vol. 40, no. 3, 2021 [Online]. Available: DOI: 10.6017/ital.v40i3.13333 [Accessed: 15-Nov-2022] D. Mimno, H. M. Wallach, E. Talley, M. Leenders, and A. McCallum, “Optimizing semantic coherence in topic models,” EMNLP 2011 - Conf. Empir. Methods Nat. Lang. Process. Proc. Conf., no. 2, pp. 262–272, 201 [Online].
DOI: https://doi.org/10.22146/ijccs.86025
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