Analisis Performa Akademik Mahasiswa Menggunakan Social Network Analysis (Studi Kasus: Prodi Bisnis Digital Universitas dr. Soebandi)

Authors

  • Khoirunnisa Afandi Bisnis Digital, Universitas dr. Soebandi
  • M. Habibullah Arief Bisnis Digital, Universitas dr. Soebandi
  • Nadya Faizatul Laily Bisnis Digital, Universitas dr. Soebandi
  • Derik Maulana Nugroho Bisnis Digital, Universitas dr. Soebandi

DOI:

https://doi.org/10.37802/joti.v5i2.514

Keywords:

Kegagalan Akademik, Performa Akademik, Social Network Analysis

Abstract

Pendidikan dengan kualitas yang baik akan menghasilkan generasi yang cerdas dan berpotensi. Kriteria utama untuk mengukur kinerja lembaga akademik adalah tingkat kelulusan siswa atau mahasiswa. Hal tersebut memunculkan permasalahan bagaimana mengukur performa akademik mahasiswa sehingga bisa menjadi lulusan yang berkualitas. Pengukuran performa akademik dilakukan dengan mengumpulkan data mahasiswa lalu menggabungkan data tersebut dengan data kuesioner yang dibagikan ke mahasiswa mengenai pengalaman belajar mereka. Penelitian dilakukan dengan melakukan prediksi dengan machine learning dan analisis menggunakan Social Network Analysis untuk menampilkan inti jaringan yang paling berpengaruh terhadap performa akademik mahasiswa. Hasil penelitian menunjukkan bahwa rata-rata akurasi algoritma untuk prediksi performa akademik mahasiswa adalah 0,76. Sehingga data tersebut dapat digunakan untuk prediksi performa akademik mahasiswa dengan tingkat akurasi yang tinggi. Hasil analisis menunjukkan bahwa Usia, Pendidikan Orang Tua, Kota Asal dan Kesulitan dalam belajar memiliki pengaruh terhadap performa akademik mahasiswa.

Downloads

Download data is not yet available.

References

S. Rajendran, S. Chamundeswari and A. A. Sinha, "Predicting the academic performance of middle- and high-school students using machine learning algorithms," Social Sciences & Humanities Open, 2022.

V. B. Kusnandar, "Hanya 6% Warga Indonesia yang Berpendidikan Tinggi pada Juni 2022," 20 9 2022. [Online]. Available: https://databoks.katadata.co.id/datapublish/2022/09/20/hanya-6-warga-indonesia-yang-berpendidikan-tinggi-pada-juni-2022#:~:text=Sampai%20Juni%202022%20penduduk%20Indonesia,tamatan%20Sekolah%20Dasar%20(SD).

Lokadata, "lokadata," 2017. [Online]. Available: https://lokadata.beritagar.id/chart/preview/10-provinsi-dengan-persentase-mahasiswa-drop-out-do-tertinggi-1519122848. [Accessed 1 November 2023].

G. J. Baars, H. G. Schmidt and P. Hermus, "Early Identification of Successful and Unsuccessful Students in the First Year at the University," Health Professions Education, Vol. 8, No. 1, 2022.

D.-L. Ngo-Hoang, J. Dayupay, S. Ajibade and O. Oyebode, "Utilization of Ensemble Techniques for Prediction of the Academic Performance of Students," Journal Of Optoelectronics Laser, Vol. 41, No. 6, 2022.

H. A. Mengash, "Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems," IEEE Access, Vol. 8, 2020.

H. Waheed, S.-U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani and R. Nawaz, "Predicting academic performance of students from VLE big data using deep learning models," Computers in Human Behavior, Vol. 104, 2020.

C. Beaulac and J. S. Rosenthal, "Predicting University Students’ Academic Success and Major Using Random Forests," Research in Higher Education, Vol. 60, pp. 1048–1064 , 2019.

E. Fernandes, M. Holanda, M. Victorino, V. Borges, R. Carvalho and G. V. Erven, "Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil," Journal of Business Research, 2018.

A. M. Bhandarkar, A. K. Pandey, R. Nayak, K. Pujary and A. Kumar, "Impact of social media on the academic performance of undergraduate medical students," Medical Journal Armed Forces India, Vol. 77, 2021.

K. Rafidah, A. Azizah, M. D. Norzaidi, S. C. Chong, M. I. Salwani and I. Noraini, "Stress And Academic Performance: Empirical Evidence From University Students," Scholarly Journal, Vol. 13, no. 1, pp. 37-51, 2009.

J. Scott, What is social network analysis?, 1 ed., London: Bloomsbury Publishing, 2012.

R. Baker, "Data Mining," in International Encyclopedia of Education (Third Edition), Elsevier, 2010, pp. 112-118.

J. A. Lara, D. Lizcano, M. A. Martínez, J. Pazos and T. Riera, "A system for knowledge discovery in e-learning environments within the European Higher Education Area – Application to student data from Open University of Madrid, UDIMA," Computers & Education, Vol. 72, pp. 23-36, 2014.

B. Chakraborty, K. Chakma and A. Mukherjee, "A density-based clustering algorithm and experiments on student dataset with noises using Rough set theory," EEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India, pp. 431-436, 2016.

R. R. Kabra and R. S. Bichkar, "Performance Prediction of Engineering Students using Decision Trees," International Journal of Computer Applications, Vol. 36, No. 11, 2011.

S. Clegg, E. Josserand, A. Mehra and T. S. Pitsis, "The Transformative Power of Network Dynamics: A research agenda," The transformative and innovative power of network dynamics, Vol. 37, No. 3, pp. 277-291, 2016.

J. Bennedsen and M. E. Caspersen, "Failure rates in introductory programming," ACM SIGCSE Bulletin, Vol. 39, No. 2, pp. 32-36, 2007.

E. B. Costa, B. Fonseca, M. A. Santana, F. F. d. Araújo and J. Rego, "Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses," Computers in Human Behavior, pp. 247-256, 2017.

J. Bravo-Agapito, S. J. Romero and S. Pamplona, "Early Prediction of Undergraduate Student’s Academic Performance in Completely Online Learning: A Five-Year Study," Computers in Human Behavior, 2020.

C. Romero, S. Ventura and P. D. Bra, "Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors," User Modeling and User-Adapted Interaction, Vol. 14, p. 425–464, 2004.

N. R. S. Raghavan, "Data mining in e-commerce: A survey," Sadhana, Vol. 30, p. 275–289, 2005.

Downloads