Sentiment Analysis on Tabungan Perumahan Rakyat (TAPERA) Program by using Support Vector Machine (SVM)
Abstract
This study aims to analyze public sentiment towards the Housing Savings Program (TAPERA) using the Support Vector Machine (SVM) algorithm. The dataset comprises 16,061 reviews about TAPERA which was gathered from web scrapping and YouTube API. The sentiment analysis results indicate that 99.8% of the reviews are negative, while only 0.2% are positive. The SVM model applied in this study achieved a very high accuracy rate of 99.81%. This indicates that the model is highly effective in classifying sentiments, particularly in identifying negative sentiments. The resulting confusion matrix shows the model's excellent performance in detecting negative sentiments, with no False Positives (FP) and a very high number of True Negatives (TN). However, the model exhibits weaknesses in detecting positive sentiments, as indicated by the presence of several False Negatives (FN) and the absence of True Positives (TP). The findings of this study suggest that the public generally holds a very negative view of the TAPERA program. This insight is crucial for program administrators to consider as they evaluate and improve the program based on negative feedback received from the public. Overall, this research provides important insights into public perceptions of TAPERA and underscores the need for better modeling for more representative sentiment analysis. These findings can serve as a basis for policymakers in designing more effective communication strategies and program improvements to increase public acceptance of TAPERA.
References
[2] N. Tania, J. Novienco, and dan Dixon Sanjaya, ‘Kajian Hukum Progresif Terhadap Implementasi Produk Tabungan Perumahan Rakyat’, Kajian Masalah Hukum dan Pembangunan, 2021, [Online]. Available: www.cnnindonesia.com/nasional/20190903212554-20-427289/
[3] F. Aftab et al., ‘A Comprehensive Survey on Sentiment Analysis Techniques’, International Journal of Technology, vol. 14, no. 6, pp. 1288–1298, 2023, doi: 10.14716/IJTECH.V14I6.6632.
[4] M. Wankhade, A. C. S. Rao, and C. Kulkarni, ‘A survey on sentiment analysis methods, applications, and challenges’, Artificial Intelligence Review 2022 55:7, vol. 55, no. 7, pp. 5731–5780, Feb. 2022, doi: 10.1007/S10462-022-10144-1.
[5] P. Gonçalves, M. Araújo, F. Benevenuto, and M. Cha, ‘Comparing and combining sentiment analysis methods’, COSN 2013 - Proceedings of the 2013 Conference on Online Social Networks, pp. 27–37, 2013, doi: 10.1145/2512938.2512951.
[6] M. F. Fakhrezi, A. F. Rochim, D. Mutiara, and K. Nugraheni, ‘Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article’, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 161–167, Feb. 2023, doi: 10.29207/RESTI.V7I1.4767.
[7] R. N. Handayani, ‘Optimasi Algoritma Support Vector Machine untuk Analisis Sentimen pada Ulasan Produk Tokopedia Menggunakan PSO’, Media Informatika, vol. 20, no. 2, pp. 97–108, Jul. 2021, doi: 10.37595/MEDIAINFO.V20I2.59.
[8] A. Shmilovici, ‘Support Vector Machines’, Data Mining and Knowledge Discovery Handbook, pp. 257–276, 2005, doi: 10.1007/0-387-25465-X_12.
[9] R. Burbidge and B. Buxton, ‘An introduction to support vector machines for data mining’, 2001.
[10] S. Huang, C. A. I. Nianguang, P. Penzuti Pacheco, S. Narandes, Y. Wang, and X. U. Wayne, ‘Applications of Support Vector Machine (SVM) Learning in Cancer Genomics’, Cancer Genomics Proteomics, vol. 15, no. 1, p. 41, Jan. 2018, doi: 10.21873/CGP.20063.
[11] N. H. Ovirianti, M. Zarlis, and H. Mawengkang, ‘Support Vector Machine Using A Classification Algorithm’, Sinkron : jurnal dan penelitian teknik informatika, vol. 6, no. 3, pp. 2103–2107, Aug. 2022, doi: 10.33395/SINKRON.V7I3.11597.
