Dissatisfaction of a Mobile-Based Application from Different Platforms Using Naïve Bayes for Sentiment Analysis and LDA for Topic Modelling
Abstract
A mobile application that is built and runs on different platforms, such as iOS (Apple App Store) and Android (Google Play Store), may not necessarily have the same user satisfaction (dissatisfaction) reviews understood by both user segments. This is due to, for example, the differences in the technology used, which ultimately result in different user behaviors. This can be observed from the average ratings on each platform, even though it is the same application. Therefore, this research aims to provide a foundation for the assumptions made. The case study used is the Satu Sehat mobile application, a widely utilized health service application. Text mining methods: sentiment analysis using Naive Bayes and topic modeling using Latent Dirichlet Allocation (LDA) were chosen due to their relevance to the research objectives. A total of 21,750 reviews from the Google Play Store and 7,350 reviews from the Apple App Store were collected using scraping techniques. The results showed that sentiment analysis model on negative sentiment in the Apple App Store excelled with a precision of 93%, recall of 93%, and F1-score of 95%, while in the Google Play Store it had a precision of 82%, recall of 87%, and F1-score of 85%. However, the performance of the positive sentiment model in the Apple App Store was very low, with a precision of 63%, recall of 33%, and F1-score of 43%, compared to the Google Play Store which had a precision of 78%, recall of 71%, and F1-score of 74%. This indicates that a higher level of dissatisfaction is observed in the Apple App Store compared to Android. These results are consistent with the average ratings of the application on both platforms. Topic modeling results, which presented 15 topics from each platform, showed similar common issues such as login, OTP verification, and data input errors on both platforms. However, reviews of the Satu Sehat running on the Apple tend to be more negative compared to the one of Android. Therefore, improving the application quality of the Apple platform is more expected to meet user expectations and enhancing the overall rating as in the Andrond one.
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T. I. I. Bessin, A. W. P. Ouédraogo, and F. Guinko, “Mobile health applications future trends and challenges,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Springer, 2020, pp. 202–211. doi: 10.1007/978-3-030-41593-8_15.
I. Ayu Mirah Cahya Dewi, I. Komang Dharmendra, and N. Wayan Setiasih, “ANALISIS SENTIMEN REVIEW APLIKASI SATU SEHAT MOBILE MENGGUNAKAN MODEL SAMPLING TOMEK LINKS,” Jurnal Teknologi Informasi dan Komputer (JUTIK), vol. 9, no. 3, pp. 497–504, Oct. 2023, doi: 10.36002/jutik.v9i5.2644.
D. I. Inan, K. T. Win, and R. Juita, “MHealth medical record to contribute to noncommunicable diseases in Indonesia,” in Procedia Computer Science, Elsevier B.V., 2019, pp. 1283–1291. doi: 10.1016/j.procs.2019.11.243.
N. F. A. Budi, W. R. Fitriani, A. N. Hidayanto, S. Kurnia, and D. I. Inan, “A study of government 2.0 implementation in Indonesia,” Socioecon Plann Sci, vol. 72, Dec. 2020, doi: 10.1016/j.seps.2020.100920.
V. Vats, P. Garg, V. Singh, and S. Yadav, “SENTIMENT ANALYSIS AND PREDICATION MODEL,” International Journal of Engineering Applied Sciences and Technology, vol. 5, no. 2, pp. 202–209, Jun. 2020, doi: 10.33564/IJEAST.2020.v05i02.030.
D. I. Inan et al., “Technology anxiety and social influence towards intention to use of ride-hailing service in Indonesia,” Case Stud Transp Policy, vol. 10, no. 3, pp. 1591–1601, Sep. 2022, doi: 10.1016/j.cstp.2022.05.017.
V. Sagvekar and P. Sharma, “Study on Product Opinion Analysis for Customer Satisfaction on E-Commerce Websites,” 2021. doi: 10.3233/apc210206.
Y. Sahria and D. Hatta Fudholi, “Analisis Topik Penelitian Kesehatan di Indonesia Menggunakan Metode Topic Modeling LDA (Latent Dirichlet Allocation),” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 1, no. 3, pp. 336–344, Apr. 2020, doi: 10.29207/resti.v4i2.1821.
H. Tannady, D. Haeraini, and D. Natalia, “Perancangan Tampilan User Interface Pada Website Klinik Sehat Berdasarkan Metode Paper Prototype,” JBASE - Journal of Business and Audit Information Systems, vol. 4, no. 2, Oct. 2021, doi: 10.30813/jbase.v4i2.2999.
M. Y. Febrianta, S. Widiyanesti, and S. R. Ramadhan, “Analisis Ulasan Indie Video Game Lokal pada Steam Menggunakan Analisis Sentimen dan Pemodelan Topik Berbasis Latent Dirichlet Allocation,” Journal of Animation & Games Studies, vol. 7, no. 2, pp. 117–144, Oct. 2021, doi: 10.24821/jags.v7i2.5162.
M. Choirul Rahmadan, A. Nizar Hidayanto, D. Swadani Ekasari, B. Purwandari, and Theresiawati, “Sentiment Analysis and Topic Modelling Using the LDA Method related to the Flood Disaster in Jakarta on Twitter,” in Proceedings - 2nd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 126–130. doi: 10.1109/ICIMCIS51567.2020.9354320.
M. Mujahid et al., “Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19,” Applied Sciences , vol. 11, no. 18, pp. 1–25, Sep. 2021, doi: 10.3390/app11188438.
