DOI: https://doi.org/10.14710/jati.20.1.11-20

TEXTBLOB-BASED SENTIMENT ANALYSIS OF TABUNGAN PERUMAHAN RAKYAT (TAPERA) POLICY: A PUBLIC PERCEPTION STUDY

*Muhammad Iqbal Faturohman orcid scopus  -  Study Program of Industrial Engineering, Telkom University, Indonesia
Miftahol Arifin scopus  -  Study Program of Logistic Engineering, Telkom University, Indonesia

Citation Format:
Abstract
TAPERA (Tabungan Perumahan Rakyat) has generated considerable debate in Indonesia. Intended to assist low-income communities with affordable housing, the program has faced criticism due to policy inconsistencies and mandatory monthly contributions, which some argue impose significant financial burdens on participants. Concerns about the transparency and accountability of fund management further complicate its reception. This study utilizes sentiment analysis with the TextBlob library within Google Colab to evaluate public opinions on TAPERA. The research involved collecting data from YouTube comments via the API, followed by data preprocessing through six stages: Data Cleaning, Stopword Removal, Tokenization, Normalization, Stemming, and Translation. Sentiments were then analyzed using TextBlob, with the final dataset comprising 2,228 comments. The sentiment analysis revealed that 76% of the comments were negative, 23% positive, and 1% neutral. The predominance of negative sentiment indicates widespread dissatisfaction, suggesting issues with TAPERA’s effectiveness, coverage, and implementation. Negative feedback reflects broader concerns about transparency and communication. These findings highlight the need for continuous evaluation and transparent communication to enhance TAPERA’s effectiveness and address public concerns. Sentiment analysis proves to be a valuable tool for gauging public perception and guiding policy refinements, ensuring that the program aligns more closely with community needs and expectations.
Fulltext View|Download
Keywords: Sentiment Analysis; Natural Language Processing; Text Mining; TextBlob; TAPERA

