Analisis Sentimen dan Pemodelan Topik Aplikasi Telemedicine Pada Google Play Menggunakan BiLSTM dan LDA

Authors

  • Siti Mutmainah Universitas Islam Indonesia, Yogyakarta
  • Dhomas Hatta Fudholi Universitas Islam Indonesia, Yogyakarta
  • Syarif Hidayat Universitas Islam Indonesia, Yogyakarta

DOI:

https://doi.org/10.30865/mib.v7i1.5486

Keywords:

Sentiment Analysis, Bidirectional Long Short-Term Memory, Telemedicine, Topic Modelling, Latent Dirichlet Allocation

Abstract

The pandemic caused by the 2019 coronavirus has revitalized telemedicine as information and communication technology-based health services and as a medium for doctors' services in diagnosing, treating, preventing and evaluating health conditions. One of the telemedicine service applications in Indonesia is Alodokter, Halodoc, KlikDokter, SehatQ and YesDok. Previous research on the same domain, namely applications telemedicine uses machine learning to perform sentiment modeling. This research performs sentiment analysis using the BiLSTM method (Bidirectional Long Short-Term Memory) which can better represent contextual information and can read user feedback information in both directions. Then sentiment analysis is described explicitly to identify topics from user sentiment using LDA (Latent Dirichlet Allocation). User feedback was collected on August 14, 2022 which was obtained in the five applications totaling 244,098. The results of the analysis on feedback obtained were 112,013 positive sentiments, 34,853 neutral sentiments and 97,228 negative sentiments. The BiLSTM and Word2Vec models used have a good performance in classifying sentiments, namely 95%, while the topic modeling for each sentiment has a coherence value of 0.6437 on positive topics, 0.6296 neutral sentiments and 0.6132 negative sentiments.

References

World Health Organization, “Implementing telemedicine services during COVID-19: guiding principles and considerations for a stepwise approach,†https://www.who.int/publications/i/item/WPR-DSE-2020-032, pp. 1–25, 2020, [Online]. Available: https://apps.who.int/iris/handle/10665/336862.

M. Kay, J. Santos, and M. Takane, “Telemedicine: Opportunities and developments in Member States,†Observatory, vol. 2, p. 96, 2010, [Online]. Available: http://www.who.int/goe/publications/goe_telemedicine_2010.pdf.

Kementerian Kesehatan RI, “Surat Edaran Menteri Kesehatan Republik Indonesia Nomor Hk.02.01/Menkes/303/2020 Tahun 2020,†Tentang Penyelenggaraan Pelayanan Kesehat. Melalui Pemanfaat. Teknol. Inf. Dan Komun. Dalam Rangka Pencegah. Penyebaran Corona Virus Dis. 2019, vol. 2016, pp. 2–5, 2020.

www.orami.co.id, “7 Aplikasi Telemedicine yang Sediakan Obat Gratis untuk Pasien Isolasi Mandiri, Catat!,†www.orami.co.id, p. https://www.orami.co.id/magazine/aplikasi-telemedi, 2022.

K. Khan, B. Baharudin, A. Khan, and A. Ullah, “Mining opinion components from unstructured reviews: A review,†J. King Saud Univ. - Comput. Inf. Sci., vol. 26, no. 3, pp. 258–275, 2014, doi: 10.1016/j.jksuci.2014.03.009.

R. timlo. ne. Ario, “Jumlah Unduhan Aplikasi di Google Play Capai 28 Miliar,†timlo.net, 2020. https://timlo.net/baca/115867/jumlah-unduhan-aplikasi-di-google-play-capai-28-miliar/ (accessed Jul. 12, 2022).

Suwanda Aditya Saputra, Didi Rosiyadi, Windu Gat, and Syepry Maulana Husain, “Analisis Sentimen E-Wallet Pada Google Play Menggunakan Algoritma Naive Bayes Berbasis Particle Swarm Optimization,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 3, pp. 377–382, 2017.

M. B. N. Taj and G. S. Girisha, “Insights of strength and weakness of evolving methodologies of sentiment analysis,†Glob. Transitions Proc., vol. 2, no. 2, pp. 157–162, 2021, doi: 10.1016/j.gltp.2021.08.059.

S. Sanagar and D. Gupta, “Unsupervised Genre-Based Multidomain Sentiment Lexicon Learning Using Corpus-Generated Polarity Seed Words,†IEEE Access, vol. 8, pp. 118050–118071, 2020, doi: 10.1109/ACCESS.2020.3005242.

Z. Hameed and B. Garcia-Zapirain, “Sentiment Classification Using a Single-Layered BiLSTM Model,†IEEE Access, vol. 8, pp. 73992–74001, 2020, doi: 10.1109/ACCESS.2020.2988550.

G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, “Sentiment analysis of comment texts based on BiLSTM,†IEEE Access, vol. 7, pp. 51522–51532, 2019, doi: 10.1109/ACCESS.2019.2909919.

