The Effect Of Chi-Square Feature Selection On The Naive Bayes Algorithm In Analyzing The Sentiment Of Gojek Application Reviews On Google Play Store
Abstract
This study analyzes customer sentiment in reviewing the Gojek application to find out whether Chi-Square feature selection can improve the performance of the sentiment analysis model. This study uses 12,000 Gojek review data, starting with labeling positive, negative, or neutral based on user ratings of the reviews. Naive Bayes with and without Chi-Square feature selection is used in testing related to accuracy, precision, recall, and F1 score. The best performance is obtained by using alpha 0.5 combined with the best 2000 Chi-Square features, which produces 86.96% accuracy, 87.84% precision, 86.96% recall, and 85.29% F1 score on imbalanced data. SMOTE is also used to handle the low number of neutral reviews, but it produces lower accuracy. In conclusion, Chi-Square feature selection in the Naive Bayes algorithm can improve model accuracy on imbalanced and balanced datasets.
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Iwan Sinanto Ate and Ahlijati Nuraminah, “Komparasi Algoritma Feature Selection Pada Analisis Sentimen Review Film,” JUITIK, vol. 2, no. 2, pp. 96–102, Jul. 2022, doi: 10.55606/juitik.v2i2.326.
A. P. P. Wardani, A. Adiwijaya, and M. D. Purbolaksono, “Sentiment Analysis on Beauty Product Review Using Modified Balanced Random Forest Method and Chi-Square,” josh, vol. 4, no. 1, pp. 1–7, Oct. 2022, doi: 10.47065/josh.v4i1.2047.
K. D. Indarwati and H. Februariyanti, “Analisis Sentimen Terhadap Kualitas Pelayanan Aplikasi Gojek Menggunakan Metode Naive Bayes Classifier,” JATISI, vol. 10, no. 1, Mar. 2023, doi: 10.35957/jatisi.v10i1.2643.
W. Yulita, “Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier,” JDMSI, vol. 2, no. 2, p. 1, Aug. 2021, doi: 10.33365/jdmsi.v2i2.1344.
M. B. Hamzah, “Classification of Movie Review Sentiment Analysis Using Chi-Square and Multinomial Naïve Bayes with Adaptive Boosting,” J. Adv. Inf. Syst. Tech, vol. 3, no. 1, pp. 67–74, Apr. 2021, doi: 10.15294/jaist.v3i1.49098.
H. Setiawan, E. Utami, and S. Sudarmawan, “Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes,” JKKI, vol. 5, no. 1, pp. 43–51, Jul. 2021, doi: 10.31603/komtika.v5i1.5189.
M. D. Hendriyanto, A. A. Ridha, and U. Enri, “Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine,” INTECOMS, vol. 5, no. 1, pp. 1–7, Apr. 2022, doi: 10.31539/intecoms.v5i1.3708.
A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “ANALISIS SENTIMEN APLIKASI RUANG GURU DI TWITTER MENGGUNAKAN ALGORITMA KLASIFIKASI,” JTI, vol. 14, no. 2, p. 115, Jul. 2020, doi: 10.33365/jti.v14i2.679.
Universitas Muria Kudus and F. Rizqi Irawan, “ANALISIS SENTIMEN TERHADAP PENGGUNA GOJEK MENGGUNAKAN METODE K-NEARSET NEIGHBORS,” JIKO, vol. 5, no. 1, pp. 62–68, Apr. 2022, doi: 10.33387/jiko.v5i1.4267.
A. R. Isnain, H. Sulistiani, B. M. Hurohman, A. Nurkholis, and S. Styawati, “Analisis Perbandingan Algoritma LSTM dan Naive Bayes untuk Analisis Sentimen,” JEPIN, vol. 8, no. 2, p. 299, Aug. 2022, doi: 10.26418/jp.v8i2.54704.
T. Ernayanti, M. Mustafid, A. Rusgiyono, and A. R. Hakim, “PENGGUNAAN SELEKSI FITUR CHI-SQUARE DAN ALGORITMA MULTINOMIAL NAÏVE BAYES UNTUK ANALISIS SENTIMEN PELANGGGAN TOKOPEDIA,” J.Gauss, vol. 11, no. 4, pp. 562–571, Feb. 2023, doi: 10.14710/j.gauss.11.4.562-571.
A. Nisa and E. Darwiyanto, “Analisis Sentimen Menggunakan Naive Bayes Classifier dengan Chi-Square Feature Selection Terhadap Penyedia Layanan Telekomunikasi”.
Ramanda Md, Reza Dwi Restiyan, and Hafidz Irsyad, “Analisis Sentimen Masyarakat terhadap Perilaku Lawan Arah yang Diunggah pada Media Sosial Youtube Menggunakan Naïve Bayes,” BANDWIDTH, vol. 2, no. 2, pp. 75–83, Jul. 2024, doi: 10.53769/bandwidth.v2i2.706.
R. A. Nurdian, Mujib Ridwan, and Ahmad Yusuf, “Komparasi Metode SMOTE dan ADASYN dalam Meningkatkan Performa Klasifikasi Herregistrasi Mahasiswa Baru,” JuTISI, vol. 8, no. 1, Apr. 2022, doi: 10.28932/jutisi.v8i1.4004.
DOI: https://doi.org/10.24167/proxies.v9i1.13124
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