Pembobotan TF-IDF Menggunakan Naïve Bayes pada Sentimen Masyarakat Mengenai Isu Kenaikan BIPIH
Abstract
The Ministry of Religious Affairs proposes to increase the cost of Hajj Travel (Bipih) in 1444 H/2023 M to Rp.69.19 million. There is a fairly high increase in costs compared to 2022. This raises sentiment in the community, there are public opinions for and against the issue of rising Bipih on social media twitter. The purpose of this study was to analyze the sentiment on the issue of increasing the cost of Hajj Travel and to prove whether Naive Bayes is a good classifier of text on the issue of incremental sentiment. Naive Baye is one of the best text classifier algorithms. Data taken from social media twitter. The Data are grouped into pro and Contra opinions and then processed using python programming language and jupyter as text editor. Data used as much as 850 data. The Data is divided into training data and testing data with a ratio of 80:20. With the number of training data of 679 data and the number of testing data of 170 data. Then implement Multinominal Naive Bayes algorithm (MNB) as text classifier and word weighting using TF-IDF. The test results obtained accuracy value of 89% and ROC value of 0.91. It is proven that Multinominal Naive Bayes algorithm (MNB) is a good classifier of text for sentiment analysis of opinion on the issue of increasing the cost of Hajj travel because it is included in the Excellent Classification.
References
A. Yusuf, “Kontroversi Biaya Haji,” Badan Pengelola Keuangan Haji, Feb. 09, 2023. https://bpkh.go.id/kontroversi-biaya-haji/ (accessed Feb. 27, 2023).
M. Khoeron, “BPIH, antara Kalkulasi Biaya dan Kebijakan Politik,” Kementerian Agama, Feb. 21, 2023. https://kemenag.go.id/read/bpih-antara-kalkulasi-biaya-dan-kebijakan-politik-v5bm1 (accessed Mar. 06, 2023).
E. Febriyani and H. Februariyanti, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Algoritma Naive Bayes Classifier Di Twitter,” Tekno Kompak, vol. 17, no. 1, pp. 25–38, 2023.
S. A. El Rahman, F. A. AlOtaibi, and W. A. AlShehri, “Sentiment Analysis of Twitter Data,” in 2019 International Conference on Computer and Information Sciences (ICCIS), 2019.
M. I. Fikri, T. S. Sabrila, and Y. Azhar, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” SMATIKA, vol. 10, no. 2, pp. 71–76, 2020.
A. Alsaeedi and M. Z. Khan, “A study on sentiment analysis techniques of Twitter data,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 2, pp. 361–374, 2019, doi: 10.14569/ijacsa.2019.0100248.
M. Abbas, K. Ali, A. Jamali, K. Ali Memon, and A. Aleem Jamali, “Multinomial Naive Bayes Classification Model for Sentiment Analysis Wireless Sensor Networks View project Analyzing Distributed Denial of Service Attacks in Cloud Computing Towards the Pakistan Information Technology Industry View project Multinomial Naive Bayes Classification Model for Sentiment Analysis,” IJCSNS International Journal of Computer Science and Network Security, vol. 19, no. 3, p. 62, 2019, doi: 10.13140/RG.2.2.30021.40169.
S. P. PM and S. B, “Sentimental Analysis using Naive Bayes Classifier,” in 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019, pp. 1–5.
A. R. Isnain, N. S. Marga, and D. Alita, “Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 15, no. 1, p. 55, Jan. 2021, doi: 10.22146/ijccs.60718.
M. Wongkar and A. Angdresey, “Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler : Twitter,” in International Conference on Informatics and Computing (ICIC), 2019.
M. Romzi and B. Kurniawan, “Pembelajaran Pemrograman Python Dengan Pendekatan Logika Algoritma,” no. 2, pp. 37–44, 2020.
“Project Jupyter’s origins and governance,” Mar. 13, 2023. https://jupyter.org/about (accessed Mar. 13, 2023).
M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing for Student Complaint Document Classification Using Sastrawi,” in IOP Conference Series: Materials Science and Engineering, Jul. 2020, vol. 874, no. 1. doi: 10.1088/1757-899X/874/1/012017.
A. Aninditya, M. A. Hasibuan, and E. Sutoyo, “Text Mining Approach Using TF-IDF and Naive Bayes for Classification of Exam Questions Based on Cognitive Level of Bloom’s Taxonomy,” in International Conference on Internet of Things and Intelligence System (IoTaIS), 2019, pp. 112–117.
“Sastrawi 1.0.1.” https://pypi.org/project/Sastrawi/ (accessed Mar. 13, 2023).
A. Kadhim, “An Evaluation of Preprocessing Techniques for Text Classification Pattern Recognition View project Improvement text classification using log(TF-IDF) with K-NN Algorithm View project,” International Journal of Computer Science and Information Security (IJCSIS), vol. 16, no. 6, pp. 22–36, 2018, [Online]. Available: https://sites.google.com/site/ijcsis/
A. Rahman Isnain, A. Indra Sakti, D. Alita, and N. Satya Marga, “Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma SVM,” JDMSI, vol. 2, no. 1, pp. 31–37, 2021, [Online]. Available: https://t.co/NfhnfMjtXw
M. Chiny, M. Chihab, Y. Chihab, and O. Bencharef, “LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, pp. 265–275, 2021, [Online]. Available: www.ijacsa.thesai.org
D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” vol. 15, no. 1, 2021.
“1.9. Naive Bayes.” https://scikit-learn.org/stable/modules/naive_bayes.html (accessed Mar. 13, 2023).
