EVALUASI IMPLEMENTASI CORETAX MENGGUNAKAN TRIANGULASI KUANTITATIF BERBASIS KUISIONER DAN ANALISIS SENTIMEN INDOBERT
DOI:
https://doi.org/10.47491/landjournal.v7i2.4754Keywords:
CoreTax, sentiment analysis, IndoBERT, model accuracy, quantitative triangulationAbstract
The digitalization of Indonesia’s tax administration through the CoreTax Administration System represents a strategic effort by the Directorate General of Taxes to enhance operational efficiency and taxpayer compliance. However, early implementation has faced various technical and administrative challenges, including server instability, longer processing times, and additional overtime for accountants and tax consultants. This study evaluates the effectiveness of CoreTax using a quantitative triangulation approach that integrates questionnaire data with IndoBERT-based sentiment analysis of 10,084 public tweets collected between December 2024 and March 2025. The Wilcoxon signed-rank test indicates a significant decrease in SPT reporting efficiency and an increase in overtime during the transition phase. Sentiment analysis results show a dominant 64.7% negative sentiment that gradually declined over time, reflecting users’ adaptation to the new system. The IndoBERT classification model achieved 91.7% accuracy and a 0.903 macro-F1 score, confirming strong model reliability. Integration of both datasets reveals a consistent interpretation that CoreTax performance improves after initial adjustment. These findings highlight the importance of system quality enhancement and continuous sentiment monitoring to ensure sustainable digital tax adoption in Indonesia.
References
M. Y. Mahadianto, P. Maulady, and S. Yani, “Interpretation of Tax Payment Mechanism in Coretax System on Compliance (Case of KPP Pratama Cirebon),” iRASD Journal of Management, vol. 7, no. 1, pp. 1–9, Mar. 2025, doi: 10.52131/irasd-jom.2025.v7i1.2777.
P. B. Saptono et al., “Quality of E-Tax System and Tax Compliance Intention: The Mediating Role of User Satisfaction,” Informatics, vol. 10, no. 1, 2023, doi: 10.3390/informatics10010022.
S. Hesami, H. Jenkins, and G. Jenkins, “Digital Transformation of Tax Administration and Compliance: A Systematic Literature Review on E-Invoicing and Prefilled Returns,” Digital Government: Research and Practice, vol. 5, Oct. 2024, doi: 10.1145/3643687.
weiqun xi and X. Ling, “Can the digitalization of tax administration promote corporate digital transformation?—— empirical evidence from China,” International Review of Economics & Finance, vol. 104, p. 104634, 2025, doi: https://doi.org/10.1016/j.iref.2025.104634.
K. Mbise and L. Baseka, “The Impact of the Digital Tax Administration System on Compliance Among SMEs,” The Journal of Informatics, vol. 2, pp. 69–82, Oct. 2022, doi: 10.59645/tji.v2i1.94.
I. N. Darmayasa and N. S. Hardika, “Core tax administration system: the power and trust dimensions of slippery slope framework tax compliance model,” Cogent Business & Management, vol. 11, no. 1, p. 2337358, 2024, doi: 10.1080/23311975.2024.2337358.
E. Bassey, E. Mulligan, and A. Ojo, “A conceptual framework for digital tax administration - A systematic review,” Gov. Inf. Q., vol. 39, no. 4, p. 101754, 2022, doi: https://doi.org/10.1016/j.giq.2022.101754.
C.-L. Cardenas-López, “Digital tax administration systems: A literature review in Scopus (2012-2022),” Revista Científica de Sistemas e Informática, vol. 3, no. 2, p. e525, Jul. 2023, doi: 10.51252/rcsi.v3i2.525.
M. Nose and A. Mengistu, “Exploring the Adoption of Selected Digital Technologies in Tax Administration: A Cross-Country Perspective,” IMF Notes, vol. 2023, no. 008, p. A001, 2023, doi: 10.5089/9798400258183.068.A001.
S. Murnidayanti and T. Putranti, “The Effectiveness of Digitizing Tax Administration to Reduce the Compliance Cost of Taxpayers of Micro, Small, and Medium Enterprises (MSMEs),” Jurnal Public Policy, vol. 9, p. 91, Oct. 2023, doi: 10.35308/jpp.v9i2.6561.
Y. He, “Digital Technology Reshapes the Tax Collection and Administration System: Impacts, Challenges and Future Prospects,” Scientific Journal of Economics and Management Research, vol. 6, pp. 85–91, Oct. 2024, doi: 10.54691/9g1jdp53.
S. A. Putri and C. I. Ratnasari, “Measuring e-Filing Adoption as an e-Government Service Using the Technology Acceptance Model,” bit-Tech, vol. 8, no. 1, pp. 988–997, Aug. 2025, doi: 10.32877/bt.v8i1.2804.
P. Heydarian, A. Bifet, and S. Corbet, “Understanding market sentiment analysis: A survey,” J. Econ. Surv., vol. 39, no. 3, pp. 1125–1147, 2025, doi: https://doi.org/10.1111/joes.12645.
A. Faccia, J. McDonald, and B. George, “NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Keeping,” Computers, vol. 13, no. 1, Dec. 2023, doi: 10.3390/computers13010005.
G. Fatouros, J. Soldatos, K. Kouroumali, G. Makridis, and D. Kyriazis, “Transforming sentiment analysis in the financial domain with ChatGPT,” Machine Learning with Applications, vol. 14, p. 100508, 2023, doi: https://doi.org/10.1016/j.mlwa.2023.100508.
A. Yadav, C. K. Jha, A. Sharan, and V. Vaish, “Sentiment analysis of financial news using unsupervised approach,” Procedia Comput. Sci., vol. 167, pp. 589–598, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.325.
