Analisis Sentimen Review Aplikasi Chat GPT dengan Memanfaatkan Algoritma Support Vector Machine

Authors

  • Alit Damar Prabadaru Program Studi Informatika, Fakultas Teknik & Informatika, Bina Sarana Informatika, Depok, Indonesia
  • Ashifa Zahrawati Program Studi Informatika, Fakultas Teknik & Informatika, Bina Sarana Informatika, Depok, Indonesia
  • Muhammad Agmal Jibran Program Studi Informatika, Fakultas Teknik & Informatika, Bina Sarana Informatika, Depok, Indonesia
  • Salsabila Nurulita Program Studi Informatika, Fakultas Teknik & Informatika, Bina Sarana Informatika, Depok, Indonesia
  • Nadya Cantika Apriani Dewana Program Studi Informatika, Fakultas Teknik & Informatika, Bina Sarana Informatika, Depok, Indonesia

DOI:

https://doi.org/10.37253/telcomatics.v10i2.11759

Keywords:

Support Vector Machine, TF-IDF, Chat GPT, User Review, Analasis Sentimen

Abstract

This study analyses user sentiment toward the ChatGPT application based on reviews collected from the Google Play Store. The goal of this research is to classify user opinions into positive and negative categories using the Support Vector Machine (SVM) algorithm. The dataset was obtained through web scraping and processed using several text preprocessing steps, including case folding, tokenization, stopword removal, and stemming. The TF-IDF method was applied to convert the text into numerical feature vectors suitable for machine learning models.

A linear SVM model was used to perform sentiment classification due to its effectiveness in handling high-dimensional text data. The results of the evaluation show that the linear SVM provides stable and accurate performance when identifying sentiment in user reviews. The findings also indicate that TF-IDF features contribute significantly to improving model accuracy.

Overall, this research concludes that SVM is a suitable and reliable method for sentiment analysis of application reviews. The outcomes can help developers understand user perceptions and improve the quality of the ChatGPT application based on the insights obtained

Downloads

Download data is not yet available.

References

Haryanto Agus Tri, “Pengguna Internet Dunia Capai 5,35 Miliar, Setengah Penduduk Bumi Terkoneksi Dunia Maya,” Feb. 2024.

A. Septini, “BULLETIN OF COMPUTER SCIENCE RESEARCH Analisis Sentimen Masyarakat di Twitter Mengenai Open AI CHATGPT Menggunakan Metode Support Vector Machine (SVM),” Media Online), vol. 5, no. 2, pp. 138–149, 2025, doi: 10.47065/bulletincsr.v5i2.475.

S. Ernawati and R. Wati, “Evaluasi Performa Kernel SVM dalam Analisis Sentimen Review Aplikasi ChatGPT Menggunakan Hyperparameter dan VADER Lexicon,” 2024.

D. Prismala, I. Kadek, and D. Nuryana, “Analisis Opinion Mining Pada Topik Chatgpt di Aplikasi X Dengan Pendekatan Algoritma SVM Berbasis Lexicon,” 2024.

J. Khatib Sulaiman, D. Atmajaya, A. Febrianti, H. Darwis, I. Artikel Abstrak, and K. Kunci, “Metode SVM dan Naive Bayes untuk Analisis Sentimen ChatGPT di Twitter,” Indonesian Journal of Computer Science Attribution, vol. 12, no. 4, p. 2173.

M. Muadin and H. Asnal, “IMPLEMENTASI METODE SUPPORT VECTOR MACHINE PADA OPINION MINING MASYARAKAT TERKAIT CHATGPT,” JOISIE Journal Of Information System And Informatics Engineering, vol. 7, no. 1, pp. 78–84, 2023.

N. Perdana and H. Santoso, “Sentimen Analisis Media Sosial Terhadap Isu Pagar Laut Dengan Algoritma Support Vector Machine dan Logistic Regression,” Jurnal Pendidikan dan Teknologi Indonesia, vol. 5, no. 7, pp. 1915–1924, Jul. 2025, doi: 10.52436/1.jpti.888.

M. Rafly Gusmansyah and H. Hendrawan, “Peningkatan Kinerja Analisis Sentimen pada Ulasan Aplikasi Identitas Kependudukan Digital (IKD) di Indonesia Menggunakan Algoritma Support Vector Machine (SVM) dan SMOTE under a Creative Commons Attribution-NonCommercial ShareAlike 4.0 International (CC BY-NC-SA 4.0),” vol. 10, no. 1, pp. 2541–1179, 2025, [Online]. Available: https://journal.uin-alauddin.ac.id/index.php/instek

Marhadi Nanda, “ Penerapan SVM dan Word2Vec untuk Analisis Sentimen Ulasan Pengguna Aplikasi DANA,” Jul. 2024.

S. Nadhifah, A. Fatkhudin, and F. A. Artanto, “Penerapan SVM Sebagai Algoritma Machine Learning Dalam Analisis Sentimen Terhadap Telemedicine di Indonesia,” Jurnal Surya Informatika, vol. 15, no. 2, pp. 125–130, Nov. 2025, doi: 10.48144/suryainformatika.v15i2.2201.

Permana Tito Dian, “PERBANDINGAN PERFORMA ALGORITMA NAIVE BAYES DAN SVM UNTUK ANALISIS SENTIMEN KOMENTAR YOUTUBETERHADAP INDUSTRI ESPORTS DI INDONESIA ,” Oct. 2025.

A. Suharman and M. Kamayani Sulaeman, “Analisis Sentimen Pengguna Aplikasi Livin’ by Mandiri Menggunakan Metode Support Vector Machine (SVM) dengan Ekstraksi Fitur TF-IDF dan Word2Vec,” Jurnal Pendidikan dan Teknologi Indonesia, vol. 5, no. 8, pp. 2201–2212, Aug. 2025, doi: 10.52436/1.jpti.941.

J. E. Br Sinulingga and H. C. K. Sitorus, “Analisis Sentimen Opini Masyarakat terhadap Film Horor Indonesia Menggunakan Metode SVM dan TF-IDF,” Jurnal Manajemen Informatika (JAMIKA), vol. 14, no. 1, pp. 42–53, Feb. 2024, doi: 10.34010/jamika.v14i1.11946.

A. Salsabila, “Analisis Sentimen Ulasan Aplikasi ChatGPT Pada Google Play Menggunakan Metode Support Vector Machine,” Feb. 2024.

N. Widaad and D. Anggraini, “SENTIMENT ANALYSIS OF CHATGPT APP USER REVIEWS USING SVM AND CNN METHODS,” Jurnal Teknik Informatika (Jutif), vol. 5, no. 6, pp. 1687–1700, Dec. 2024, doi: 10.52436/1.jutif.2024.5.6.4010.

Muthia Sakhdiah, Admi Salma, Dony Permana, and Dina Fitria, “Sentiment Analysis Using Support Vector Machine (SVM) of ChatGPT Application Users in Play Store,” UNP Journal of Statistics and Data Science, vol. 2, no. 2, pp. 151–158, May 2024, doi: 10.24036/ujsds/vol2-iss2/158.

Downloads

Published

2025-12-29

Issue

Vol. 10 No. 2 (2025)

Section

Articles

License

Copyright (c) 2025 Telcomatics

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.