(2) * RZ Abdul Aziz (Institut Informatika dan Bisnis Darmajaya, Indonesia)
(3) Muhammad Said Hasibuan (Institut Informatika dan Bisnis Darmajaya)
*corresponding author
AbstractUser reviews significantly impact how mobile apps are perceived and provide developers with valuable insights into improving the functionality and quality of their products. Sentiment analysis of these evaluations helps identify the main issues faced by consumers, such as technical difficulties, costs, and service levels. The main objective of this study is to classify user sentiment into positive and negative categories, focusing on the MyTelkomsel app. With the use of Google Play Scraper, 39,493 reviews on various app versions and user experiences were collected. This data was analyzed using multiple machine learning models, including Support Vector Machines (SVM), Naive Bayes, Random Forest, and Gradient Boosting, alongside the Natural Language Processing (NLP) approach. The results show that 39.2% of the reviews are positive, while 60.8% reflect negative sentiment. Among the models, SVM showed the highest accuracy in sentiment classification with a value of (0.854792), while Naive Bayes (0.775541), Random Forest (0.829725), and Gradient Boosting (0.819344) also performed well in sentiment classification. These findings suggest that developers can leverage the insights gained from this analysis to proactively improve the performance and user experience of the MyTelkomsel app, by addressing technical and service-related issues identified in user reviews. KeywordsSentiment Analysis, Mobile Applications, MyTelkomsel, Natural Language Processing (NLP).
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DOIhttps://doi.org/10.29099/ijair.v8i2.1229 |
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References
R. Z. A. Aziz, “The opportunities for MSMEs in the industrial technology,” in Proceeding International Conference on Information Technology and Business, 2019, hal. 272–286.
F. Fajrin dan N. Andini, “Analisis Kepuasan Pelanggan Ojek Online Di Kawasan Kota Bandung,” J. Ilm. Manaj. Dan Bisnis, vol. 2, no. 1, hal. 1–13, 2023.
S. Kemp dan others, “Digital 2021: Indonesia.” 2021.
M. M. Rahman, S. S. M. M. Rahman, S. M. Allayear, M. F. K. Patwary, dan M. T. A. Munna, “A sentiment analysis based approach for understanding the user satisfaction on android application,” in Data Engineering and Communication Technology: Proceedings of 3rd ICDECT-2K19, 2020, hal. 397–407.
P. H. Prastyo, I. Ardiyanto, dan R. Hidayat, “Indonesian Sentiment Analysis: An Experimental Study of Four Kernel Functions on SVM Algorithm with TF-IDF,” in 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020, hal. 1–6.
F. Susanti, “Sentiment Analysis Provider by. U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method,” 2020.
P. Riswanto, R. Z. A. Aziz, dan others, “PENERAPAN DECISION TREE C4. 5 SEBAGAI SELEKSI FITUR DAN SUPPORT VECTOR MACHINE (SVM) UNTUK DIAGNOSA KANKER PAYUDARA,” J. Inform., vol. 19, no. 1, hal. 54–61, 2019.
M. S. Hasibuan dan R. Z. A. Aziz, “Detection of learning styles with prior knowledge data using the SVM, K-NN and Na{"i}ve Bayes algorithms,” J. Infotel, vol. 14, no. 3, hal. 209–213, 2022.
C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discov., vol. 2, no. 2, hal. 121–167, 1998.
O. Chapelle, V. Vapnik, O. Bousquet, dan S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn., vol. 46, hal. 131–159, 2002.
J. A. F. Pierna, V. Baeten, A. M. Renier, R. P. Cogdill, dan P. Dardenne, “Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds,” J. Chemom. A J. Chemom. Soc., vol. 18, no. 7–8, hal. 341–349, 2004.
A. Setyawan, F. Y. Arini, dan I. Akhlis, “Comparative analysis of Simple Additive Weighting method and weighted product method to new employee recruitment Decision Support System (DSS) at PT. Warta Media Nusantara,” Sci. J. Informatics, vol. 4, no. 1, hal. 34–42, 2017.
R. M. Balabin dan E. I. Lomakina, “Support vector machine regression (SVR/LS-SVM)an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data,” Analyst, vol. 136, no. 8, hal. 1703–1712, 2011.
N. P. Husain, S. Sukirman, dan S. SAJIAH, “Analisis Sentimen Ulasan Pengguna Tiktok pada Google Play Store Berbasis TF-IDF dan Support Vector Machine,” J. Syst. Comput. Eng., vol. 5, no. 1, hal. 91–102, 2024.
