Analisis Sentimen Tweet Tentang UU Cipta Kerja Menggunakan Algoritma SVM Berbasis PSO
DOI:
https://doi.org/10.14421/jiska.2022.7.1.10-19Keywords:
Sentiment Analysis, Twitter, UU Cipta Kerja, SVM, PSOAbstract
Support Vector Machine (SVM) is one of the most widely used classification algorithms for sentiment analysis and has been shown to provide satisfactory performance. However, despite its advantages, the SVM algorithm still has weaknesses in selecting the right SVM parameters to optimize the performance. In this study, sentiment analysis was done with the use of data called tweets about Undang-Undang Cipta Kerja which reap many pros and cons by the people in Indonesia, especially the laborers. The classification method used in this study is the Support Vector Machine algorithm which is optimized using the Particle Swarm Optimization method for the SVM parameters selection in the hope of optimizing the performance generated by the SVM algorithm in sentiment analysis. The results of the study using 10 k-fold cross-validations using the SVM algorithm resulted in an accuracy of 92,99%, a precision of 93,24%, and a recall of 93%. Meanwhile, the SVM and PSO algorithms produce an accuracy of 95%, precision of 95,08%, and recall of 94,97%. The results show that the Particle Swarm Optimization method can overcome the weaknesses of the Support Vector Machine algorithm in the problem of parameter selection and has succeeded in improving the resulting performance where the SVM-PSO is more superior to SVM without optimization in sentiment analysis.
References
Al Rivan, M. E., Rachmat, N., & Ayustin, M. R. (2020). Klasifikasi Jenis Kacang-Kacangan Berdasarkan Tekstur Menggunakan Jaringan Syaraf Tiruan. Jurnal Komputer Terapan, 6(1), 89–98. https://doi.org/10.35143/jkt.v6i1.3546
Alasadi, S. A., & Bhaya, W. S. (2017). Review of Data Preprocessing Techniques in Data Mining. In Journal of Engineering and Applied Sciences (Vol. 12, Issue 16, pp. 4102–4107). https://doi.org/10.3923/jeasci.2017.4102.4107
Arsi, P., Wahyudi, R., & Waluyo, R. (2021). Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 231–237. https://doi.org/10.29207/resti.v5i2.2698
Basari, A. S. H., Hussin, B., Ananta, I. G. P., & Zeniarja, J. (2013). Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. Procedia Engineering, 53, 453–462. https://doi.org/10.1016/j.proeng.2013.02.059
Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences, 340–341, 250–261. https://doi.org/10.1016/j.ins.2016.01.033
Godara, N., & Kumar, S. (2019). Opinion Mining using Machine Learning Techniques. International Journal of Engineering and Advanced Technology, 9(2), 4287–4292. https://doi.org/10.35940/ijeat.B4108.129219
Hayatin, N., Marthasari, G. I., & Nuarini, L. (2020). Optimization of Sentiment Analysis for Indonesian Presidential Election using Naive Bayes and Particle Swarm Optimization. Jurnal Online Informatika, 5(1), 81–88. https://doi.org/10.15575/join.v5i1.558
Iqbal, F., Hashmi, J. M., Fung, B. C. M., Batool, R., Khattak, A. M., Aleem, S., & Hung, P. C. K. (2019). A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction. IEEE Access, 7(c), 14637–14652. https://doi.org/10.1109/ACCESS.2019.2892852
Jo, V. (2019). Introduction. Seminars in Diagnostic Pathology, 36(2), 83–84. https://doi.org/10.1053/j.semdp.2019.02.002
Kiranyaz, S., Ince, T., & Gabbouj, M. (2014). Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Springer.
Kristiyanti, D. A., & Wahyudi, M. (2017). Feature selection based on Genetic algorithm, particle swarm optimization and principal component analysis for opinion mining cosmetic product review. 2017 5th International Conference on Cyber and IT Service Management (CITSM), 1–6. https://doi.org/10.1109/CITSM.2017.8089278
Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., & Wang, S. (2011). An improved particle swarm optimization for feature selection. Journal of Bionic Engineering, 8(2), 191–200. https://doi.org/10.1016/S1672-6529(11)60020-6
Prasetyaningrum, I., Fathoni, K., & Priyantoro, T. T. J. (2020). Application of recommendation system with AHP method and sentiment analysis. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3), 1343. https://doi.org/10.12928/telkomnika.v18i3.14778
Que, V. K. S., Iriani, A., & Purnomo, H. D. (2020). Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 9(2), 162–170. https://doi.org/10.22146/jnteti.v9i2.102
Rana, S., & Singh, A. (2016). Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques. 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), October, 106–111. https://doi.org/10.1109/NGCT.2016.7877399
Rekha, V., Raksha, R., Patil, P., Swaras, N., & Rajat, G. L. (2019). Sentiment Analysis on Indian Government Schemes Using Twitter data. 2019 International Conference on Data Science and Communication (IconDSC), 1–5. https://doi.org/10.1109/IconDSC.2019.8817036
Risawati, R., Ernawati, S., & Maryani, I. (2020). Optimasi Parameter PSO Berbasis SVM untuk Analisis Sentimen Review Jasa Maskapai Penerbangan Berbahasa Inggris. EVOLUSI : Jurnal Sains Dan Manajemen, 8(2), 64–71. https://doi.org/10.31294/evolusi.v8i2.9248
Rustam, F., Ashraf, I., Mehmood, A., Ullah, S., & Choi, G. S. (2019). Tweets Classification on the Base of Sentiments for US Airline Companies. Entropy, 21(11), 1078. https://doi.org/10.3390/e21111078
Sharma, A., & Daniels, A. (2020). Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques.
Tineges, R., Triayudi, A., & Sholihati, I. D. (2020). Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM). JURNAL MEDIA INFORMATIKA BUDIDARMA, 4(3), 650. https://doi.org/10.30865/mib.v4i3.2181
Wardhani, N. K., Rezkiani, Kurniawan, S., Setiawan, H., Gata, G., Tohari, S., Gata, W., & Wahyudi, M. (2018). Sentiment analysis article news coordinator minister of maritime affairs using algorithm naive bayes and support vector machine with particle swarm optimization. Journal of Theoretical and Applied Information Technology, 96(24), 8365–8378.
Windha Mega, P. D., & Haryoko. (2019). Optimization Of Parameter Support Vector Machine (SVM) using Genetic Algorithm to Review Go-Jek’s Services. 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 6, 301–304. https://doi.org/10.1109/ICITISEE48480.2019.9003894
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Trifebi Shina Sabrila, Yufis Azhar, Christian Sri Kusuma Aditya
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms as stated in http://creativecommons.org/licenses/by-nc/4.0
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
b. 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.
c. 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.
