Decision Tree for Sentiment Analysis of Facebook Social Media Posts Related to Traffic Congestion in Palembang City
DOI:
https://doi.org/10.30871/jaic.v10i3.12627Keywords:
Decision Tree, Sentiment Analysis, Traffic Congestion, Facebook Social MediaAbstract
This study aims to analyze public perception of traffic congestion in Palembang City through sentiment analysis on Facebook using the Decision Tree algorithm. Data were collected from public comments related to traffic over 32 months using web scraping techniques. Text data were processed through preprocessing stages including case folding, tokenization, stemming, and stopword removal, followed by TF-IDF feature extraction with unigram representation. The model classifies sentiments into positive, negative, and neutral categories. The results show an accuracy of 90.42%. However, the model tends to perform better on the neutral class, influenced by imbalanced data distribution. Therefore, evaluation metrics such as precision and recall are also considered to provide a more comprehensive analysis.
Downloads
References
[1] D. Antoni, M. I. Herdiansyah, M. Akbar, and A. Sumitro, “Pengembangan Infrastruktur Jaringan Untuk Meningkatkan Pelayanan Publik di Kota Palembang,” J. Media Inform. Budidarma, vol. 5, no. 4, p. 1652, 2021, doi: 10.30865/mib.v5i4.3318.
[2] Peraturan Pemerintah Republik Indonesia Nomor 30, “Peraturan Pemerintah Republik Indonesia Nomor: 30 Tahun 2O21, Tentang Penyelenggaraan Bidang Lalu Lintas Dan Angkutan Jalan,” LN.2021/No.40, TLN No.6642, jdih.setkab.go.id43 hlm., no. 085113, pp. 1–57, 2021, [Online]. Available: https://peraturan.bpk.go.id/Home/Details/161874/pp-no-30-tahun-2021.
[3] J. J. Van Bavel, C. E. Robertson, K. del Rosario, J. Rasmussen, and S. Rathje, “Social Media and Morality,” Annu. Rev. Psychol., vol. 75, pp. 311–340, 2024, doi: 10.1146/annurev-psych-022123-110258.
[4] F. Bruno, A. Mautone, D. A. Ali, A. Fassima, H. Khabbache, and A. Rizzo, “Evaluating Facebook scales: A systematic review of the psychological assessment tools,” Syst. Rev. Artic. Psychol., vol. 2, no. 3, pp. 142–156, 2025, doi: 10.5281/zenodo.13351015.
[5] A. Nazir, Y. Rao, L. Wu, and L. Sun,“Issues and Challenges of Aspect-based Sentiment Analysis: A Comprehensive Survey,” IEEE Trans. Affect. Comput., vol. 13, no. 2, pp. 845–863, 2022, doi: 10.1109/TAFFC.2020.2970399.
[6] H. M. Sayeed, W. Smallwood, S. G. Baird, and T. D. Sparks, “NLP meets materials science: Quantifying the presentation of materials data in literature,” Matter, vol. 7, no. 3, pp. 723–727, 2024, doi: 10.1016/j.matt.2023.12.032.
[7] Y. Mao, Q. Liu, and Y. Zhang,“Sentiment-analysis-methods, applications, and challenges: A systematic-literature-review,” J. King Saud Univ. Comput. Inf. Sci., vol.36, no.4, p.102048, 2024, doi: 10.1016/j.jksuci.2024.102048.
[8] A. Chamekh, M. Mahfoudh, and G. Forestier,“Sentiment Analysis Based on Deep Learning in E-Commerce,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13369 LNAI, pp. 498–507, 2022, doi: 10.1007/978-3-031-10986-7_40.
[9] E. Helmud, E. Helmud, F. Fitriyani, and P. Romadiana, “Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 13, no. 1, pp. 92–97, 2024, doi: 10.32736/sisfokom.v13i1.1985.
[10] C.A. Nurhaliza Agustina, R. Novita, Mustakim, and N.E. Rozanda, “The Implementation of TF-IDF and Word2Vec on Booster Vaccine Sentiment Analysis Using Support Vector Machine Algorithm,” Procedia Comput. Sci., vol.234, pp.156–163, 2024, doi: 10.1016/j.procs.2024.02.162.
[11] J. Jin, “Research and construction of student management platform for special needs students with decision tree model and big data technology,” Syst. Soft Comput., vol. 7, no. May, p. 200310, 2025, doi: 10.1016/j.sasc.2025.200310.
[12] R. R. Fajriani et al., “Analisis sentimen pengguna lrt jabodebek di platform x menggunakan algoritma svm,” vol. 8, no. 6, pp. 12446–12453, 2024.
[13] M. A. Brown, A. Gruen, G. Maldoff, S. Messing, Z. Sanderson, and M. Zimmer, “Webscraping for research: legal, ethical, institutionall, and Scientific Considerations,” pp.1–43, 2024, [Online].Available: http://arxiv.org/abs/2410.23432, 2024.
[14] I. Lauriola, A. Lavelli, and F. Aiolli, “An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools,” Neurocomputing, vol. 470, no. xxxx, pp. 443–456, 2022, doi: 10.1016/j.neucom.2021.05.103.
[15] A. Kurniasih and L. P. Manik, “On the Role of Text Preprocessing in BERT embedding-based DNNs for Classifying Informal Texts,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, pp. 927–934, 2022, doi: 10.14569/IJACSA.2022.01306109.
[16] N.Wulandari, Y.Cahyana, and H.Handayani, “Sentiment Analysis on the Relocation of the National Capital (IKN) on Social Media X-Using Naive Bayes and K-Nearest Neighbor (KNN) Methods,” vol.9, no.3, pp.724–731, 2025.
[17] M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.
[18] N. Trung Tuyen, N. Thanh Dang, D. Hon Minh, and T. Q. Nguyen, “Enhancing human activity recognition model performance through KMeans-based stratified data splitting,” Meas. Control (United Kingdom), 2025, doi: 10.1177/00202940241312873.
[19]M. Grootendorst, “BERTopic: Neural topic modeling with a class-based TF-IDF procedure,” 2022, [Online]. Available: http://arxiv.org/abs/2203.05794, 2022.
[20] A. Ayashi, A. Pourahmad, H. A. Faraji Sabokbar, S. Zanganeh Shahraki, and F. Carbone, “Introducing an intelligent web scraping (mining) method for analyzing online reviews of Tehran accommodation users,” J. Urban Tour., vol.12, no.1, pp.99–118, 2025, [Online]. Available: http://doi.org/10.22059/JUT.2025.378415.1221
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Sukemi Sukemi, Ahmad Fali Oklilas, Hatta Efrizal
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
