Mining the public sentiment for wayang climen preservation and promotion

(1) * Aji Prasetya Wibawa Mail (Universitas Negeri Malang, Indonesia)
(2) Adjie Rosyidin Mail (Universitas Negeri Malang, Indonesia)
(3) Fitriana Kurniawati Mail (Universitas Negeri Malang, Indonesia)
(4) Gwinny Tirza Rarastri Mail (Universitas Negeri Malang, Indonesia)
(5) Ilham Ari Elbaith Zaeni Mail (Universitas Negeri Malang, Indonesia)
(6) Suyono Suyono Mail (Universitas Negeri Malang, Indonesia)
(7) Agung Bella Putra Utama Mail (Universitas Negeri Malang, Indonesia)
(8) Felix Andika Dwiyanto Mail (AGH University of Science and Technology, Poland)
*corresponding author

Abstract


Indonesia is a country that has a variety of cultural arts, one of which is shadow puppetry (Wayang). Wayang, in a staged, simple, and minimalist manner, is called Wayang Climen. Wayang Climen has been performed since the COVID-19 pandemic as a solution to keep working while still complying with health protocols. Utilization through YouTube social media attracts people to watch and provide opinions through comments. This opinion is beneficial and can be used as a feasibility study through sentiment analysis information classified as positive, negative, and neutral opinions. Sentiment analysis determines a person's opinion and tendency to opinionated sentences. The methods used are Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The dataset comes from YouTube comments of Dalang Seno and Ki Seno Nugroho. The best accuracy is generated by SVM (70.29%). The positive sentiment shows the public's appreciation for the Wayang Climen performance, which ultimately represents the performance even though it is staged densely. This research contributes to effectively utilizing digital platforms for cultural preservation and audience engagement during challenging times, demonstrating the potential for innovative solutions in traditional arts and entertainment.

Keywords


Sentiment Analysis; Wayang Climen; Social Media; SVM; Random Forest; Naïve Bayes

   

DOI

https://doi.org/10.31763/viperarts.v5i2.1163
      

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References


[1] N. W. Somawati and I. W. Karja, “Contemporary Wayang Beber: Transformative Narration in the Discourse of Philosophy and Society,” Int. J. Soc. Sci. Hum. Res., vol. 6, no. 08, Aug. 2023, doi: 10.47191/ijsshr/v6-i8-52.

[2] A. Arafahan, Saidin, and F. Y. Sitepu, “Copyright Protection Of Wayang Kulit As A Traditional Indonesian Culture,” POLICY, LAW, NOTARY Regul. ISSUES, vol. 1, no. 4, pp. 1–8, Oct. 2022, doi: 10.55047/polri.v1i4.413.

[3] A. Windarsih, “Traditional Art’s Survival in the Digital Era: Puppet Performance on YouTube,” in Deconstructing Culture and Communication in the Global South, 2023, pp. 141–157. doi: 10.4018/978-1-6684-8093-9.ch010

[4] A. Alacovska, P. Booth, and C. Fieseler, “A Pharmacological Perspective on Technology-Induced Organised Immaturity: The Care-giving Role of the Arts,” Bus. Ethics Q., vol. 33, no. 3, pp. 565–595, Jul. 2023, doi: 10.1017/beq.2022.39.

[5] M. Muslih, A. Rohman, A. Ahmad, and A. Saifullah, “Sunan Kalijaga’s Da’wah Strategy In Suluk Linglung And Its Implication To Indonesian Radicalism Movement,” El-Harakah (Terakreditasi), vol. 23, no. 1, pp. 1–19, Jun. 2021, doi: 10.18860/eh.v23i1.11672.

[6] R. U. Dona, “Character Wayang Timplong of Panji Asmarabangun Figure and its Relevance to the Pancasila Students ’ Profile,” vol. 15, pp. 2846–2856, 2023, doi: 10.35445/alishlah.v15i3.3177.

[7] M. Hänska and S. Bauchowitz, “Can social media facilitate a European public sphere? Transnational communication and the Europeanization of Twitter during the Eurozone crisis,” Soc. Media + Soc., vol. 5, no. 3, p. 205630511985468, Jul. 2019, doi: 10.1177/2056305119854686.

[8] B. Bimber and H. Gil de Zúñiga, “The unedited public sphere,” New Media Soc., vol. 22, no. 4, pp. 700–715, Apr. 2020, doi: 10.1177/1461444819893980.

