Evaluasi Kinerja Machine Learning dalam Memprediksi Kemampuan Adaptasi Mahasiswa pada Lingkungan Pembelajaran Daring

Shindy Arti; Elan  Suherlan

doi:10.55382/jurnalpustakaai.v5i1.901

Penulis

Shindy Arti Universitas Negeri Jakarta
Elan Suherlan Universitas YARSI

DOI:

https://doi.org/10.55382/jurnalpustakaai.v5i1.901

Kata Kunci:

Tingkat Adaptasi Mahasiswa,, Pembelajaran Online, , Evaluasi Model machine learning, , neural network

Abstrak

Perkembangan kecerdasan buatan mendorong transformasi pembelajaran daring yang adaptif, di mana kemampuan adaptasi mahasiswa menjadi faktor krusial dalam keberhasilan belajar. Penelitian ini bertujuan mengevaluasi kemampuan algoritme machine learning dalam memprediksi tingkat adaptasi mahasiswa terhadap pembelajaran online. Data yang digunakan berasal dari tingkat pendidikan tinggi sebanyak 675 data mahasiswa, kemudian diolah menggunakan Python dan Orange 3.8. Metode pengembangan yang digunakan dalam penelitian ini merujuk pada metode CRISP-DM. Terdapat lima tahapan yang dilakukan yaitu kejelasan alur bisnis, pemahaman data, persiapan data, pemodelan dan evaluasi kinerja model. Dari tujuh algoritma supervised learning digunakan, algoritma Neural Network memiliki performa terbaik secara keseluruhan maupun per tingkat adaptasi (tinggi, sedang, rendah), dengan akurasi tertinggi sebesar 0,943. Penelitian ini juga mengidentifikasi faktor utama adaptasi menggunakan algoritma Decision Tree, dengan durasi kelas sebagai pembeda dominan. Rekomendasi penelitian selanjutnya mencakup seleksi fitur, penyeimbangan data, dan pengembangan sistem interaktif berbasis pengguna.

Unduhan

Data unduhan belum tersedia.

Biografi Penulis

Elan Suherlan, Universitas YARSI

Referensi

N. Rane, S. Choudhary, and J. Rane, “Education 4.0 and 5.0: Integrating Artificial Intelligence (AI) for Personalized and Adaptive Learning,” SSRN Electron. J., no. January, 2023, doi: 10.2139/ssrn.4638365.

R. Shafique, W. Aljedaani, F. Rustam, E. Lee, A. Mehmood, and G. S. Choi, “Role of Artificial Intelligence in Online Education: A Systematic Mapping Study,” IEEE Access, vol. 11, pp. 52570–52584, 2023, doi: 10.1109/ACCESS.2023.3278590.

C. She, Q. Liang, W. Jiang, and Q. Xing, “Learning adaptability facilitates self-regulated learning at school: the chain mediating roles of academic motivation and self-management,” Front. Psychol., vol. 14, no. May, pp. 1–9, 2023, doi: 10.3389/fpsyg.2023.1162072.

M. M. H. Suzan, N. A. Samrin, A. A. Biswas, and M. A. Pramanik, “Students’ Adaptability Level Prediction in Online Education using Machine Learning Approaches,” 2021 12th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2021, no. July, 2021, doi: 10.1109/ICCCNT51525.2021.9579741.

M. Sree, J. J. James, A. Shaji, and A. M. Kuruvilla, “Estimation of Learners’ Levels of Adaptability in Online Education Using Imbalanced Dataset,” 2023 IEEE Int. Conf. Recent Adv. Syst. Sci. Eng., pp. 1–7, 2023, doi: 10.1109/rasse60029.2023.10363543.

Y. Gonca Telli and A. S. Karakose, “Changes in Innovativeness After Covid-19 Pandemic,” UTMS J. Econ., vol. 11, no. 2, pp. 161–170, 2020.

Z. Li et al., “Students’ online learning adaptability and their continuous usage intention across different disciplines,” Humanit. Soc. Sci. Commun., vol. 10, no. 1, pp. 1–10, 2023, doi: 10.1057/s41599-023-02376-5.

O. H. T. Lu, A. Y. Q. Huang, J. C. H. Huang, A. J. Q. Lin, H. Ogata, and S. J. H. Yang, “Applying learning analytics for the early prediction of students’ academic performance in blended learning,” Educ. Technol. Soc., vol. 21, no. 2, pp. 220–232, 2018.

O. Iparraguirre-Villanueva et al., “Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 4, pp. 494–503, 2023, doi: 10.14569/IJACSA.2023.0140455.

G. Stankovska, I. Memedi, and S. P. Grncarovska, “Impact of COVID-19 on Higher Education: Challenges and Opportunities.,” Bulg. Comp. Educ. Soc., vol. 20, pp. 181–188, 2022, [Online]. Available: https://eric.ed.gov/?id=ED622717.

C. Janiesch and K. Heinrich, “Machine learning and deep learning,” 2021.

L. C. Joy and A. Raj, “A Review on Student Placement Chance Prediction,” 2019 5th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2019, pp. 542–545, 2019, doi: 10.1109/ICACCS.2019.8728505.

W. Dwi Yuniarti, E. Winarko, and A. Musdholifah, “Data mining for student assessment in e-leaming: A survey,” 2020 5th Int. Conf. Informatics Comput. ICIC 2020, 2020, doi: 10.1109/ICIC50835.2020.9288533.

M. M. H. Suzan, N. A. Samrin, A. A. Biswas, and M. A. Pramanik, “Students’ Adaptability Level Prediction in Online Education using Machine Learning Approaches,” 2021 12th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2021, pp. 1–7, 2021, doi: 10.1109/ICCCNT51525.2021.9579741.

M. Shoaib, N. Sayed, J. Singh, J. Shafi, S. Khan, and F. Ali, “AI student success predictor: Enhancing personalized learning in campus management systems,” Comput. Human Behav., vol. 158, no. February, p. 108301, 2024, doi: 10.1016/j.chb.2024.108301.

V. Teslyuk, A. Doroshenko, and D. Savchuk, “Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning,” CEUR Workshop Proc., vol. 3387, pp. 331–343, 2023.

hsan, I., & Putra, A. A. (2018). Autonomous Sales Robot untuk Pengenal Produk Berbasis Barcode dan Arduino ATMega328. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 2(1), 397 - 402. https://doi.org/10.29207/resti.v2i1.264.