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
KONSEP FLEKSIBILITAS DALAM GIG WORKER DAN PENGARUHNYA PADA KINERJA PERUSAHAAN JASA: LITERATURE REVIEW TEXTBLOB-BASED SENTIMENT ANALYSIS OF TABUNGAN PERUMAHAN RAKYAT (TAPERA) POLICY: A PUBLIC PERCEPTION STUDY PENGEMBANGAN PRODUK PANGAN MELALUI BAHAN ALTERNATIF DAN PENGAYAAN KANDUNGAN GIZI More recent articles
Most cited articles
RANCANG BANGUN SISTEM INFORMASI MANAJEMEN ASET BERBASIS WEB UNTUK OPTIMALISASI PENELUSURAN ASET DI TEKNIK INDUSTRI UNDIP ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN VIA INTERNET PADA TOKO ONLINE MODEL KONSEPTUAL UNTUK MENGUKUR ADAPTABILITAS BANK SAMPAH DI INDONESIA SIMULASI MODEL DINAMIK PENGANGKUTAN CRUDE PALM OIL (CPO) DI PT. XYZ UNTUK MEMINIMALKAN BIAYA TRANSPORTASI PENGADAAN BAHAN IDENTIFIKASI KEPENTINGAN RELATIF KONSUMEN TERHADAP ROBOT REHABILITASI PASIEN PASCA STROKE DENGAN MENGGUNAKAN CONJOINT ANALYSIS More cited articles
  1. Aresta, R. (2018). The Influence of Translation Techniques on the Accuracy and Acceptability of Translated Utterances that Flout the Maxim of Quality. Jurnal Humaniora, 30(2), 176. https://doi.org/10.22146/jh.v30i2.33645
  2. Bagheri, A., Giachanou, A., Mosteiro, P., & Verberne, S. (2023). Natural Language Processing and Text Mining (Turning Unstructured Data into Structured) BT - Clinical Applications of Artificial Intelligence in Real-World Data (F. W. Asselbergs, S. Denaxas, D. L. Oberski, & J. H. Moore (eds.); pp. 69–93). Springer International Publishing. https://doi.org/10.1007/978-3-031-36678-9_5
  3. Baidarus, M., Febriano, D., Mubarok, D. A., & Ramadhani, M. A. (2023). Kajian Sistematis Kebijakan Skema Pembiayaan Kerja Sama Pemerintah Dengan Badan Usaha (Kpbu) Pada Sektor Perumahan Guna Mengatasi Backlog Di Indonesia. Jurnal BPPK: Badan Pendidikan Dan Pelatihan Keuangan, 16(1), 1–13. https://doi.org/10.48108/jurnalbppk.v16i1.711
  4. Bharadwaj, Lakshay (2023). Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification. International Journal For Multidisciplinary Research, 5(5), 1–34. https://doi.org/10.36948/ijfmr.2023.v05i05.6090
  5. Choudhary, K., & Kelley, M. L. (2023). ChemNLP: A Natural Language-Processing-Based Library for Materials Chemistry Text Data. Journal of Physical Chemistry C, 127(35), 17545–17555. https://doi.org/10.1021/acs.jpcc.3c03106
  6. ChandraKala, S & Sindhu, C. (2012). Opinion Mining and Sentiment Classification: a Survey. ICTACT Journal on Soft Computing, 03(01), 420–427. https://doi.org/10.21917/ijsc.2012.0065
  7. Dallo, K. A. M. (2023). Natural language processing for business analytics. Advances in Engineering Innovation, 3(1), None-None. https://doi.org/10.54254/2977-3903/3/2023038
  8. De Filippi, F., & Cocina, G. G. (2022). Digital Participatory Platforms: Conclusions and the Way Forward BT - Urban Regeneration and Community Empowerment Through ICTs: A Focus on Digital Participatory Platforms (DPPs) (F. De Filippi & G. G. Cocina (eds.); pp. 121–138). Springer International Publishing. https://doi.org/10.1007/978-3-030-97755-9_5
  9. Deviyani, A., & Black, A. W. (2022). Text Normalization for Speech Systems for All Languages. 20–25. https://doi.org/10.21437/s4sg.2022-5
  10. Elgendy, N., & Elragal, A. (2014). Big data analytics: A literature review paper. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8557 LNAI, 214–227. https://doi.org/10.1007/978-3-319-08976-8_16
  11. Fakhruzzaman, M. N., Jannah, S. Z., Gunawan, S. W., Pratama, A. I., & Ardanty, D. A. (2024). IndoPolicyStats: sentiment analyzer for public policy issues. Bulletin of Electrical Engineering and Informatics, 13(1), 482–489. https://doi.org/10.11591/eei.v13i1.5263
  12. Fan, Y., Arora, C., & Treude, C. (2023). Stop Words for Processing Software Engineering Documents: Do they Matter? Proceedings - 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering, NLBSE 2023, 40–47. https://doi.org/10.1109/NLBSE59153.2023.00016
  13. Feng, K. J. K., Gao, A., & Karras, J. S. (2022). Towards Semantically Aware Word Cloud Shape Generation. UIST 2022 Adjunct - Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. https://doi.org/10.1145/3526114.3558724
  14. Ganesha Putra Henriko , Fahmi Erwin, T. K. (2020). DI DKI JAKARTA Tabungan Perumahan Rakyat ( Tapera ) sesuai amanat Undang-Undang Republik Indonesia pembiayaan untuk memperoleh hunian . Namun , sejauhmana Tapera dapat diaplikasikan secara. 3(2), 321–332
  15. Goebel, R. (2019). PRICAI 2019 : Trends in Series Editors. In Pricai (Vol. 2, Issue 61672281). https://doi.org/10.1007/978-3-030-29894-4
  16. Gudankwar, A., Pratik Milind Mendhe, Lukesh Namdeo Oghare, & Atharva Ramu Yemde. (2024). Sentiments of Public Opinion. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(2), 459–461. https://doi.org/10.32628/cseit2410239
  17. Hasan, M., Ahmed, T., & Uddin, M. P. (2023). Leveraging Textual Information for Social Media News Categorization and Sentiment Analysis. https://doi.org/10.2139/ssrn.4425901
  18. Humphreys, L., Lewis, N. A., Sender, K., & Won, A. S. (2021). Integrating Qualitative Methods and Open Science: Five Principles for More Trustworthy Research*. Journal of Communication, 71, 855–874. https://doi.org/10.1093/joc/jqab026
