Komparasi Pengaruh Model Klasifikasi Naive Bayes dan Support Vector Machine Pada Analisis Data Sentimen Di Bidang Pendidikan
(1) Universitas Budi Luhur
(2) Universitas Budi Luhur
(*) Corresponding Author
Abstract
Penerapan data mining dalam text mining processing banyak dimanfaatkan dalam penelitian analisis sentimen. Beberapa penelitian analisis sentimen menggunakan model klasifikasi supervised machine learning seperti Naive Bayes dan Support Vector Machine. Tujuan penelitian adalah mengevaluasi bagaimana pengaruh model klasifikasi Naive Bayes dan Support Vector Machine pada analisis sentimen, khususnya dibidang pendidikan. Beberapa penelitian terdahulu banyak mengambil objek penelitian analisis sentimen pada bidang pemasaran, sosial, ekonomi, politik, sehingga analisa penelitian akan membantu memberikan strategi pengembangan penelitian analisis sentimen dibidang pendidikan. Pada bidang bidang pendidikan sumber data yang digunakan misalnya dari opini siswa dan guru terkait capaian pembelajaran. Hasil penelitian menunjukkan model klasifikasi Naive Bayes dan Support Vector Machine dapat memberikan nilai akurasi yang baik dalam penelitian analisis sentimen, namun penggabungan kedua model dengan pendekatan ensemble lebih meningkatkan capaian akurasi. Untuk penelitian anaisis sentimen dibidang pendidikan ada beberapa faktor penting yang perlu diperhatikan seperti kontribusi penelitian, metode implementasi data mining, parameter yang mempengaruhi, evaluasi data dan resiko kegagalan. Semua faktor tersebut diharapkan dapat diperhatikan sebagai conceptual framework yang akan mendukung keberhasilan dalam penelitian analisis sentimen di bidang pendidikan bagi penelitian yang dilakukan di masa mendatang.
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A. A. Q. Aqlan, B. Manjula, and R. Lakshman Naik, A study of sentiment analysis: Concepts, techniques, and challenges, vol. 28. Springer Singapore, 2019. doi: 10.1007/978-981-13-6459-4_16.
P. A. and Y. K. M. Ganpat Singh Chauhan, “Aspect-Based Sentiment Analysis of Students’ Feedback to Improve Teaching–Learning Process,” Asp. Sentim. Anal. Students’ Feed. to Improv. Teaching–Learning Process, vol. 2, no. January, pp. 83–93, 2019, doi: 10.1007/978-981-13-1747-7.
M. K. Jiawei Han, Data Mining : Concepts and Techniques, no. Vol 3. 2012. [Online]. Available: https://eur-lex.europa.eu/legal-content/PT/TXT/PDF/?uri=CELEX:32016R0679&from=PT%0Ahttp://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52012PC0011:pt:NOT
F. Martinez-Plumed et al., “CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories,” IEEE Trans. Knowl. Data Eng., vol. 33, no. 8, pp. 3048–3061, 2021, doi: 10.1109/TKDE.2019.2962680.
A. N. H. Ananda Kejora Rotty, Triwulandari Satitidjati Dewayana, “Cross-Industry Standard Process for Data Mining (CRISP-DM) Approach in Determining the Most Significant Employee Engagement Drivers to Sales at X Car Dealership,” Proc. 3rd Asia Pacific Int. Conf. Ind. Eng. Oper. Manag. Johor Bahru, Malaysia, Sept. 13-15, 2022, pp. 3368–3379, 2023, doi: 10.46254/ap03.20220552.
Y. Indulkar and A. Patil, “Comparative study of machine learning algorithms for twitter sentiment analysis,” 2021 Int. Conf. Emerg. Smart Comput. Informatics, ESCI 2021, no. July 2014, pp. 295–299, 2021, doi: 10.1109/ESCI50559.2021.9396925.
A. Y. Mücahid Mustafa Saritas, “Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification,” Int. J. Intell. Syst. Appl. Eng., vol. 7(2), pp. 88–91, 2019, doi: 10.1039/b000000x.
K. Korovkinas and P. Dan?nas, “SVM and Naïve Bayes Classification Ensemble Method for Sentiment Analysis,” Balt. J. Mod. Comput., vol. 5, no. 4, pp. 398–409, 2017, doi: 10.22364/bjmc.2017.5.4.06.
