ANALISIS SENTIMEN ULASAN PUBLIK PADA X MENGGUNAKAN NAIVE BAYES UNTUK MENINGKATKAN KUALITAS LAYANAN TRANSJAKARTA
Abstract
As the largest public transportation system in Jakarta, TransJakarta frequently becomes a topic of discussion on Twitter due to various user experiences ranging from satisfaction to service-related complaints. The abundance of public opinions provides an opportunity to apply machine learning–based sentiment analysis as a faster, more objective, and measurable method for evaluating service quality. This study employs the Naive Bayes algorithm to classify tweet sentiments into positive, negative, and neutral categories. Data were collected through a crawling process using keywords related to TransJakarta, then processed through several stages including tokenization, text cleaning, stopword removal, and stemming to produce analysis-ready data. Several service aspects discussed most frequently include bus arrival punctuality, travel comfort, and fleet conditions. The analysis results indicate that the Naive Bayes model can accurately identify public sentiment patterns in near real-time, enabling the detection of opinion trends across different service dimensions. These findings demonstrate that Naive Bayes–based sentiment analysis can serve as an effective tool for monitoring public perception and provide a strong foundation for TransJakarta in formulating strategies to improve service quality.
References
Cindy Mutia Annur, “No Title,” Ini Rute Bus Transjakarta dengan Jumlah Penumpang Terbanyak pada 2021. Accessed: Dec. 12, 2025. [Online]. Available: https://databoks.katadata.co.id/transportasi-logistik/statistik/9d26bc18b58a174/ini-rute-bus-transjakarta-dengan-jumlah-penumpang-terbanyak-pada-2021
K. F. Ramdhania, D. F. Hidayat, and R. Salkiawati, “Implementasi Metode Naïve Bayes dan Support Vector Machine ( SVM ) untuk Menganalisis Sentimen Pengguna Twitter terhadap Transjakarta ( Implementation of Naïve Bayes and SVM Methods to Analyze Twitter User Sentiment on Transjakarta ),” J. Mat. dan Pendidik. Mat., vol. 9, no. 1, pp. 1–14, 2024.
M. I. Fikri, T. S. Sabrila, and Y. Azhar, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” Smatika J., vol. 10, no. 02, pp. 71–76, 2020, doi: 10.32664/smatika.v10i02.455.
O. P. Zusrotun, A. C. Murti, and R. Fiati, “Sentimen Analisis Belajar Online Di Twitter Menggunakan Naïve Bayes Jurnal Nasional Pendidikan Teknik Informatika : Janapati | 311,” J. Nas. Pendidik. Tek. Inform. JANAPATI |, vol. 11, pp. 310–320, 2022.
I. B. G. Sarasvananda, D. Selivan, M. L. Radhitya, and I. N. T. A. Putra, “Analisis Sentimen Pada Pembelajaran Daring Di Indonesia Melalui Twitter Menggunakan Naïve Bayes Classifier,” SINTECH (Science Inf. Technol. J., vol. 5, no. 2, pp. 227–233, 2022, doi: 10.31598/sintechjournal.v5i2.1241.
H. Zhafran Muflih and F. Noor Hasan, “KLIK: Kajian Ilmiah Informatika dan Komputer Analisis Sentimen Terhadap Pelayanan TransJakarta Berdasarkan Tweets Menggunakan Metode Naïve Bayes Classifier,” Media Online), vol. 4, no. 6, pp. 3044–3052, 2024, doi: 10.30865/klik.v4i6.1927.
N. Safitri, R. Alfiran, D. Tamitiadini, D. Dewi, W. W. A. Febriani, Analisis Sentimen: Metode Alternatif Penelitian Big Data. Universitas Brawijaya Press, 2021.
I. Iwandini, A. Triayudi, and G. Soepriyono, “Analisa Sentimen Pengguna Transportasi Jakarta Terhadap Transjakarta Menggunakan Metode Naives Bayes dan K-Nearest Neighbor,” J. Inf. Syst. Res., vol. 4, no. 2, pp. 543–550, 2023, doi: 10.47065/josh.v4i2.2937.
E. Martantoh and N. Yanih, “Implementasi Metode Naïve Bayes Untuk Klasifikasi Karakteristik Kepribadiaan Siswa Di Sekolah MTS Darussa’adah Menggunakan Php Mysql,” J. Teknol. Sist. Inf., vol. 3, no. 2, pp. 166–175, 2022, doi: 10.35957/jtsi.v3i2.2896.