[12] Z. Zhang, Z. Liu, and C. Qiao, ‘Tendency Mining in Dynamic Association Rules Based on SVM Classifier’, 2014.
[13] A. Mat Deris, A. Mohd Zain, and R. Sallehuddin, ‘Overview of Support Vector Machine in Modeling Machining Performances’, Procedia Eng, vol. 24, pp. 308–312, Jan. 2011, doi: 10.1016/J.PROENG.2011.11.2647.
[14] W. Chu, S. S. Keerthi, and C. J. Ong, ‘A general formulation for support vector machines’, ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age, vol. 5, pp. 2522–2526, 2002, doi: 10.1109/ICONIP.2002.1201949.
[15] O. Devos, G. Downey, and L. Duponchel, ‘Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils’, Food Chem, vol. 148, pp. 124–130, 2014, doi: 10.1016/J.FOODCHEM.2013.10.020.
[16] Y. Qi and Z. Shabrina, ‘Sentiment analysis using Twitter data: a comparative application of lexicon- and machine-learning-based approach’, Soc Netw Anal Min, vol. 13, no. 1, Dec. 2023, doi: 10.1007/S13278-023-01030-X.
[17] P. Kurniawati, R. Y. Fa’rifah, and D. Witarsyah, ‘Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm’, Building of Informatics, Technology and Science (BITS), vol. 5, no. 2, Sep. 2023, doi: 10.47065/bits.v5i2.4265.
[18] M. P. Dwi Cahyo, Widodo, and B. Prasetya Adhi, ‘Kinerja Algoritma Support Vector Machine dalam MenentukanKebenaran Informasi Banjir di Twitter’, PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer, vol. 3, no. 2, pp. 116–121, Dec. 2019, doi: 10.21009/pinter.3.2.5.
[19] J. Friadi, and D. E. Kurniawan, "Analisis Sentimen Ulasan Wisatawan Terhadap Alun-Alun Kota Batam: Perbandingan Kinerja Metode Naive Bayes dan Support Vector Machine," Jurnal Sistem Informasi Bisnis, vol. 14, no. 4, pp. 403-407, Oct. 2024. https://doi.org/10.21456/vol14iss4pp403-407
[20] D. Vonega, A. Fadila, and D. Kurniawan, “Analisis Sentimen Twitter Terhadap Opini Publik Atas Isu Pencalonan Puan Maharani dalam PILPRES 2024”, JAIC, vol. 6, no. 2, pp. 129-135, Nov. 2022.
[21] M. Narvekar and S. F. Syed, ‘An optimized algorithm for association rule mining using FP tree’, in Procedia Computer Science, Elsevier B.V., 2015, pp. 101–110. doi: 10.1016/j.procs.2015.03.097.
[22] F. O. Widarta and R. A. Syahputra, ‘The application of machine learning algorithms for assessing the maturity level of palm fruits as the prominent commodity in the Western-Southern Area of Aceh’, Operations Excellence: Journal of Applied Industrial Engineering, vol. 16, no. 1, pp. 56–63, Jun. 2024, doi: 10.22441/OE.2024.V16.I1.102.
[23] D. D. Soeprapto, ‘SWOT analysis of BP. Tapera: A public housing savings implementing agency in Indonesia’, International Journal of Research in Business and Social Science (2147- 4478), vol. 9, no. 6, pp. 230–243, Oct. 2020, doi: 10.20525/IJRBS.V9I6.900.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Penulis yang telah mempublikasikan artikel pada JAIC menyatakan setuju bahwa:
1. Artikel belum dan tidak pernah dipublikasikan sebelumnya pada jurnal ilmiah lain, prosiding ataupun jurnal elektronik lainnya.
2. Artikel yang telah diserahkan menjadi hak penuh kepada pengelola JAIC Politeknik Negeri Batam
3. Artikel diperbolehkan untuk dishare ke khalayak untuk meningkatkan produktivitas rujukan dan sitasi dari naskah yang telah terbit.
