ANALISIS PENERIMAAN AI MENGGUNAKAN PLS-SEM DAN MACHINE LEARNING PADA GURU SMA NEGERI 1 BANJARHARJO BREBES

Dina Mariani1* Hari Tri Wibowo2
(1) Universitas Bima Sakapenta
(2) Universitas Bima Sakapenta
(*) Corresponding Author

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi penerimaan Artificial Intelligence (AI) pada guru SMA Negeri 1 Banjarharjo dengan menggunakan pendekatan Technology Acceptance Model (TAM) yang dikombinasikan dengan PLS-SEM dan machine learning. Konstruk yang diuji meliputi persepsi kemudahan, persepsi kegunaan, minat penggunaan AI, dan penggunaan AI. Metode penelitian menggunakan kuantitatif (kuesioner) dan dianalisis menggunakan SmartPLS4 untuk menguji hubungan kausal antarkonstruk, serta Random Forest dan XGBoost untuk mengevaluasi kemampuan prediktif dan mengidentifikasi feature importance. Sampel penelitian berjumlah 59 responden. Hasil PLS-SEM menunjukkan bahwa persepsi kemudahan tidak berpengaruh terhadap minat penggunaan AI dan berpengaruh terhadap penggunaan AI. Persepsi kegunaan berpengaruh terhadap minat penggunaan AI dan tidak berpengaruh terhadap minat penggunaan AI. Minat penggunaan AI tidak berpengaruh terhadap penggunaan AI. Analisis machine learning menunjukkan bahwa persepsi kegunaan merupakan prediktor dominan terhadap minat penggunaan, sedangkan persepsi kemudahan lebih dominan dalam memprediksi penggunaan AI. Model Random Forest menunjukkan performa prediksi yang lebih baik dibandingkan XGBoost.

References

Alasgarova, R., & Rzayev, J. (2025). The Implications of Artificial Intelligence for Teacher Agency and Teacher-Student Relationships through the Technology Acceptance Model. International Journal of Technology in Education and Science, 9(3), 450–473. https://doi.org/10.46328/ijtes.645

Belmonte, Z. J. A., Prasetyo, Y. T., Cahigas, M. M. L., Nadlifatin, R., & Gumasing, M. J. J. (2024). Factors influencing the intention to use e-wallet among generation Z and millennials in the Philippines: An extended technology acceptance model (TAM) approach. Acta Psychologica, 250. https://doi.org/10.1016/j.actpsy.2024.104526

Bishop, C. M. (2006). Pattern Recognition And Machine Learning. Springer.

Breiman, L. (2001). Random Forests (Vol. 45).

Cervantes, J., & Navarro, E. (2025). Business Students’ Perceptions of AI in Higher Education: An Analysis Using the Technology Acceptance Model. Journal of Interdisciplinary Perspectives, 3(6). https://doi.org/10.69569/jip.2025.194

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016, 785–794. https://doi.org/10.1145/2939672.2939785

Chen, X., Jiang, L., Zhou, Z., & Li, D. (2025). Impact of perceived ease of use and perceived usefulness of humanoid robots on students’ intention to use. Acta Psychologica, 258. https://doi.org/10.1016/j.actpsy.2025.105217

Ching, K. W., & Jamaludin, K. A. (2025). Understanding School Teachers’ Acceptance of AI in Education: Insights from the Technology Acceptance Model (TAM). International Journal of Academic Research in Progressive Education and Development, 14(3). https://doi.org/10.6007/ijarped/v14-i3/25196

Darayseh, A. Al. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2023.100132

Davis, F. D. (1989). Perceived Usefulness , Perceived Ease of Use , and User Acceptance of lnformation Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

Fauzan, M., Arisandy, D., Putra Sembiring, E., Wijaya, H. A., & Yusuf, M. (2025). Behavioral Intention Analysis of AI Use in Academic Writing: Implementing the UTAUT Model among English Education Students in Jambi. Indonesian Educational Administration and Leadership Journal, 07, 2. https://doi.org/10.22437/ideal.v7i2.51029

Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O’Reilly Media, Incorporated.

Gupta, K. P. (2024). Understanding teachers’ intentions and use of AI tools for research. Journal of E-Learning and Knowledge Society, 20(2), 13–25. https://doi.org/10.20368/1971-8829/1135969

Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications Ltd.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hasri, R. A., & Miranda, E. (2025). Analyzing ChatGPT’s Impact on Graduates’ Communication, Collaboration, and Logical Thinking Skills Using an Extended Technology Acceptance Model. Jurnal Teknik Informatika (Jutif), 6(4), 2207–2222. https://doi.org/10.52436/1.jutif.2025.6.4.4688

Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Husna, N. L., Cahyaningsih, S., & Kumar, A. M. (2025). Pengaruh Perceived Usefulness Dan Perceived Ease Of Use Terhadap Actual Usage Melalui Attitude Toward Using Dalam Digitalisasi Sistem Perpajakan Umkm. Jurnal Ilmiah Fokus Ekonomi, Manajemen, Bisnis Dan Akuntansi, 4(2), 257–274.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Springer.