S. A. Ashari, M. W. A. Saputra, E. Larosa, and B. S. Rijal, “Analisis Sentimen pada Aplikasi Translate Google Menggunakan Metode SVM (Studi Kasus: Komentar Pada Playstore),” Jurnal Teknik, vol. 21, no. 2, pp. 168–182, Dec. 2023, doi: 10.37031/jt.v21i2.412.
P. Patmawati and M. Yusuf, “Analisis Topik Modelling Terhadap Penggunaan Sosial Media Twitter oleh Pejabat Negara,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 3, pp. 122–129, Dec. 2021, doi: 10.47065/bits.v3i3.1012.
A. Nurlayli and M. A. Nasichuddin, “Topic Modeling Penelitian Dosen JPTEI UNY pada Google Scholar Menggunakan Latent Dirichlet Allocation,” ELINVO (Electronics, Informatics, and Vocational Education), vol. 4, no. 2, pp. 154–161, Nov. 2019, doi: 10.21831/elinvo.v4i2.
D. Rudini, D. Gita Purnama, and A. Achmad Khan, “PENGGUNAAN TEKNIK WEB SCRAPING DALAM APLIKASI PENGAMBILAN DATA DARI GOOGLE MAPS UNTUK MENUNJANG DIGITAL MARKETING,” Lentera: Multidisciplinary Studies, vol. 2, no. 1, pp. 10–19, Nov. 2023, doi: 10.57096/lentera.v2i1.61.
R. Pancawati, “DEVELOPMENT OF A GUIDEBOOK FOR DESIGNING INTERACTIVE DISTANCE LEARNING (PIJAR) FOR LECTURERS OF TECHNOLOGY AND VOCATIONAL EDUCATIONAL DEPARTEMENT,” BALANGA: Jurnal Pendidikan Teknologi dan Kejuruan, vol. 9, no. 1, pp. 27–34, Jun. 2021, doi: 10.37304/balanga.v9i1.2993.
L. Hermawan and M. B. Ismiati, “Pembelajaran Text Preprocessing berbasis Simulator Untuk Mata Kuliah Information Retrieval,” JURNAL TRANSFORMATIKA, vol. 17, no. 2, pp. 188–199, Jan. 2020, doi: 10.26623/transformatika.v17i2.1705.
I. Rasyada, Y. Setiowati, A. Barakbah, and M. T. Fiddin Al Islami, “Sentiment Analysis of BPJS Kesehatan’s Services Based on Affective Models,” in IES 2020 - International Electronics Symposium: The Role of Autonomous and Intelligent Systems for Human Life and Comfort, Institute of Electrical and Electronics Engineers Inc., Sep. 2020, pp. 549–556. doi: 10.1109/IES50839.2020.9231940.
A. Anggraini, E. M. Kusumaningtyas, A. R. Barakbah, and M. T. Fiddin Al Islami, “Indonesian conjunction rule based sentiment analysis for service complaint regional water utility company surabaya,” in IES 2020 - International Electronics Symposium: The Role of Autonomous and Intelligent Systems for Human Life and Comfort, Institute of Electrical and Electronics Engineers Inc., Sep. 2020, pp. 541–548. doi: 10.1109/IES50839.2020.9231772.
R. Setiyawan and Z. Mustofa, “Comparison of the performance of naive bayes and support vector machine in sirekap sentiment analysis with the lexicon-based approach,” Journal of Soft Computing Exploration (JOSCEX), vol. 5, no. 4, pp. 122–132, Jun. 2024, doi: 10.52465/joscex.v5i1.367.
Y. Religia, G. T. Pranoto, and I. M. Suwancita, “Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications,” JISA (Jurnal Informatika dan Sains), vol. 4, no. 2, pp. 133–137, Dec. 2021, doi: 10.31326/jisa.v4i2.946.
R. T. Aldisa and P. Maulana, “Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, pp. 106–109, Jun. 2022, doi: 10.47065/bits.v4i1.1581.
Y. Sahria, N. Isnaini Febriarini, and P. Dwi Oktavianti, “PEMODELEN TOPIK PENELITIAN BIDANG KEPERAWATAN INDONESIA PADA REPOSITORY JURNAL SINTA MENGGUNAKAN METODE TOPIC MODELLING LDA (LATENT DIRICHLET ALLOCATION),” SEMASEMASTER: Seminar Nasional Teknologi Informasi & Ilmu Komputer, vol. 1, no. 1, pp. 90–102, Dec. 2020, doi: 10.31849/semaster.v1i1.6032.
G. Brookes and T. McEnery, “The utility of topic modelling for discourse studies: A critical evaluation,” Discourse Stud, vol. 21, no. 1, pp. 3–21, Feb. 2019, doi: 10.1177/1461445618814032.
A. Jiana Putri, A. Salsabila Syafira, M. Eka Purbaya, and D. Purnomo, “Analisis Sentimen E-Commerce Lazada pada Jejaring Sosial Twitter Menggunakan Algoritma Support Vector Machine,” Jurnal Teknik Industri, Bisnis Digital dan Teknik Logistik (TRINISTIK), vol. 01, no. 3, pp. 16–21, Mar. 2022, doi: 10.20895/trinistik.v1i1.447.
A. F. Hidayatullah, S. K. Aditya, Karimah, and S. T. Gardini, “Topic modeling of weather and climate condition on twitter using latent dirichlet allocation (LDA),” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Mar. 2019. doi: 10.1088/1757-899X/482/1/012033.
A. R. Riadhi, M. K. Aidid, and A. S. Ahmar, “Analisis Penyebaran Hunian dengan Menggunakan Metode Nearest Neighbor Analysis,” VARIANSI: Journal of Statistics and Its application on Teaching and Research, vol. 2, no. 1, p. 46, Mar. 2020, doi: 10.35580/variansiunm12901.
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