[9] S. G. Purnama and D. Susanna, “Attitude to COVID-19 Prevention With Large-Scale Social Restrictions (PSBB) in Indonesia: Partial Least Squares Structural Equation Modeling,” Front. Public Heal., vol. 8, Oct. 2020, doi: 10.3389/fpubh.2020.570394.

[10] C. Respa and J. D. Imelda, “Digital Capital As Informal Social Protection In The Pandemic Covid-19’s Era (Case Study Of The Wayang Kulit Association In Yogyakarta),” JHSS (Journal Humanit. Soc. Stud., vol. 5, no. 3, pp. 242–246, Oct. 2021, doi: 10.33751/jhss.v5i3.3994.

[11] C. Baden, C. Pipal, M. Schoonvelde, and M. A. C. G. van der Velden, “Three Gaps in Computational Text Analysis Methods for Social Sciences: A Research Agenda,” Commun. Methods Meas., vol. 16, no. 1, pp. 1–18, Jan. 2022, doi: 10.1080/19312458.2021.2015574.

[12] E. S. Rice, E. Haynes, P. Royce, and S. C. Thompson, “Social media and digital technology use among Indigenous young people in Australia: a literature review,” Int. J. Equity Health, vol. 15, no. 1, p. 81, Dec. 2016, doi: 10.1186/s12939-016-0366-0.

[13] M. L. Khan, “Social media engagement: What motivates user participation and consumption on YouTube?,” Comput. Human Behav., vol. 66, pp. 236–247, Jan. 2017, doi: 10.1016/j.chb.2016.09.024.

[14] L. Maunder, “Motivating people to stay at home: using the Health Belief Model to improve the effectiveness of public health messaging during the COVID-19 pandemic,” Transl. Behav. Med., vol. 11, no. 10, pp. 1957–1962, Oct. 2021, doi: 10.1093/tbm/ibab080.

[15] I. Meloni and E. O. Allasso, “Going ‘Viral,’” J. World Pop. Music, vol. 10, no. 1, Jul. 2023, doi: 10.1558/jwpm.26376.

[16] Kauffmann, Peral, Gil, Ferrández, Sellers, and Mora, “Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining,” Sustainability, vol. 11, no. 15, p. 4235, Aug. 2019, doi: 10.3390/su11154235.

[17] C. Zucco, B. Calabrese, G. Agapito, P. H. Guzzi, and M. Cannataro, “Sentiment analysis for mining texts and social networks data: Methods and tools,” WIREs Data Min. Knowl. Discov., vol. 10, no. 1, Jan. 2020, doi: 10.1002/widm.1333.

[18] Y. HaCohen-Kerner, D. Miller, and Y. Yigal, “The influence of preprocessing on text classification using a bag-of-words representation,” PLoS One, vol. 15, no. 5, p. e0232525, May 2020, doi: 10.1371/journal.pone.0232525.

[19] C. Daryani, G. S. Chhabra, H. Patel, I. K. Chhabra, and R. Patel, “An Automated Resume Screening System Using Natural Language Processing And Similarity,” in Ethics And Information Technology, Jan. 2020, pp. 99–103, doi: 10.26480/etit.02.2020.99.103.

[20] A. Briliani, B. Irawan, and C. Setianingsih, “Hate Speech Detection in Indonesian Language on Instagram Comment Section Using K-Nearest Neighbor Classification Method,” in 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), Nov. 2019, pp. 98–104, doi: 10.1109/IoTaIS47347.2019.8980398.

[21] A. Ishaq et al., “Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques,” IEEE Access, vol. 9, pp. 39707–39716, 2021, doi: 10.1109/ACCESS.2021.3064084.

[22] J. Sun, H. Li, H. Fujita, B. Fu, and W. Ai, “Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting,” Inf. Fusion, vol. 54, pp. 128–144, Feb. 2020, doi: 10.1016/j.inffus.2019.07.006.

[23] J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, Sep. 2020, doi: 10.1016/j.neucom.2019.10.118.

[24] C. Song, A. Shafieezadeh, and R. Xiao, “High-Dimensional Reliability Analysis with Error-Guided Active-Learning Probabilistic Support Vector Machine: Application to Wind-Reliability Analysis of Transmission Towers,” J. Struct. Eng., vol. 148, no. 5, May 2022, doi: 10.1061/(ASCE)ST.1943-541X.0003332.

