Sentiment Analysis of UINSU Students' Comfort Towards Trans Metro Deli Services at Taman Budaya Bus Stop Using the Naive Bayes Method
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Abstract
The large number of bus passengers at the Taman Budaya bus stop is one of the public transportation problems. Finding that the Metro Deli Bus Organizer is still operating. They are considered capable of meeting the requirements for choosing a mode of transportation. The purpose of this study is to determine the passenger transportation factors on the Trans Metro Deli Bus. The Trans Metro Deli Bus Passenger Transportation Factor is the purpose of this study. Data Collection Techniques Using Questionnaires Student comfort factors when using the Trans Metro Deli bus service. This study's methodology starts with problem identification and moves on to problem-solving techniques and assessment procedures. Respondents were given a questionnaire to fill out to collect data. The author of this study used Google Forms. The author of this study solved the problem using the Naïve Bayes algorithm. The Naïve Bayes algorithm model produces results with an accuracy of up to 71.43%, which is quite good. The accuracy results of 71.43% and approaching 100% show how accurate the sentiment analysis is using the Naïve Bayes classification. The accuracy results of 71.43% and approaching 100% show how accurate the sentiment analysis is using the Naïve Bayes classification. 'The bus took a long time to arrive' and 'didn't get a seat' were the most common negative reviews, indicating that some students felt uncomfortable. The Naïve Bayes results of the study showed that people who reviewed the Trans Metro Deli Bus expressed more positive opinions, with the highest score of 71.43%.
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References
W. Putri, L. Levyda, and T. Hardiyanto, (2021), “Pengaruh Harga, Kualitas Produk, Kualitas Pelayanan, dan Promosi terhadap Keputusan Pembelian Produk,” Management & Accounting Expose, vol. 4, no. 2, pp. 129–138. DOI: https://doi.org/10.36441/accounting.v4i2.372
M. Khairani, F. Rakhmawati, and H. Cipta, (2022), “Penentuan Faktor-Faktor Prioritas Yang Mempengaruhi Masyarakat Dalam Penggunaan Bus Trans Metro Deli Medan Dengan Analisis Metode Analytic Hierarchy Process,” Jurnal Informatika Teknologi dan Sains, vol. 4, no. 4, pp. 400–405, doi:10.51401/jinteks.v4i4.2137. DOI: https://doi.org/10.51401/jinteks.v4i4.2137
M. I. Santoso and A. R. Dzikrillah, (2024), “Analisis Sentimen Pengguna Terhadap Kinerja Sistem Transportasi Umum Jakarta Menggunakan Algoritma Naive Bayes,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 6, pp. 3032–3043, doi:10.30865/klik.v4i6.1936.
F. V. Sari and A. Wibowo, (2019), “Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” Jurnal SIMETRIS, vol. 10, no. 2, pp. 681–686.
J. Ling, I. P. E. Kencana, and T. B. Oka, (2014), “Analisis Sentimen Menggunakan Metode Naïve Bayes Classifier Dengan Seleksi Fitur Chi Square,” E-Jurnal Matematika, vol. 3, no. 3, p. 92, doi:10.24843/MTK.2014.V03.I03.P070. DOI: https://doi.org/10.24843/MTK.2014.v03.i03.p070
E. P. Kondy, S. Siswanto, and N. Ilyas, (2024), “Data Balancing Approach Using Combine Sampling on Sentiment Analysis With K-Nearest Neighbor,” SISTEMASI, vol. 13, no. 5, p. 1836, doi:10.32520/STMSI.V13I5.4013. DOI: https://doi.org/10.32520/stmsi.v13i5.4013
Y. Yunefri, Y. E. Fadrial, and S. Sutejo, (2021), “Chatbot Pada Smart Cooperative Oriented Problem Menggunakan Natural Language Processing dan Naive Bayes Classifier,” INTECOMS: Journal of Information Technology and Computer Science, vol. 4, no. 2, pp. 131–140, doi:10.31539/intecoms.v4i2.2704. DOI: https://doi.org/10.31539/intecoms.v4i2.2704
O. I. Gifari, M. Adha, F. Freddy, and F. F. S. Durrand, (2022), “Film Review Sentiment Analysis Using TF-IDF and Support Vector Machine,” Journal of Information Technology, vol. 2, no. 1, pp. 36–40. DOI: https://doi.org/10.46229/jifotech.v2i1.330
E. Kurnianto and D. Febriawan, (2023), “Analisis Sentimen Perbedaan Pendapat Netizen Indonesia Terhadap Penutupan Tiktok Shop Menggunakan Algoritma Naïve Bayes,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 5, no. 2, p. 404, doi:10.30865/json.v5i2.7170. DOI: https://doi.org/10.30865/json.v5i2.7170
A. Aziz, (2022), “Analisis Sentimen Identifikasi Opini Terhadap Produk, Layanan dan Kebijakan Perusahaan Menggunakan Algoritma TF-IDF dan SentiStrength,” Jurnal Sains Komputer & Informatika (J-SAKTI, vol. 6, no. 1, p. 115.
A. Salsabila, R. Yunita, and C. Rozikin, (2021), “Identifikasi Citra Jenis Bunga menggunakan Algoritma KNN dengan Ekstrasi Warna HSV dan Tekstur GLCM,” Technomedia Journal, vol. 6, no. 1, pp. 124–137, doi:10.33050/tmj.v6i1.1667. DOI: https://doi.org/10.33050/tmj.v6i1.1667
N. Riska Fadhila et al., (2023), “Implementasi Machine Learning Untuk Klasifikasi Narasi Informative dan Non-Informative Pada Media Sosial Twitter TMC Polda Metro Jaya Menggunakan Naïve Bayes Classifier,” Jurnal Komputer Antartika, vol. 1, p. 2023.
A. U. T. Ama, D. N. Mulya, Y. P. D. Astuti, and I. B. G. Prasadhya, (2022), “Analisis Sentimen Customer Feedback Tokopedia Menggunakan Algoritma Naïve Bayes,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 4, no. 1, p. 50, doi:10.30865/json.v4i1.4783. DOI: https://doi.org/10.30865/json.v4i1.4783
N. K. Widyasanti, I. K. G. Darma Putra, and N. K. Dwi Rusjayanthi, (2018), “Seleksi Fitur Bobot Kata dengan Metode TFIDF untuk Ringkasan Bahasa Indonesia,” Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), p. 119, doi:10.24843/JIM.2018.V06.I02.P06. DOI: https://doi.org/10.24843/JIM.2018.v06.i02.p06