Sentiment Analysis of the Matahari Application to Provide User Experience Insights using Support Vector Machine
- Moch Arif Samsul Rizal Universitas Dr. Soetomo, Surabaya, Indonesia
-
Anik Vega Vitianingsih
*
Universitas Dr. Soetomo, Surabaya,
Indonesia
- Hewa Majeed Zangana Duhok Polytechnic University, Duhok, Iraq
- Anastasia Lidya Maukar President University, Bekasi, Indonesia
- Fitri Marisa Universitas Widyagama Malang, Malang, Indonesia (*) Corresponding Author
Abstract
The expansion of Indonesia's digital commerce ecosystem has pushed retail companies to strengthen the quality of their online services to remain competitive. Matahari, one of the country's leading retail brands, launched its mobile app as a platform for shopping, promotions, and customer interaction. However, user feedback on the Google Play Store indicates persistent problems with system responsiveness, ease of use, and the consistency of promotional information. This study examines sentiment patterns in 2,500 user reviews and classifies them using a Support Vector Machine (SVM) based model that incorporates three kernel types: Linear, RBF, and Polynomial. Before modelling, the text corpus underwent several pre-processing steps—such as tokenization, stopword filtering, and stemming represented numerically using TF-IDF weighting. Among all tested configurations, the Linear kernel produced the strongest results, achieving an accuracy rate of 88%. Despite a moderate distribution across categories (1030 negative, 886 neutral, and 584 positive), the model achieved consistent performance across all classes. Evaluation using Precision, Recall, and F1-Score confirmed the validity of the 88% accuracy without the need for additional sampling techniques. From a scholarly standpoint, this research adds insight into sentiment analysis for retail applications within the Indonesian context by applying a machine-learning approach. In practice, the outcomes highlight areas for improvement, particularly technical stability, the intuitiveness of user flows, and promotional clarity to support a better overall user experience.
Downloads
References
Rini Tresnasari, Era Pemasaran 5.0 : Paradigma Baru Strategi Bisnis. Padang: Takaza Innovatix Labs, 2024.
Badan Pusat Statistik, STATISTIK E-COMMERCE 2023. Jakarta: Badan Pusat Statistik, 2025.
M. Z. Siregar, A. M. Elhanafi, and D. Irwan, “Analisis Sentimen Terhadap Ulasan Aplikasi Media Sosial Di Google Play Menggunakan Algoritma Naive Bayes,” Jurnal Mahasiswa Teknik Informatika, vol. 9, no. 2, Apr. 2025, doi: 10.36040/jati.v9i2.12841.
R. A. E. V. T. Sapanji, D. Hamdani, and P. Harahap, “Sentiment Analysis of the Top 5 E-commerce Platforms in Indonesia using Text Mining and Natural Language Processing (NLP),” Journal of Applied Informatics and Computing (JAIC), vol. 7, no. 2, p. 202, 2023, doi: 10.30871/jaic.v7i2.6517.
A. Nurian, M. S. Ma’arif, I. N. Amalia, and C. Rozikin, “Analisis Sentimen Pengguna Aplikasi Shopee Pada Situs Google Play Menggunakan Naive Bayes Classifier,” Jurnal Informatika Dan Teknik Elektro Terapan, vol. 12, no. 1, 2024, doi: 10.23960/jitet.v12i1.3631.
P. A. A. Anam, D. A. Fatah, and M. A. Syakur, “Analisis Sentimen Review Aplikasi Zalora Di Google Playstore Menggunakan Metode Support Vector Machine,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 2, Mar. 2025, doi: 10.36040/jati.v9i2.13083.
D. Fadila and M. Ikhsan, “Analisis Sentimen Pada Aplikasi Tokopedia Menggunakan Metode Support Vector Machine,” Progresif: Jurnal Ilmiah Komputer, vol. 21, no. 1, pp. 238–247, Feb. 2025, doi: 10.35889/progresif.v21i1.2593.
M. Bagus Prayogi and G. Masitoh, “Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest,” Jurnal Informatika Sunan Kalijaga), vol. 10, no. 2, pp. 158–170, 2025, doi: 10.14421/jiska.2025.10.2.158-170.
