ANALISIS PENERIMAAN INTEGRASI AI ADAPTIF PADA E-LEARNING PRA-IMPLEMENTASI MENGGUNAKAN TAM
DOI:
https://doi.org/10.31849/qpj6sn93Keywords:
Artificial Intelligence, E-learning, TAM, PLS-SEM, Penerimaan teknologiAbstract
Integrasi Artificial Intelligence (AI) adaptif dalam sistem e-learning menjadi salah satu peluang pengembangan pembelajaran digital yang mampu mendukung personalisasi pengalaman belajar mahasiswa. Namun, kajian mengenai kesiapan pengguna terhadap penerapan teknologi tersebut pada tahap pra-implementasi masih relatif terbatas, khususnya dalam konteks platform e-learning internal perguruan tinggi. Penelitian ini bertujuan untuk menganalisis potensi penerimaan integrasi AI adaptif pada platform eLena (E-learning Nurul Fikri Academy) menggunakan pendekatan Technology Acceptance Model (TAM). Penelitian menggunakan metode kuantitatif dengan pendekatan survei terhadap 122 mahasiswa Program Studi Sistem Informasi angkatan 2022 yang telah menggunakan platform eLena. Teknik pengambilan sampel dilakukan secara purposive sampling, sedangkan analisis data menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM) dengan bantuan perangkat lunak SmartPLS 4. Hasil penelitian menunjukkan bahwa persepsi kegunaan dan persepsi kemudahan penggunaan berpengaruh signifikan terhadap sikap pengguna terhadap sistem e-learning. Selain itu, sikap pengguna terbukti berpengaruh signifikan terhadap potensi penerimaan integrasi AI adaptif. Persepsi kegunaan juga menunjukkan pengaruh langsung terhadap potensi penerimaan AI adaptif, sedangkan persepsi kemudahan penggunaan tidak menunjukkan pengaruh langsung yang signifikan. Temuan ini menunjukkan bahwa pembentukan sikap pengguna menjadi faktor penting dalam mendorong kesiapan penerimaan teknologi baru. Penelitian ini memberikan gambaran awal bagi institusi pendidikan dalam merancang pengembangan e-learning berbasis AI yang lebih sesuai dengan kebutuhan dan persepsi pengguna.
References
[1] I. Maita and S. Majid, “Analisis Penerimaan terhadap Penggunaan E-Learning Menggunakan Metode Technology Acceptance Model (TAM),” Jurnal Sistim Informasi dan Teknologi, pp. 30–35, Mar. 2022, doi: 10.37034/jsisfotek.v4i1.120.
[2] D. H. Benita and W. A. Kusuma, “Analisis tingkat efektivitas Platform E-Learning Learning Management System (LMS) terhadap mahasiswa,” Equivalent: Jurnal Ilmiah Sosial Teknologi, vol. 4, no. 1, Feb. 2022, doi: 10.46799/jequi.v4i1.77.
[3] Y. Wirani, T. Nabarian, and M. S. Romadhon, “Evaluation of continued use on Kahoot! As a gamification-based learning platform from the perspective of Indonesia students,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 545–556. doi: 10.1016/j.procs.2021.12.172.
[4] F. A. S. Sihaloho and Z. Napitupulu, “Penggunaan Kecerdasan Buatan (Artificial Intelligence) dalam Dunia Pendidikan Di Indonesia Tinjauan Literatur,” Rekognisi: Jurnal Pendidikan dan Kependidikan, vol. 9, Jun. 2024.
[5] R. Oktavian, R. F. Aldya, and R. F. Arifendi, “Artificial Intelligence dan Pendidikan Era Society 5.0,” Jurnal Ilmu Pendidikan, vol. 6, no. 2, pp. 143–150, 2023.
[6] N. Barz, M. Benick, L. Dörrenbächer-Ulrich, and F. Perels, “Students’ acceptance of e-learning: extending the technology acceptance model with self-regulated learning and affinity for technology,” Discover Education, vol. 3, no. 1, Dec. 2024, doi: 10.1007/s44217-024-00195-7.
