Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm
-
Putri Kurniawati
*
Telkom University, Bandung,
Indonesia
- Riska Yanu Fa'rifah Telkom University, Bandung, Indonesia
- Deden Witarsyah Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
The continuous emergence of online transportation service platforms is one of the effects of the ever-increasing technological advancements. One such online transportation service application, Maxim, has recently been slowly gaining ground in the ride-hailing market in Indonesia. According to data collected by one media outlet in 2022, Maxim ranks third as the most preferred online transportation platform by the public, following Gojek and Grab. This suggests that there are factors causing users to lack interest in or hesitate to use the Maxim application. On the Google Play Store, user ratings (in numerical values) and written reviews serve as reasons for the potential users lack of interest. Analyzing ratings alone is less accurate and does not provide in-depth information and meaning regarding users experiences. To understand user opinions about Maxim's service and functionality, an analysis of user reviews is crucial. Therefore, this research conducts sentiment analysis on Maxim user reviews using the Support Vector Machine (SVM) algorithm to classify reviews quickly. The reviews are categorized into two classes: positive and negative sentiment. The classification process is carried out in three scenarios with different data training and testing ratios: 60:40, 70:30, and 80:20, using a Linear kernel and hyperparameter optimization with GridSearch. The best accuracy is achieved with a 70:30 ratio, which is 89.82%. Evaluation using the confusion matrix also yields a precision of 92.66%, recall of 94.09%, and an F1 score of 93.38%. The ROC-AUC curve evaluation results in an AUC value of 0.8505. The sentiment analysis results tend to lean towards positive sentiment, indicating a high level of user satisfaction with the Maxim application. Based on these sentiment results, developers can identify what aspects of the Maxim application need to be maintained and improved.
Downloads
References
M. Arif and K. U. Apjii, “Profil Internet Indonesia 2022,” no. June, 2022.
R. K. Hastuti, “Duh, 2 Tahun Terakhir Ada 10 Ojol yang Tergilas Grab & Gojek,” CNBC Indonesia, 2019. https://www.cnbcindonesia.com/tech/20190813122355-37-91630/duh-2-tahun-terakhir-ada-10-ojol-yang-tergilas-grab-gojek
Shopback, “Sering Membandingkan Harga Transportasi Online? Aplikasi Ini Akan Memudahkan Penggunanya,” Shopback.co.id, 2018. https://www.shopback.co.id/katashopback/transportasi-online-makin-digemari
Taximaxim.com, “Tentang Perusahaan.” https://id.taximaxim.com/id/2093-jakarta/about/ (tanggal akses 10 Juli 2023)
A. Mutia, “Survei: Publik Jabodetabek Paling Sering Pakai Gojek, Bagaimana Grab, Maxim, dan InDriver?,” Databoks.katadata.co.id, 2022. https://databoks.katadata.co.id/datapublish/2022/10/11/survei-publik-jabodetabek-paling-sering-pakai-gojek-bagaimana-grab-maxim-dan-indriver
P. A. Permatasari, L. Linawati, and L. Jasa, “Survei Tentang Analisis Sentimen Pada Media Sosial,” Maj. Ilm. Teknol. Elektro, vol. 20, no. 2, p. 177, 2021, doi: 10.24843/mite.2021.v20i02.p01.
S. W. Iriananda et al., “ANALISIS SENTIMEN DAN ANALISIS DATA EKSPLORATIF ULASAN,” no. Ciastech, pp. 473–482, 2021.
A. Saepulrohman, S. Saepudin, and D. Gustian, “Analisis Sentimen Kepuasan Pengguna Aplikasi Whatsapp Menggunakan Algoritma Naïve Bayes Dan Support Vector Machine,” is Best Account. Inf. Syst. Inf. Technol. Bus. Enterp. this is link OJS usf@, vol. 6, no. 2, pp. 91–105, 2021, doi: 10.34010/aisthebest.v6i2.4919.