Jasin, M. (2022). The effect of perceived ease of use on behavior intention through perceived enjoyment as an intervening variable on digital payment in the digital era. Journal of Industrial Engineering & Management Research, 3(5), 127–133. https://doi.org/10.7777/jiemar

Jeyaraj, A. (2021). Rethinking the intention to behavior link in information technology use: Critical review and research directions. International Journal of Information Management, 59, 1–12. https://doi.org/10.1016/j.ijinfomgt.2021.102345

Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100328

Li, K. (2023). Determinants of College Students’ Actual Use of AI-Based Systems: An Extension of the Technology Acceptance Model. Sustainability (Switzerland) , 15(6). https://doi.org/10.3390/su15065221

Lin, T., Zhang, J., & Xiong, B. (2025). Effects of Technology Perceptions, Teacher Beliefs, and AI Literacy on AI Technology Adoption in Sustainable Mathematics Education. Sustainability (Switzerland), 17(8). https://doi.org/10.3390/su17083698

Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2021). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512–520. https://doi.org/10.1016/j.procs.2021.12.168

Ng, L., Osborne, S., Eley, R., Tuckett, A., & Walker, J. (2024). Exploring nursing students’ perceptions on usefulness, ease of use, and acceptability of using a simulated Electronic Medical Record: A descriptive study. Collegian, 31(2), 120–127. https://doi.org/10.1016/j.colegn.2023.12.006

Park, J. H., Lee, C. W., & Do, C. (2025). Examining Users’ Acceptance Intention of Health Applications Based on the Technology Acceptance Model. Healthcare, 13(6), 596. https://doi.org/10.3390/healthcare13060596

Rahmawati, R. N., & Narsa, I. M. (2019). Penggunaan e-learning dengan Technology Acceptance Model (TAM). Jurnal Inovasi Teknologi Pendidikan, 6(2), 127–136. https://doi.org/10.21831/jitp.v6i2.26232

Richter, N. F., & Tudoran, A. A. (2024). Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms. Journal of Business Research, 173. https://doi.org/10.1016/j.jbusres.2023.114453

Runge, I., Hebibi, F., & Lazarides, R. (2025). Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model. Education Sciences, 15(17), 1–17. https://doi.org/10.3390/educsci15020167

Salsabilla Wijayanti, A., Faroqi, A., & Ridwandono, D. (2025). Peran Kepercayaan dalam Kesadaran, Penerimaan, dan Adopsi Teknologi AI di Pendidikan Tinggi: Analisis dengan Model TAM Modifikasi. Jurnal Pendidikan Dan Teknologi Indonesia (JPTI), 5(4), 1175–1191. https://doi.org/10.52436/1.jpti.784

Sharma, V., Jangir, K., Gupta, M., & Rupeika-Apoga, R. (2024). Does service quality matter in FinTech payment services? An integrated SERVQUAL and TAM approach. International Journal of Information Management Data Insights, 4(2). https://doi.org/10.1016/j.jjimei.2024.100252

Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310. https://doi.org/10.1214/10-STS330

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

Tunca, B. (2025). Hybrid Use Of Structural Equation Modeling And Machine Learning: Literature Review And Future Potential. Structural Equation Modelling And Multivariate Research (SMMR), 2(1), 1–23. https://doi.org/10.5281/zenodo.15740696

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model : Four Longitudinal Field Studies. Management Science Publication, 46(2), 186–204. https://doi.org/http://dx.doi.org/10.1287/mnsc.46.2.186.11926 Full

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.1017/CBO9781107415324.004

Villaceran, E. C., & Himang, C. M. (2025). Data on behavioural intention to use AI copilot through TAM and AI ecological education policy lens. Data in Brief, 61, 1–11. https://doi.org/10.1016/j.dib.2025.111686

Wibowo, H. M., Maghfiroh, I. S. E., & Rokhmawati, R. I. (2017). Analisis Faktor-Faktor Yang Memengaruhi Intention To Use Dan Actual Usage Dalam Penggunaan Aplikasi Beam Di Lingkungan Universitas Brawijaya. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 1(1), 2548–2964. http://j-ptiik.ub.ac.id

Yue, M., Jong, M. S. Y., & Ng, D. T. K. (2024). Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies, 29(15), 19505–19536. https://doi.org/10.1007/s10639-024-12621-2

Zuo, J. (2025). Artificial Intelligence in Education: A Review of Recent Developments and Emerging Trends. Scientific Journal of Intelligent Systems Research , 7(10). https://doi.org/https://doi.org/10.54691/anywv286



DOI: https://doi.org/10.53514/ir.v10i1.714

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