[7] A. Hakimi, R. L. M. Yue, M. S. Muhsin, M. A. Bakar, C. T. Y. Teng, and K. D. Prihadi, “The social impact of artificial intelligence chatbots on college students,” International Journal of Evaluation and Research in Education, vol. 14, no. 1, pp. 10–16, Feb. 2025, doi: 10.11591/ijere.v14i1.29469.
[8] T. Nabarian and Ali Akbar, “Evaluation of Continued use Factors on Academic Chatbot Utilization in Higher Education,” The Indonesian Journal of Computer Science, vol. 14, no. 5, Oct. 2025, doi: 10.33022/ijcs.v14i5.5033.
[9] Z. Z. A. Thaariq, D. Kuswandi, and M. D. K. Degeng, “Measuring the quality of adaptive environments in instruction based on student perceptions,” Inovasi Kurikulum, vol. 21, no. 1, pp. 1–14, Feb. 2024, doi: 10.17509/jik.v21i1.62255.
[10] F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” vol. 13, no. 3, 1989, doi: 10.2307/249008.
[11] Sugiono, Metode Penelitian Kuantitatif, Kualitatif, dan R&D. 2023. [Online]. Available: www.cvalfabeta.com
[12] I. Ghozali and K. A. Kusumadewi, SmartPLS 4 Partial Least Squares (Konsep, Teknik, dan Aplikasi), 1st ed. 2024.
[13] J. A. M. García, C. G. Gómez, A. T. López, and M. J. Schlosser, “Applying the technology acceptance model to online self-learning: A multigroup análisis,” Journal of Innovation and Knowledge, vol. 9, no. 4, Oct. 2024, doi: 10.1016/j.jik.2024.100571.
[14] H. Y. Ahn, “AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application,” Sustainability (Switzerland), vol. 16, no. 20, Oct. 2024, doi: 10.3390/su16209066.
[15] X. Zhang, X. Hu, Y. Sun, L. Li, S. Deng, and X. Chen, “Integrating AI Literacy with the TPB-TAM Framework to Explore Chinese University Students’ Adoption of Generative AI,” Behavioral Sciences, vol. 15, no. 10, Oct. 2025, doi: 10.3390/bs15101398.
[16] M. Rodriguez, A. Rizky, Y. I. Tanjung, and Sumliyah, “Optimizing E-Learning Platforms through Adaptive Learning for Diverse Demographics,” Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, vol. 4, no. 1, pp. 30–39, Jul. 2025, doi: 10.33050/mentari.v4i1.905.
[17] H. D. Pradana, R. Rusijono, I. Y. Maureen, and E. Youhanita, “Artificial Intelligence in Learning Design: Acceptance, Perceived Effectiveness, and Barriers,” Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika, vol. 9, no. 2, pp. 489–511, Jul. 2025, doi: 10.36312/e-saintika.v9i2.2688.
[18] Wisnu Mu’amar, Andri Firmansyah Caniago, Fadil Tazakka Pribadi, Arifah Budi Hidayah, and Ito Setiawan, “Analisis Penerimaan Teknologi Artificial Intelligence dalam Penyelesaian Tugas Mahasiswa Menggunakan Model Tam,” Jurnal Teknik Informatika dan Teknologi Informasi, vol. 5, no. 1, pp. 412–426, Jun. 2025, doi: 10.55606/jutiti.v5i1.5203.
[19] A. S. Damaiwa, P. Adytia, and S. Mallala, “Perancangan Smart UI Adaptif pada E-Learning Menggunakan Metode Design Thinking Berbasis Preferensi dan Perilaku Pengguna,” Jurnal Sistem Informasi, vol. 7, no. 3, pp. 11–22, Jan. 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 ZONAsi: Jurnal Sistem Informasi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
CC BY-SA 4.0
Attribution-ShareAlike 4.0
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