K. A. Rokhman, B. Berlilana, and P. Arsi, “Perbandingan Metode Support Vector Machine Dan Decision Tree Untuk Analisis Sentimen Review Komentar Pada Aplikasi Transportasi Online,” J. Inf. Syst. Manag., vol. 3, no. 1, pp. 1–7, 2021, doi: 10.24076/joism.2021v3i1.341.
A. M. Rahat, A. Kahir, and A. K. M. Masum, “Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset,” Proc. 2019 8th Int. Conf. Syst. Model. Adv. Res. Trends, SMART 2019, pp. 266–270, 2020, doi: 10.1109/SMART46866.2019.9117512.
N. Herlinawati, Y. Yuliani, S. Faizah, W. Gata, and S. Samudi, “Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, p. 293, 2020, doi: 10.24114/cess.v5i2.18186.
S. A. Salloum, M. Al-emran, and A. A. Monem, “Using Text Mining Techniques for Extracting Information from Research Using Text Mining Techniques for Extracting Information from Research Articles,” no. January, 2018, doi: 10.1007/978-3-319-67056-0.
S. Bhatia, M. Sharma, and K. K. Bhatia, “Sentiment Analysis and Mining of Opinions,” Stud. Big Data, vol. 30, no. May, pp. 503–523, 2018, doi: 10.1007/978-3-319-60435-0_20.
J. Han, J. Pei, and H. Tong, Data Mining : Concepts and Techniques. Morgan Kaufmann, 2022.
K. X. Han, W. Chien, C. C. Chiu, and Y. T. Cheng, “Application of support vector machine (SVM) in the sentiment analysis of twitter dataset,” Appl. Sci., vol. 10, no. 3, 2020, doi: 10.3390/app10031125.
P. S. Saragih, D. Witarsyah, F. Hamami, and J. M. MacHado, “Sentiment Analysis of Social Media Twitter with Case of Large Scale Social Restriction in Jakarta using Support Vector Machine Algorithm,” 2021 Int. Conf. Adv. Data Sci. E-Learning Inf. Syst. ICADEIS 2021, vol. 19, no. January 2020, pp. 1–6, 2021, doi: 10.1109/ICADEIS52521.2021.9701961.
S. S. Chaeikar, A. A. Manaf, A. A. Alarood, and M. Zamani, “PFW: Polygonal fuzzy weighted—an SVM kernel for the classification of overlapping data groups,” Electron., vol. 9, no. 4, 2020, doi: 10.3390/electronics9040615.
A. Zaiem and N. Charibaldi, “Komparasi Fungsi Kernel Metode Support Vector Machine untuk Analisis Sentimen Instagram dan Twitter ( Studi Kasus : Komisi Pemberantasan Korupsi ),” vol. 9, no. 2, pp. 33–42, 2021.
A. Pranata, E. Budianita, Yusra, and E. P. Cynthia, “Klasifikasi Sentimen Terhadap Maxim Menggunakan Algoritma SVM Pada Media Sosial TwittAnggi Pranata, N. (2022). Klasifikasi Sentimen Terhadap Maxim Menggunakan Algoritma SVM Pada Media Sosial Twitter. Klasifikasi Sentimen Terhadap Maxim Menggunakan Algorit,” Klasifikasi Sentimen Terhadap Maxim Menggunakan Algoritm. SVM Pada Media Sos. Twitter, vol. 5, no. 3, pp. 332–341, 2022.
W. Musu, A. Ibrahim, and Heriadi, “Pengaruh Komposisi Data Training dan Testing terhadap Akurasi Algoritma C4 . 5,” Pros. Semin. Ilm. Sist. Inf. Dan Teknol. Inf., vol. X, no. 1, pp. 186–195, 2021.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm
Pages: 466−475
Copyright (c) 2023 Putri Kurniawati, Riska Yanu Fa'rifah, Deden Witarsyah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- 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.
- 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.
- 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).
