Studi Perbandingan Naïve Bayes dan Support Vector Machine (SVM) dalam Analisis Sentimen Pengguna Metaverse
DOI:
https://doi.org/10.55826/jtmit.v4i3.1122Keywords:
Analisis Sentimen, Metaverse, Naïve Bayes, Support Vector Machine (SVM)Abstract
Penelitian ini bertujuan mengevaluasi persepsi publik di Indonesia terhadap isu metaverse melalui analisis sentimen berbasis text mining. Metaverse, yang memadukan media sosial, permainan daring, augmented reality (AR), virtual reality (VR), serta aset digital seperti cryptocurrency, semakin mendapat perhatian sejak pengumuman perubahan nama Facebook menjadi Meta pada tahun 2021 dan memunculkan beragam opini publik. Data diperoleh dari Twitter (X) dan dianalisis menggunakan dua algoritma klasifikasi teks, yaitu Naïve Bayes dan Support Vector Machine (SVM). Dalam penerapannya, Naïve Bayes menggunakan fungsi MultinomialNB, sedangkan SVM dijalankan dengan LinearSVC yang lebih sesuai untuk data teks berdimensi tinggi. Hasil penelitian menunjukkan bahwa SVM memberikan kinerja lebih baik dengan akurasi 78,3% dan Macro-F1 78,3%, dibandingkan Naïve Bayes yang memperoleh akurasi 72,4% dan Macro-F1 sebesar 60,2%. Selain itu, SVM lebih seimbang dalam mengenali seluruh kelas sentimen, khususnya kategori negatif, sementara Naïve Bayes tetap relevan sebagai baseline karena kesederhanaan dan efisiensinya. Penelitian ini berkontribusi dalam menyajikan perbandingan komparatif kedua algoritma pada analisis sentimen metaverse di Indonesia, sekaligus membuka ruang bagi pengembangan metode yang lebih mutakhir pada studi berikutnya.
References
F. Mahiri, A. Najoua, S. Souda Ben, And N. Amini, “From Industry 4.0 To Industry 5.0: The Transition to Human Centricity And Collaborative Hybrid Intelligence,” Journal Of Hunan University Natural Sciences, Vol. 50, No. 4, Apr. 2023, Doi: 10.55463/Issn. 1674-2974.50.4.8.
A. Ertanto, V. C. Mawardi, And N. J. Perdana, “Analisa Topik Terhadap Komentar Mengenai Metaverse Menggunakan Metode Clustering K-Means,” Jurnal Ilmu Komputer Dan Sistem Informasi, Vol. 11, No. 1, Pp. 1–6, 2023, Doi: 10.24912/Jiksi.V11i1.24073.
A. Nur And Y. D. Putra, “Manajemen Risiko Ekonomi Pada Penerapan Metaverse di Indonesia,” 2023.
S. Yang, H. Joo, And J. Kim, “Metaverse Search System: Architecture, Challenges, And Potential Applications,” ICT Express, Dec. 2023.
Casey Newton, “Facebook’s CEO On Why the Social Network Is Becoming ‘A Metaverse Company’’,’” The Verge. Accessed: Jan. 03, 2024. [Online]. Available: Https://Www.Theverge.Com/22588022/Mark-Zuckerberg-Facebook-Ceo-Metaverse-Interview
Adhitya Karel Maulaya and Junadhi, “Analisis Sentimen Menggunakan Support Vector Machine Masyarakat Indonesia di Twitter Terkait Bjorka,” Jurnal Coscitech (Computer Science and Information Technology), Vol. 3, No. 3, Pp. 495–500, 2022, Doi: 10.37859/Coscitech.V3i3.4358.
M. Fuad Nasvian and R. Afif, “Public Opinion on Facebook Rebrand to Meta: A Twitter Big Data Analysis On The First 24 Hours After Meta Launched Opini Publik Terhadap Rebrand Facebook Ke Meta: Analisis Data Besar Twitter Pada 24 Jam Pertama Setelah Meta Diluncurkan,” 2022.
P. M. Nirmala Dharmapatni and N. L. P. Merawati, “Penerapan Algoritma Support Vector Machine Dalam Sentimen Analisis Terkait Kenaikan Tarif BPJS Kesehatan,” Jurnal Bumigora Information Technology (Bite), Vol. 2, No. 2, Pp. 105–112, Sep. 2020, Doi: 10.30812/Bite.V2i2.904.