A. Tholib and Z. Arifin, “Analisis Sentimen Terhadap Ulasan Aplikasi Shopee di Google Play Store Menggunakan Metode TF-IDF dan Long Short-Term Memory (LSTM),” Journal homepage: Journal of Electrical Engineering and Computer (JEECOM), vol. 6, no. 2, 2024, doi: 10.33650/jeecom.v4i2.
Wororomi, Data Mining (Memahami Pola Di Balik Angka). Purbalingga: EUREKA MEDIA AKSARA, 2024.
D. Rifaldi, Abdul Fadlil, and Herman, “Teknik Preprocessing Pada Text Mining Menggunakan Data Tweet ‘Mental Health,’” Decode: Jurnal Pendidikan Teknologi Informasi, vol. 3, no. 2, pp. 161–171, Apr. 2023, doi: 10.51454/decode.v3i2.131.
R. R. Salam, M. F. Jamil, and Y. Ibrahim, “Analisis Sentimen Terhadap Bantuan Langsung Tunai (BLT) Bahan Bakar Minyak (BBM) Menggunakan Support Vector Machine,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, pp. 27–35, Apr. 2023, doi: 10.57152/malcom.v3i1.590.
C. Sager, C. Janiesch, and P. Zschech, “A survey of image labelling for computer vision applications,” Journal of Business Analytics, vol. 4, no. 2, pp. 91–110, 2021, doi: 10.1080/2573234X.2021.1908861.
R. Darman, “Analisis Sentimen Respons Twitter terhadap Persyaratan Badan Penyelenggara Jaminan Sosial (BPJS) di Kantor Pertanahan,” Widya Bhumi, vol. 3, no. 2, p. 113, Oct. 2023, doi: 10.31292/wb.v3i2.61.
D. F. Surianto and D. F. Surianto, “Enhancing K-Means Clustering for Journal Articles using TF-IDF and LDA Feature Extraction,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 2, pp. 964–972, Mar. 2025, doi: 10.47709/brilliance.v4i2.5547.
V. B. Lestari and C. A. Hutagalung, “Evaluation of TF-IDF Extraction Techniques in Sentiment Analysis of Indonesian-Language Marketplaces Using SVM, Logistic Regression, and Naive Bayes,” Journal of Computer Science and Applications, vol. 8, no. 1, pp. 22–2025, 2025, doi: 10.21009/jkoma.v8i1.05.
I. S. Aisah, B. Irawan, and T. Suprapti, “Algoritma Support Vector Machine (Svm) Untuk Analisis Sentimen Ulasan Aplikasi Al Qur’an Digital,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 6, 2023, doi: 10.36040/jati.v7i6.8263.
J. A. P. Ginting, R. M. Sari, M. R. D. Siregar, and D. Kiswanto, “Analisis Support Vector Machine (SVM) Untuk Klasifikasi Jenis Kelamin Pada Ikan Cupang Dengan Bantuan Local Binary Pattern (LBP),” JATI(Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 6, 2024, doi: 10.36040/jati.v8i6.12028.
R. Nurhidayat and K. E. Dewi, “Penerapan Algoritma K-Nearest Neighbor Dan Fitur Ekstraksi N-Gram Dalam Analisis Sentimen Berbasis Aspek,” Komputa : Jurnal Ilmiah Komputer dan Informatika, vol. 12, no. 1, May 2023, doi: 10.34010/komputa.v12i1.9458.
S. Riyadi, F. D. Daffa, C. D. Damarjati, and M. S. A. M. A. Ali, “Sentiment Analysis on Social Media Uusing CNN-RNN Hybrid: A Case Study of Indonesian Presidential Candidate,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, May 2025, doi: 10.22219/kinetik.v10i2.2125.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Sentiment Analysis of the Matahari Application to Provide User Experience Insights using Support Vector Machine
Pages: 2109-2117
Copyright (c) 2025 Moch Arif Samsul Rizal, Anik Vega Vitianingsih, Hewa Majeed Zangana, Anastasia Lidya Maukar, Fitri Marisa
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).