H. Harnelia, “ANALISIS SENTIMEN REVIEW SKINCARE SKINTIFIC DENGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM),” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 2, 2024.
V. Fitriyana, L. Hakim, D. C. R. Novitasari, dan A. H. Asyhar, “Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine,” J. Buana Inform., vol. 14, no. 01, hal. 40–49, 2023.
M. S. Hasibuan, R. Z. abdul Aziz, D. Naista, dan N. A. Syafira, “Implementation Of A Classification Algorithm To Detect Felder-Silverman Learning Style.”
H. C. Husada dan A. S. Paramita, “Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Teknika, vol. 10, no. 1, hal. 18–26, 2021.
A. T. J. Harjanta, “Preprocessing text untuk meminimalisir kata yang tidak berarti dalam proses text mining,” J. Inform. Upgris, vol. 1, no. 1 Juni, 2015.
H. P. P. R. Zuriel dan A. Fahrurozi, “Implementasi Algoritma Klasifikasi Support Vector Machine Untuk Analisa Sentimen Pengguna Twitter Terhadap Kebijakan Psbb,” J. Ilm. Inform. Komput., vol. 26, no. 2, hal. 149–162, 2021.
M. F. Al-shufi dan A. Erfina, “Sentimen Analisis Mengenai Aplikasi Streaming Film Menggunakan Algoritma Support Vector Machine Di Play Store,” in Prosiding Seminar Nasional Sistem Informasi dan Manajemen Informatika Universitas Nusa Putra, 2021, vol. 1, hal. 156–162.
A. I. Kadhim, “Term weighting for feature extraction on Twitter: A comparison between BM25 and TF-IDF,” in 2019 international conference on advanced science and engineering (ICOASE), 2019, hal. 124–128.
I. Widaningrum, D. Mustikasari, R. Arifin, S. L. Tsaqila, dan D. Fatmawati, “Algoritma Term Frequency--Inverse Document Frequency (TF-IDF) dan K-Means Clustering Untuk Menentukan Kategori Dokumen,” Pros. SISFOTEK, vol. 6, no. 1, hal. 145–149, 2022.
E. Widjaja, W. Zheng, dan Z. Huang, “Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines,” Int. J. Oncol., vol. 32, no. 3, hal. 653–662, 2008.
V. Vapnik, The nature of statistical learning theory. Springer science & business media, 2013.
M. Tahir, A. Khan, dan A. Majid, “Protein subcellular localization of fluorescence imagery using spatial and transform domain features,” Bioinformatics, vol. 28, no. 1, hal. 91–97, 2012.
D. Kim, D. Seo, S. Cho, dan P. Kang, “Multi-co-training for document classification using various document representations: TF--IDF, LDA, and Doc2Vec,” Inf. Sci. (Ny)., vol. 477, hal. 15–29, 2019.
L. Ardiani, H. Sujaini, dan T. Tursina, “Implementasi sentiment analysis tanggapan masyarakat terhadap pembangunan di Kota Pontianak,” JUSTIN (Jurnal Sist. dan Teknol. Informasi), vol. 8, no. 2, hal. 183–190, 2020.
M. A. Syahira dan R. Kurniawan, “Analisis Sentimen Cyberbullying Pada Media Sosial X Menggunakan Metode Support Vector Machine,” J. MEDIA Inform. BUDIDARMA, vol. 8, no. 3, hal. 1724–1733, 2024.
S. Chen, G. I. Webb, L. Liu, and X. Ma, "A novel selective naïve Bayes algorithm," Knowledge-Based Systems, vol. 192, p. 105361, 2020.
B. Dai, R. C. Chen, S. Z. Zhu, and W. W. Zhang, "Using random forest algorithm for breast cancer diagnosis," in 2018 International Symposium on Computer, Consumer and Control (IS3C), 2018, pp. 449-452.
S. J. Rigatti, "Random forest," Journal of Insurance Medicine, vol. 47, no. 1, pp. 31-39, 2017.
M. S. Hosen and R. Amin, "Significant of gradient boosting algorithm in data management system," Eng. Int., vol. 9, no. 2, pp. 85-100, 2021.
P. Bahad and P. Saxena, "Study of adaboost and gradient boosting algorithms for predictive analytics," in International Conference on Intelligent Computing and Smart Communication 2019: Proceedings of ICSC 2019, Springer, Singapore, 2020, pp. 235-244.
A. Natekin and A. Knoll, "Gradient boosting machines, a tutorial," Frontiers in Neurorobotics, vol. 7, p. 21, 2013.
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