D. Darwis, N. Siskawati, And Z. Abidin, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” Vol. 15, No. 1, 2021.
V. B. Kobayashi, S. T. Mol, J. Vrolijk, And G. Kismihók, “Text Mining In Career Studies: Generating Insights From Unstructured Textual Data1,” Handbook Of Research Methods In Careers, Pp. 139–163, 2021, Doi: 10.4337/9781788976725.00015.
A. R. Isnain, A. I. Sakti, D. Alita, And N. S. Marga, “Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma SVM,” Jurnal Data Mining Dan Sistem Informasi, 2021, [Online]. Available: Https://Api.Semanticscholar.Org/Corpusid:233922818
P. J. B. Pajila, B. G. Sheena, A. Gayathri, J. Aswini, M. Nalini, And R. Siva Subramanian, “A Comprehensive Survey On Naive Bayes Algorithm: Advantages, Limitations And Applications,” In Proceedings Of The 4th International Conference On Smart Electronics And Communication, ICOSEC 2023, Institute Of Electrical And Electronics Engineers Inc., 2023, Pp. 1228–1234. Doi: 10.1109/ICOSEC58147.2023.10276274.
N. Ardeshir, C. Sanford, And D. Hsu, “Support Vector Machines and Linear Regression Coincide with Very High-Dimensional Features,” Oct. 2021, [Online]. Available: Http://Arxiv.Org/Abs/2105.14084
R. Guido, S. Ferrisi, D. Lofaro, And D. Conforti, “An Overview on The Advancements of Support Vector Machine Models in Healthcare Applications: A Review,” Information (Switzerland), Vol. 15, No. 4, Apr. 2024, Doi: 10.3390/Info15040235.
M. M. Hamad, M. A. Salih, And R. A. Jaleel, “Sentiment Analysis of Restaurant Reviews In Social Media Using Naïve Bayes,” 2021. [Online]. Available: Http://Arqiipubl.Com/Ams
R. Kamali, Y. S. Sari, I. Aldmour, And R. Budiarto, “Verification of Covid-19 Social Assistance Recipients Using Naïve Bayes Classifier,” International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence, Vol. 1, No. 2, Pp. 1–12, Sep. 2022, Doi: 10.54938/Ijemdcsai.2022.01.2.100.
M. H. Santoso, D. A. Larasati, F. Teknik, And U. M. Area, “Wayang Image Classification Using SVM Method and GLCM Feature Extraction,” Vol. 4, No. January, 2021.
P. C. Shilpa, R. Shereen, S. Jacob, And P. Vinod, “Sentiment Analysis Using Deep Learning,” In Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, Institute Of Electrical And Electronics Engineers Inc., Feb. 2021, Pp. 930–937. Doi: 10.1109/ICICV50876.2021.9388382.
R. Varghese, “A Survey on Sentiment Analysis and Opinion Mining.” [Online]. Available: Http://Www.Ijret.Org
H. T. Duong and T. A. Nguyen-Thi, “A Review: Preprocessing Techniques and Data Augmentation for Sentiment Analysis,” Comput Soc Netw, Vol. 8, No. 1, Dec. 2021, Doi: 10.1186/S40649-020-00080-X.
M. A. Palomino and F. Aider, “Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis,” Applied Sciences (Switzerland), Vol. 12, No. 17, Sep. 2022, Doi: 10.3390/App12178765.
H. J. Aleqabie, M. S. Sfoq, R. A. Albeer, And E. H. Abd, “A Review of Text Mining Techniques: Trends, And Applications in Various Domains,” 2024, College Of Education, Al-Iraqia University. Doi: 10.52866/Ijcsm. 2024.05.01.009.
V. R. Joseph, “Optimal Ratio for Data Splitting,” Feb. 2022, Doi: 10.1002/Sam. 11583.
D. Eliane Birba, “A Comparative Study of Data Splitting Algorithms for Machine Learning Model Selection.”
S. M. Mohammad, “Sentiment Analysis,” In Emotion Measurement, Elsevier, 2021, Pp. 323–379. Doi: 10.1016/B978-0-12-821124-3.00011-9.
N. Malik and S. Jain, “Comparative Study of Machine Learning Algorithms for Social Media Text Analysis,” In Communications In Computer And Information Science, Springer, 2020, Pp. 223–235. Doi: 10.1007/978-981-15-5830-6_19.
A. Rahman Isnain, N. Hendrastuty, And L. Andraini, “Comparison of Support Vector Machine And Naïve Bayes On Twitter Data Sentiment Analysis,” Vol. 6, No. 1, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Sang Dara Parameswari, Muharman Lubis, Sinung Suakanto, Yumna Zahran Ramadhan, Raisyah Nurul Amanah, Revyolla Ananta Dila
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
