Klasifikasi Sentimen Ulasan Pengguna Aplikasi Qpon dengan Support Vector Machine dan Logistic Regression

Authors

  • Iin Febyanti Universitas Pembangunan Veteran Jawa Timur
  • Arsinta Safira Devi Universitas Pembangunan Veteran Jawa Timur
  • Salsabila Wardah Universitas Pembangunan Veteran Jawa Timur
  • Shindy Shella May Wara Universitas Pembangunan Veteran Jawa Timur
  • Aviolla Terza Damaliana Universitas Pembangunan Veteran Jawa Timur

DOI:

https://doi.org/10.54259/jdmis.v4i1.4663

Keywords:

Analisis Sentimen, Logistic Regression, Support Vector Machine , SVM, Sentiment Analysis

Abstract

The increasing number of user reviews in mobile applications is an important source of information in understanding user satisfaction and experience with the services used. One of the applications used in this study is the Qpon application. Reviews left by users often contain positive or negative opinions that can influence other users in making decisions. Therefore, sentiment analysis is needed to determine the tendency of opinions in these reviews. This study aims to classify Qpon application user reviews into two sentiment categories, namely positive and negative. Data were collected through the web scraping method and obtained 866 review data. After going through text preprocessing stages such as removing unimportant words, normalization, and tokenization, the data were analyzed using the TF-IDF method as a feature representation, then classified using the Logistic Regression and Support Vector Machine (SVM) algorithms. The testing process was carried out using the Stratified K-Fold Cross Validation technique and measured based on five evaluation metrics, namely accuracy, precision, recall, F1-score, and ROC AUC. The results showed that SVM had the highest accuracy and precision values, while Logistic Regression was superior in recall and ROC AUC. These findings indicate that SVM is superior in terms of classification accuracy, while Logistic Regression is more sensitive to positive reviews. This study is expected to be used as a reference for the development of a sentiment analysis system to improve application services based on user review data.

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References

Raihanda Luthfiansyah, Budi Wasito, "ANALISIS SENTIMEN TERHADAP PARA KANDIDAT PRESIDEN 2024 BERDASARKAN NETIZEN PENGGUNA TWITTER DENGAN METODE DATA MINING DAN TEXT MINING," Jurnal Informatika dan Bisnis, vol. 11, no. 2, p. 3, 2022.

A. Novantika and Sugiman, "Analisis Sentimen Ulasan Pengguna Aplikasi Video Conference Google Meet menggunakan Metode SVM dan Logistic Regression," PRISMA, Prosiding Seminar Nasional Matematika, vol. 5, pp. 808-813, 2022.

M. Djufri, "PENERAPAN TEKNIK WEB SCRAPING UNTUK PENGGALIAN POTENSI PAJAK (Studi Kasus pada Online Market Place Tokopedia, Shopee dan Bukalapak)," Jurnal BPPK : BADAN PENDIDIKAN DAN PELATIHAN KEUANGAN KEMENTRIAN KEUANGAN REPUBLIK INDONESIA, vol. 13, no. 2, pp. 65-75, 2020.

E. R. Lidinillah, T. Rohana and A. R. Juwita, "Analisis sentimen twitter terhadap steam menggunakan algoritma logistic regression dan support vector machine," TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika, vol. 10, no. 2, pp. 154-164, 2023.

I. Syahrohim, S. D. Saputra, R. W. Saputra, V. H. Pranatawijaya and R. Priskila, "PERBANDINGAN ANALISIS SENTIMEN SETELAH PILPRES 2024 DI TWITTER MENGGUNAKAN ALGORITMA MACHINE LEARNING," Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, 2024.

M. R. Adrian, M. P. Putra, M. H. Rafialdy and N. A. Rakhmawati, "Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB," Jurnal Informatika Upgris, vol. 7, no. 1, 2021.

R. R. Salam, M. F. Jamil, Y. Ibrahim, Rahmaddeni, Soni and Herianto, "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, no. 1, pp. 27-35, 2023.

P. Agusia, M. U. A. Manurung, V. Calista and V. C. Mawardi, "Pemanfaatan Word Cloud Pada Analisis Sentimen Dalam Menggali Persepsi Publik," SEMINAR NASIONAL CORISINDO, pp. 25-30, 2024.

J. J. A. Limbong, I. Sembiring and K. D. Hartomo, "ANALISIS KLASIFIKASI SENTIMEN ULASAN PADA E-COMMERCE SHOPEE BERBASIS WORD CLOUD DENGAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR," Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) , vol. 9, no. 2, pp. 327-356, 2022.

A. I. Nurrizqi, Erfiani, Indahwati, A. Fitrianto and R. Amelia, "Pemodelan Regresi Logistik Berbasis Backward Elimination Untuk Mengetahui Faktor yang Memengaruhi Tingkat Kemiskinan di Indonesia Tahun 2021," Jurnal Statistika dan Aplikasinya, vol. 6, no. 2, pp. 160-170, 2022.

S. D. Anugrawati, Nurhikma, I. W. Saputri and K. Nurfadilah, "Analisis Regresi Logistik Biner dalam Penentuan Faktor-Faktor yang Mempengaruhi Ketepatan Waktu Lulus Mahasiswa UIN Alauddin Makassar," Journal of Mathematics: Theory and Applications, vol. 5, no. 1, pp. 11-16, 2023.

I. M. Parapat, M. T. Furqon and Sutrisno, "Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 10, pp. 3163-3169, 2018.

I. S. K. Idris, Y. A. Mustofa and I. A. Salihi, "Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Menggunakan Algoritma Support Vector Machine (SVM)," Jambura Journal of Electrical and Electronics Engineering, vol. 5, no. 1, pp. 32-35, 2023.

S. S. M. Wara, A. F. Adziima, M. Nasrudin, and A. R. Pratama, “Predictive Analysis of Government Application Comment on Playstore with Clustered Support Vector Machine,” Proceeding - IEEE 10th Inf. Technol. Int. Semin. ITIS 2024, pp. 84–88, 2024, doi: 10.1109/ITIS64716.2024.10845453.

M. Fadli and R. A. Saputra, "KLASIFIKASI DAN EVALUASI PERFORMA MODEL RANDOM FOREST UNTUK PREDIKSI STROKE," JT: Jurnal Teknik, vol. 12, no. 2, pp. 72-80, 2023.

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Published

2026-02-28

How to Cite

Febyanti, I., Devi, A. S., Wardah, S., Wara, S. S. M., & Damaliana, A. T. (2026). Klasifikasi Sentimen Ulasan Pengguna Aplikasi Qpon dengan Support Vector Machine dan Logistic Regression. JDMIS: Journal of Data Mining and Information Systems, 4(1), 10–18. https://doi.org/10.54259/jdmis.v4i1.4663

Issue

Vol. 4 No. 1 (2026): February 2026

Section

Articles

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Copyright (c) 2026 Iin Febyanti, Arsinta Safira Devi, Salsabila Wardah, Shindy Shella May Wara, Aviolla Terza Damaliana

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