  19. Indrani P, Janaki P, Gayathri P, Chandrahasini P, Charitha Reddy, Apparao G, Rajeshwari. (2024). Product Review Sentiment Analysis. International Journal For Multidisciplinary Research, 6(3), 1–8. https://doi.org/10.36948/ijfmr.2024.v06i03.20551
  20. Jayachandran, S. (2023). The inherent trade-off between the environmental and anti-poverty goals of payments for ecosystem services. Environmental Research Letters, 18(2). https://doi.org/10.1088/1748-9326/acb1a7
  21. Kampatzis, A., Sidiropoulos, A., Diamantaras, K., & Ougiaroglou, S. (2024). Sentiment Dimensions and Intentions in Scientific Analysis: Multilevel Classification in Text and Citations. Electronics (Switzerland), 13(9). https://doi.org/10.3390/electronics13091753
  22. Kucukyilmaz, T., & Akin, T. (2024). A Feature-based Approach on Automatic Stopword Detection. Lecture Notes in Networks and Systems, 825, 51–67. https://doi.org/10.1007/978-3-031-47718-8_4
  23. Kunilovskaya, M., & Plum, A. (2021). Text Preprocessing and its Implications in a Digital Humanities Project. International Conference Recent Advances in Natural Language Processing, RANLP, 2021-Septe, 85–93. https://doi.org/10.26615/issn.2603-2821.2021_013
  24. Lindgren, C. J., Wang, W., Upadhyay, S. K., & Kobayashi, V. B. (2023). Sentiment Analysis for Organizational Research. Research in Occupational Stress and Well Being, 21(November 2023), 95–117. https://doi.org/10.1108/S1479-355520230000021006
  25. Lippman, S. A., Libby, M. K., Nakphong, M. K., Arons, A., Balanoff, M., Mocello, A. R., Arnold, E. A., Shade, S. B., Qurashi, F., Downing, A., Moore, A., Dow, W. H., & Lightfoot, M. A. (2023). A guaranteed income intervention to improve the health and financial well-being of low-income black emerging adults: study protocol for the Black Economic Equity Movement randomized controlled crossover trial. Frontiers in Public Health, 11(November). https://doi.org/10.3389/fpubh.2023.1271194
  26. Liu, H., Tsang, S., Wood, A., & Tong, X. (2024). Longitudinal Sentiment Analysis with Conversation Textual Data. Fudan Journal of the Humanities and Social Sciences, 0123456789. https://doi.org/10.1007/s40647-024-00417-0
  27. Lohmann, S., Heimerl, F., Bopp, F., Burch, M., & Ertl, T. (2015). Concentri cloud: Word cloud visualization for multiple text documents. Proceedings of the International Conference on Information Visualisation, 2015-Septe, 114–120. https://doi.org/10.1109/iV.2015.30
  28. Saha, S., Showrov, M. I. H., Rahman, M. M., & Majumder, M. Z. H. (2023). VADER vs. BERT: A Comparative Performance Analysis for Sentiment on Coronavirus Outbreak BT - Machine Intelligence and Emerging Technologies (M. S. Satu, M. A. Moni, M. S. Kaiser, & M. S. Arefin (eds.); pp. 371–385). Springer Nature Switzerland
  29. Singh, J., & Gupta, V. (2017). A systematic review of text stemming techniques. Artificial Intelligence Review, 48. https://doi.org/10.1007/s10462-016-9498-2
  30. Singh, N. T., Mishra, A. M., Singh, A., & Chanu, A. D. (2023). Advancing Spelling Correction through Natural Language Processing and TextBlob: A Context-Aware Approach. 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023, March, 1–6. https://doi.org/10.1109/IC-RVITM60032.2023.10435139
  31. Soeprapto, D. D. (2021). Determining the key success factors in the organization of BP Tapera. International Journal of Research in Business and Social Science (2147- 4478), 10(6), 42–55. https://doi.org/10.20525/ijrbs.v10i6.1270
  32. Srivastava, C., Pratishtha, ;, & Kaur, N. (2022). An Overview on Data Cleaning on Real World Data. https://doi.org/10.36227/techrxiv.21064039.v1
  33. Thakur, M., Arora, A. M., Sandhu, Y. S., & Singh, G. (2024). Sentimental Journeys: Novel Insight into Textual Information. Advancements in Communication and Systems, 305–313. https://doi.org/10.56155/978-81-955020-7-3-26
  34. Udayana, I. P. A. E. D., Indrawan, I. G. A., & Putra, I. P. D. G. A. (2023). Decision Support System for Sentiment Analysis of Youtube Comments on Government Policies. Journal of Computer Networks, Architecture and High Performance Computing, 5(1), 27–37. https://doi.org/10.47709/cnahpc.v5i1.1999
  35. Wang, C., Qiu, M., Zhang, T., Liu, T., Li, L., Wang, J., Wang, M., Huang, J., & Lin, W. (2022). EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing. EMNLP 2022 - 2022 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Demonstrations Session, 2, 22–29. https://doi.org/10.18653/v1/2022.emnlp-demos.3
  36. Wang, S., Zhuang, S., & Zuccon, G. (2024). Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures. 2492–2496. https://doi.org/10.1145/3626772.3657949
  37. Yang, J. (2024). Rethinking Tokenization: Crafting Better Tokenizers for Large Language Models. https://doi.org/10.1075/ijchl.00023.yan
  38. Zhang, H., Cheng, Y.-C., Kumar, S., Huang, W. R., Chen, M., & Mathews, R. (2022). Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model. 2–6. http://arxiv.org/abs/2202.08171

Last update:

No citation recorded.

Last update: 2025-02-23 05:35:12

No citation recorded.