Deteksi Sentimen Komentar Aplikasi Gobis Suroboyo dengan Metode Naive Bayes dan Metode Regresi Logistik
DOI:
https://doi.org/10.54259/jdmis.v3i2.4691Keywords:
Sentiment Analysis, Naive Bayes, Logistic Regression, Gobis SuroboyoAbstract
This research discusses sentiment analysis of user comments on the Gobis Suroboyo application using the Naive Bayes algorithm and Logistic Regression. Data was obtained through web scraping method from Google Play Store, with a total of 1,015 comments which then went through text pre-processing such as data cleaning, case folding, stemming, normalisation, filtering, tokenizing, and feature selection using TF-IDF. Sentiment labels were determined based on user ratings, with ratings above 3 as positive and 3 and below as negative. The results show that the Naive Bayes algorithm is better at classifying positive sentiment with a precision of 81% and f1-score of 77%, while Logistic Regression excels at negative sentiment with a precision of 82% and f1-score of 82%. The WordCloud visualisation shows dominant words such as “app”, “good”, and “bus stop” that reflect users attention to the app features and transportation services. The findings show that both algorithms have competitive and reliable performance for evaluating public opinion on comment-based digital services. This research is expected to be a reference for app developers and local governments in improving the quality of digital public services.
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S. Rahayu, Y. MZ, J. E. Bororing, dan R. Hadiyat, “Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP,” Edumatic: Jurnal Pendidikan Informatika, vol. 6, no. 1, hlm. 98–106, Jun 2022, doi: 10.29408/edumatic.v6i1.5433.
D. Nugraha dan D. Gustian, “Analisis Sentimen Penggunaan Aplikasi Transportasi Online Pada Ulasan Google Play Store dengan Metode Naive Bayes Classifier,” 2024.
M. A. S. Nugroho, D. Susilo, dan D. Retnoningsih, “ANALISIS SENTIMEN ULASAN APLIKASI ”ACCESS BY KAI” MENGGUNAKAN ALGORITMA MACHINE LEARNING,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 7, no. 2, hlm. 820, Des 2024, doi: 10.37600/tekinkom.v7i2.1854.
D. A. Nugroho, "Perbandingan Kinerja Algoritma Logistic Regression dan Naïve Bayes dalam Prediksi Penyakit Jantung," Laporan Skripsi, Fakultas Ilmu Komputer, Universitas Mercu Buana, Jakarta, 2023.
M. Khoirul, U. Hayati, dan O. Nurdiawan, “ANALISIS SENTIMEN APLIKASI BRIMO PADA ULASAN PENGGUNA DI GOOGLE PLAY MENGGUNAKAN ALGORITMA NAIVE BAYES,” 2023.
O. Lock dan C. Pettit, “Social media as passive geo-participation in transportation planning–how effective are topic modeling & sentiment analysis in comparison with citizen surveys?,” Geo-Spatial Information Science, vol. 23, no. 4, hlm. 275–292, 2020, doi: 10.1080/10095020.2020.1815596.
N. F. Hilmi dan F. Irwiensyah, “Analisis Sentimen Terhadap Aplikasi Tiktok Dari Ulasan Pada Google Playstore Menggunakan Metode Naïve Bayes,” SMATIKA JURNAL, vol. 14, no. 01, hlm. 146–156, Jul 2024, doi: 10.32664/smatika.v14i01.1210.
W. Khofifah, D. N. Rahayu, dan A. M. Yusuf, “Analisis Sentimen Menggunakan Naive Bayes Untuk Melihat Review Masyarakat Terhadap Tempat Wisata Pantai Di Kabupaten Karawang Pada Ulasan Google Maps,” Jurnal Interkom: Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 16, no. 4, hlm. 28–38, Jan 2022, doi: 10.35969/interkom.v16i4.192.
A. Averina, H. Hadi, dan J. Siswantoro, “Analisis Sentimen Multi-Kelas Untuk Film Berbasis Teks Ulasan Menggunakan Model Regresi Logistik,” Teknika, vol. 11, no. 2, hlm. 123–128, Jun 2022, doi: 10.34148/teknika.v11i2.461.
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," 2024 IEEE 10th Information Technology International Seminar (ITIS), Surabaya, Indonesia, 2024, pp. 84-88, doi: 10.1109/ITIS64716.2024.10845453.
B. Ramadhani dan R. R. Suryono, “Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 2, hlm. 714, Apr 2024, doi: 10.30865/mib.v8i2.7458.
C. Zai dan A. Rahman Isnain, “Komparasi Algoritma Naïve Bayes dan Support Vector Machine (SVM) pada Analisis Sentimen Capcut,” vol. 9, no. 1, hlm. 2024.
H. Parasian Doloksaribu dan Y. T. Samuel, “KOMPARASI ALGORITMA DATA MINING UNTUK ANALISIS SENTIMEN APLIKASI PEDULILINDUNGI,” vol. 16, no. 1, 2022, doi: 10.47111/JTI.
Friska Aditia Indriyani, Ahmad Fauzi, dan Sutan Faisal, “Analisis sentimen aplikasi tiktok menggunakan algoritma naïve bayes dan support vector machine,” TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika, vol. 10, no. 2, hlm. 176–184, Jul 2023, doi: 10.37373/tekno.v10i2.419.
J. Homepage, N. C. Agustina, D. Herlina Citra, W. Purnama, C. Nisa, dan A. Rozi Kurnia, “MALCOM: Indonesian Journal of Machine Learning and Computer Science The Implementation of Naïve Bayes Algorithm for Sentiment Analysis of Shopee Reviews on Google Play Store Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Shopee pada Google Play Store,” vol. 2, hlm. 47–54, 2022.
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Copyright (c) 2025 Shifa Elmaliyasari, Muhammad Arsyad Alzam, Nanda Aulia Pratiwi, Shindi Sheila May Wara, Kartika Maulida Hindrayani
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