S. Siami-Namini, N. Tavakoli, and A. S. Namin, “The Performance of LSTM and BiLSTM in Forecasting Time Series,†Proc. - 2019 IEEE Int. Conf. Big Data, Big Data 2019, pp. 3285–3292, 2019, doi: 10.1109/BigData47090.2019.9005997.

N. Afrianto, D. H. Fudholi, and S. Rani, “Prediksi Harga Saham Menggunakan BiLSTM dengan Faktor Sentimen Publik,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 41–46, 2022, doi: 10.29207/resti.v6i1.3676.

O. Oyebode, F. Alqahtani, and R. Orji, “Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews,†IEEE Access, vol. 8, pp. 111141–111158, 2020, doi: 10.1109/ACCESS.2020.3002176.

N. A. S. ER, “IMPLEMENTASI LATENT DIRICHLET ALLOCATION (LDA) UNTUK KLASTERISASI CERITA BERBAHASA BALI,†J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, pp. 127–134, 2021, doi: 10.25126/jtiik.202183556.

Y. Zhang and L. Zhang, “Movie Recommendation Algorithm Based on Sentiment Analysis and LDA,†Procedia Comput. Sci., vol. 199, pp. 871–878, 2022, doi: 10.1016/j.procs.2022.01.109.

C. Naury, D. H. Fudholi, and A. F. Hidayatullah, “Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM,†J. Media Inform. Budidarma, vol. 5, no. 1, p. 24, 2021, doi: 10.30865/mib.v5i1.2556.

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,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 123–131, 2021, doi: 10.29207/resti.v5i1.2587.

Janu Akrama Wardhana and Yuliant Sibaroni, “Aspect Level Sentiment Analysis on Zoom Cloud Meetings App Review Using LDA,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 631–638, 2021, doi: 10.29207/resti.v5i4.3143.

H. A. Prakosa, A. B. Riyanto, and S. Nasiroh, “Analisis sentimen dan pemodelan topik pandemi Covid-19 pada media sosial Twitter menggunakan Naïve Bayes Classifier dan Latent Dirichlet Allocation,†Jnanaloka, pp. 73–78, 2021, doi: 10.36802/jnanaloka.2021.v2-no2-73-78.

A. Hendra and F. Fitriyani, “Analisis Sentimen Review Halodoc Menggunakan Nai ̈ve Bayes Classifier,†JISKA (Jurnal Inform. Sunan Kalijaga), vol. 6, no. 2, pp. 78–89, 2021, doi: 10.14421/jiska.2021.6.2.78-89.

N. R. Wardani and A. Erfina, “ANALISIS SENTIMEN MASYARAKAT TERHADAP LAYANAN KONSULTASI DOKTER MENGGUNAKAN ALGORITMA NAIVE BAYES,†SISMATIK (Seminar Nas. Sist. Inf. dan Manaj. Inform., pp. 11–18, 2021.

E. Indrayuni, A. Nurhadi, and D. A. Kristiyanti, “Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc,†Fakt. Exacta, vol. 14, no. 2, p. 64, 2021, doi: 10.30998/faktorexacta.v14i2.9697.

G. Xu, Z. Yu, H. Yao, F. Li, Y. Meng, and X. Wu, “Chinese Text Sentiment Analysis Based on Extended Sentiment Dictionary,†IEEE Access, vol. 7, pp. 43749–43762, 2019, doi: 10.1109/ACCESS.2019.2907772.

F. Koto and G. Y. Rahmaningtyas, “Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs,†Proc. 2017 Int. Conf. Asian Lang. Process. IALP 2017, vol. 2018-Janua, no. December, pp. 391–394, 2018, doi: 10.1109/IALP.2017.8300625.

Alvi Rahmy Royyan and Erwin Budi Setiawan, “Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in Twitter,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 78–84, 2022, doi: 10.29207/resti.v6i1.3525.

E. I. Setiawan and I. Lestari, “Stance Classification Pada Berita Berbahasa Indonesia Berbasis Bidirectional LSTM,†J. Intell. Syst. Comput., vol. 3, no. 1, pp. 41–48, 2021, doi: 10.52985/insyst.v3i1.148.

D. M. Blei, A. Y. Ng, and M. T. Jordan, “Latent dirichlet allocation,†Adv. Neural Inf. Process. Syst., 2002.

D. Ridhwanulah and D. H. Fudholi, “Pemodelan Topik pada Cuitan tentang Penyakit Tropis di Indonesia dengan Metode Latent Dirichlet Allocation,†J. Ilm. SINUS, vol. 20, no. 1, p. 11, 2022, doi: 10.30646/sinus.v20i1.589.

Downloads

Published

2023-01-28

Issue

Vol. 7 No. 1 (2023): Januari 2023

Section

Articles

License

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

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).