F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, vol. 13, no. 3, pp. 319–340, 1989, doi: 10.2307/249008.
A. H. Hermanto, N. A. Windasari, and M. S. Purwanegara, “Taxpayers’ adoption of online tax return reporting: extended meta-UTAUT model perspective,” Cogent Business & Management, vol. 9, no. 1, p. 2110724, 2022, doi: 10.1080/23311975.2022.2110724.
W. H. DeLone and E. R. McLean, “The DeLone and McLean Model of Information Systems Success: A Ten-Year Update,” Journal of Management Information Systems, vol. 19, no. 4, pp. 9–30, 2003, doi: 10.1080/07421222.2003.11045748.
A. Jeyaraj, “DeLone & McLean models of information system success: Critical meta-review and research directions,” Int. J. Inf. Manage., vol. 54, p. 102139, 2020, doi: https://doi.org/10.1016/j.ijinfomgt.2020.102139.
Z. Mi and H. Zhan, “Text Mining Attitudes toward Climate Change: Emotion and Sentiment Analysis of the Twitter Corpus,” Weather, Climate, and Society, vol. 15, no. 2, pp. 277–287, 2023, doi: 10.1175/WCAS-D-22-0123.1.
M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P.-M. Cuenca-Jiménez, “A review on sentiment analysis from social media platforms,” Expert Syst. Appl., vol. 223, p. 119862, 2023, doi: https://doi.org/10.1016/j.eswa.2023.119862.
R. F. Prasetyaningrum and Z. Ekastuti, “Evaluasi Kualitas Aplikasi DJP Online Terhadap Kepuasan Pengguna Dengan Metode EUCS Dan IPA,” Jurnal Aplikasi Perpajakan, vol. 6, no. 1, pp. 283–300, 2025, doi: 10.29303/jap.v6i1.128.
I. Adu, Sudarwati, and I. DPW, “Kualitas Layanan Elektronik DJP Online Terhadap Kepuasan Pengguna Dengan Mediasi Kepercayaan,” Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah, vol. 7, Oct. 2025, doi: 10.47467/alkharaj.v7i3.7036.
D. Sekti Ari and L. Hanum, “PENGARUH KUALITAS PELAYANAN WEBSITE DJP TERHADAP KEPUASAN PENGGUNA DENGAN MODIFIKASI E GOVQUAL,” Profit, vol. 15, pp. 104–111, Oct. 2021, doi: 10.21776/ub.profit.2021.015.01.11.
H. Wang and X. Wang, “Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming,” Global Media and China, vol. 8, no. 2, pp. 213–233, 2023, doi: 10.1177/20594364231181745.
R. Riyanto and A. Azis, “Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia,” Journal of Applied Data Sciences, vol. 2, no. 3, pp. 102–108, 2021, doi: 10.47738/jads.v2i3.40.
M. Armanda and F. A. T. Tobing, “Implementation of Support Vector Machine Method for Twitter Sentiment Analysis Related to Cancellation of u-20 World Cup in Indonesia,” IJNMT (International Journal of New Media Technology), vol. 11, no. 1, pp. 27–34, Jul. 2024, doi: 10.31937/ijnmt.v11i1.3673.
J. G. Pinem et al., “Sentiment Analysis of Indonesian New Capitol (IKN) Tweets by Stacked Generalization of Deep Learning,” in Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications, in IC3INA ’22. New York, NY, USA: Association for Computing Machinery, 2023, pp. 204–210. doi: 10.1145/3575882.3575922.
N. K. Denzin, The Research Act: A Theoretical Introduction to Sociological Methods, 2nd ed. New York: McGraw-Hill, 1978.
U. Flick, An Introduction to Qualitative Research, 6th ed. London: Sage Publications, 2018. doi: 10.4135/9781529716641.
M. Hadler, B. Klösch, M. Reiter-Haas, and E. Lex, “Combining Survey and Social Media Data: Respondents’ Opinions on COVID-19 Measures and Their Willingness to Provide Their Social Media Account Information,” Frontiers in Sociology, vol. Volume 7-2022, 2022, doi: 10.3389/fsoc.2022.885784.
F. Knappertsbusch, M. Schreier, N. Burzan, and N. Fielding, “Innovative Applications and Future Directions in Mixed Methods and Multimethod Social Research,” Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, vol. 24, no. 1, Jan. 2023, doi: 10.17169/fqs-24.1.4013.
K. T. Des Ryantini, Y. Putra Semadi, and L. P. E. Damayanthi, “Penguatan Literasi Perpajakan Melalui MPK Bahasa Indonesia sebagai Implementasi Karakter Nasionalis,” Mimbar Ilmu, vol. 27, no. 3, pp. 493–500, Dec. 2022, doi: 10.23887/mi.v27i3.51410.
S. D. P. N. Tyas and A. Prasetyo, “Penerapan Sistem Informasi Akuntansi Penjualan dengan Software Accurate untuk Meningkatkan Efisiensi Proses Penjualan: Studi Kasus PT. Karya Mitra Tehnik,” Khatulistiwa: Jurnal Pendidikan dan Sosial Humaniora, vol. 5, no. 3, pp. 986–997, Jul. 2025, doi: 10.55606/khatulistiwa.v5i3.7300.
E. D. Madyatmadja, J. Ricardo, B. T. Tanto, and R. Yuristo, “Dataset of ’Challenges and Perceptions of Coretax: A Sentiment Analysis Approach’ ,” Jun. 2025, Zenodo. doi: 10.5281/zenodo.15586791.