[25] X.-F. Song, Y. Zhang, D.-W. Gong, and X.-Z. Gao, “A Fast Hybrid Feature Selection Based on Correlation-Guided Clustering and Particle Swarm Optimization for High-Dimensional Data,” IEEE Trans. Cybern., vol. 52, no. 9, pp. 9573–9586, Sep. 2022, doi: 10.1109/TCYB.2021.3061152.

[26] S. Dong, “Multi class SVM algorithm with active learning for network traffic classification,” Expert Syst. Appl., vol. 176, p. 114885, Aug. 2021, doi: 10.1016/j.eswa.2021.114885.

[27] P. Sarang, “Naive Bayes,” in The Springer Series in Applied Machine Learning, 2023, pp. 143–152. doi: 10.1007/978-3-031-02363-7_7

[28] R. Ardianto, T. Rivanie, Y. Alkhalifi, F. S. Nugraha, and W. Gata, “Sentiment Analysis On E-Sports For Education Curriculum Using Naive Bayes And Support Vector Machine,” J. Ilmu Komput. dan Inf., vol. 13, no. 2, pp. 109–122, Jul. 2020, doi: 10.21609/jiki.v13i2.885.

[29] I. Ibrahim and A. Abdulazeez, “The Role of Machine Learning Algorithms for Diagnosing Diseases,” J. Appl. Sci. Technol. Trends, vol. 2, no. 01, pp. 10–19, Mar. 2021, doi: 10.38094/jastt20179.

[30] K. Maswadi, N. A. Ghani, S. Hamid, and M. B. Rasheed, “Human activity classification using Decision Tree and Naïve Bayes classifiers,” Multimed. Tools Appl., vol. 80, no. 14, pp. 21709–21726, Jun. 2021, doi: 10.1007/s11042-020-10447-x.

[31] H. Chen, S. Hu, R. Hua, and X. Zhao, “Improved naive Bayes classification algorithm for traffic risk management,” EURASIP J. Adv. Signal Process., vol. 2021, no. 1, p. 30, Dec. 2021, doi: 10.1186/s13634-021-00742-6.

[32] P. P. Surya and B. Subbulakshmi, “Sentimental Analysis using Naive Bayes Classifier,” in 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), Mar. 2019, pp. 1–5, doi: 10.1109/ViTECoN.2019.8899618.

[33] R. van de Schoot et al., “Bayesian statistics and modelling,” Nat. Rev. Methods Prim., vol. 1, no. 1, p. 1, Jan. 2021, doi: 10.1038/s43586-020-00001-2.

[34] A. B. Shaik and S. Srinivasan, “A Brief Survey on Random Forest Ensembles in Classification Model,” in Lecture Notes in Networks and Systems, Singapore: Springer, 2019, pp. 253–260. doi: 10.1007/978-981-13-2354-6_27

[35] T.-H. Lee, A. Ullah, and R. Wang, “Bootstrap Aggregating and Random Forest,” in Advanced Studies in Theoretical and Applied Econometrics, 2020, pp. 389–429. doi: 10.1007/978-3-030-31150-6_13

[36] Kurniabudi, D. Stiawan, Darmawijoyo, M. Y. Bin Idris, A. M. Bamhdi, and R. Budiarto, “CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection,” IEEE Access, vol. 8, pp. 132911–132921, 2020, doi: 10.1109/ACCESS.2020.3009843.

[37] X. Zhou, P. Lu, Z. Zheng, D. Tolliver, and A. Keramati, “Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree,” Reliab. Eng. Syst. Saf., vol. 200, p. 106931, Aug. 2020, doi: 10.1016/j.ress.2020.106931.

[38] T. Zhu, “Analysis on the Applicability of the Random Forest,” J. Phys. Conf. Ser., vol. 1607, no. 1, p. 012123, Aug. 2020, doi: 10.1088/1742-6596/1607/1/012123.

[39] G. Wang, B. Zhao, B. Wu, C. Zhang, and W. Liu, “Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases,” Int. J. Min. Sci. Technol., vol. 33, no. 1, pp. 47–59, Jan. 2023, doi: 10.1016/j.ijmst.2022.07.002.


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Copyright (c) 2023 Aji Prasetya Wibawa, Adjie Rosyidin, Fitriana Kurniawati, Gwinny Tirza Rarastri, Ilham Ari Elbaith Zaeni, Suyono Suyono, Agung Bella Putra Utama, Felix Andika Dwiyanto

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