V. H. Nhu et al., “Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms,” Int. J. Environ. Res. Public Health, vol. 17, no. 8, 2020, doi: 10.3390/ijerph17082749.
M. Guia, R. R. Silva, and J. Bernardino, “Comparison of Naive Bayes, support vector machine, decision trees and random forest on sentiment analysis,” IC3K 2019 - Proc. 11th Int. Jt. Conf. Knowl. Discov. Knowl. Eng. Knowl. Manag., vol. 1, no. Ic3k, pp. 525–531, 2019, doi: 10.5220/0008364105250531.
O. Y. Adwan, M. Al-Tawil, A. M. Huneiti, R. A. Shahin, A. A. Abu Zayed, and R. H. Al-Dibsi, “Twitter sentiment analysis approaches: A survey,” Int. J. Emerg. Technol. Learn., vol. 15, no. 15, pp. 79–93, 2020, doi: 10.3991/ijet.v15i15.14467.
J. Watkins, M. Fabielli, and M. Mahmud, “SENSE: A Student Performance Quantifier using Sentiment Analysis,” Proc. Int. Jt. Conf. Neural Networks, pp. 0–5, 2020, doi: 10.1109/IJCNN48605.2020.9207721.
Z. Kastrati, F. Dalipi, A. S. Imran, K. P. Nuci, and M. A. Wani, “Sentiment analysis of students’ feedback with nlp and deep learning: A systematic mapping study,” Appl. Sci., vol. 11, no. 9, 2021, doi: 10.3390/app11093986.
S. Khomsah, “Naive Bayes Classifier Optimization on Sentiment Analysis of Hotel Reviews,” J. Penelit. Pos dan Inform., vol. 10, no. 2, p. 157, 2020, doi: 10.17933/jppi.2020.100206.
P. Mehta and S. Pandya, “A review on sentiment analysis methodologies, practices and applications,” Int. J. Sci. Technol. Res., vol. 9, no. 2, pp. 601–609, 2020.
Z. Li, R. Li, and G. Jin, “Sentiment analysis of danmaku videos based on naïve bayes and sentiment dictionary,” IEEE Access, vol. 8, pp. 75073–75084, 2020, doi: 10.1109/ACCESS.2020.2986582.
D. A. Kristiyanti, D. A. Putri, E. Indrayuni, A. Nurhadi, and A. H. Umam, “E-Wallet Sentiment Analysis Using Naïve Bayes and Support Vector Machine Algorithm,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012079.
R. P. M. Hajah T. Sueno, Bobby D. Gerardo, “Multi-class Document Classification using Support Vector Machine (SVM) Based on Improved Naïve Bayes Vectorization Technique,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 3, pp. 3937–3944, 2020, doi: 10.30534/ijatcse/2020/216932020.
S. Bahassine, A. Madani, M. Al-Sarem, and M. Kissi, “Feature selection using an improved Chi-square for Arabic text classification,” J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 2, pp. 225–231, 2020, doi: 10.1016/j.jksuci.2018.05.010.
A. Madasu and S. Elango, “Efficient feature selection techniques for sentiment analysis,” Multimed. Tools Appl., vol. 79, no. 9–10, pp. 6313–6335, 2020, doi: 10.1007/s11042-019-08409-z.
A. R. FITRIANSYAH, “Analisis Sentimen Terhadap Pembangunan Kereta Cepat Jakarta - Bandung pada Media Sosial Twitter Menggunakan Metode SVM dan GloVe Word Embedding,” J. Tugas Akhir Fak. Inform., vol. 9, no. 5, pp. 6078–6083, 2022, [Online]. Available: https://repository.telkomuniversity.ac.id/home/catalog/id/181602
S. Styawati, A. R. Isnain, N. Hendrastuty, and L. Andraini, “Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis,” J. Inform. J. Pengemb. IT, vol. 6, no. 1, pp. 56–60, 2021, doi: 10.30591/jpit.v6i1.3245.
A. S. Neogi, K. A. Garg, R. K. Mishra, and Y. K. Dwivedi, “Sentiment analysis and classification of Indian farmers’ protest using twitter data,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 2, p. 100019, 2021, doi: 10.1016/j.jjimei.2021.100019.
DOI: http://dx.doi.org/10.30998/faktorexacta.v17i2.22342
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