Investigating the Impact of Mobile Legends Gameplay on Students' Academic Performance with Ordinal Logistic Regression
DOI:
https://doi.org/10.36456/jstat.vol16.no2.a7567Keywords:
Mobile Legends, Online Game, Ordinal Logistic RegressionAbstract
The development of information technology and online games, such as Mobile Legends Bang Bang, has spread to various segments of society in Indonesia, including children, students, and university students. Although the government has supported the e-sports industry, research on the influence of interest in playing Mobile Legends on students' academic performance is still limited. Therefore, this research aims to identify the impact of interest in playing Mobile Legends and the significant factors affecting students' academic performance. We used the ordinal logistic regression method in our analysis, a statistical technique to measure the relationship between independent variables and ordinal dependent variables, such as academic performance levels categorized as low, moderate, or high GPA. Our analysis results in two models: low Cumulative Grade Point Average (GPA) and moderate GPA. The significant factors are the level of interest, including the 'very interested' and 'interested' categories, and Gender with the category 'male.' Our analysis also indicates that the obtained model provides good results and is acceptable since all the explanatory variables are statistically significant.
References
S.J. Akbar, et al., "Bagging Regresi Logistik Ordinal pada Status Gizi Balita," in Media Statistika, vol. 3, no. 2, 2010, pp. 103-116. DOI: https://doi.org/10.14710/medstat.3.2.103-116
Q. Chen & J. Qi, “How Much Should We Trust R2 and Adjusted R2: Evidence from Regressions in Top Economics Journals and Monte Carlo Simulations,” in Journal of Applied Economics, vol. 26, no. 1, 2207326, 2023. DOI: https://doi.org/10.1080/15140326.2023.2207326
D. Chicco, M.J. Warrens, & G. Jurman, “The Coefficient of Determination R-squared is More Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation,” in PeerJ Computer Science, 7, e623, 2021. DOI: https://doi.org/10.7717/peerj-cs.623
N. Fitriyani, L. Awalushaumi, & A. Kurnia, “Polytomous Logistic Regression in Analyzing the Presence of National Pilot Mosque in Karang Baru Mataram”, in the Proceeding, 1st ICST Mataram University, 2016
I. Ghozali, “Aplikasi Analisis Multivariate dengan Program SPSS 25. Semarang, Universitas Diponegoro, 2018.
D. Gujarati, Dasar-dasar Ekonometri Edisi Ketiga, Jilid I dan II. Erlangga, Jakarta, 2007.
C. Hagquist & M. Stenbeck, “Goodness of Fit in Regression Analysis–R2 and G2 Reconsidered”, in Quality and Quantity, vol. 32, no. 3, pp. 229-245, 1998. DOI: https://doi.org/10.1023/A:1004328601205
D.W. Hosmer and S. Lemeshow, "Applied Logistic Regression," John Wiley & Sons, Inc., New York, 2000. DOI: https://doi.org/10.1002/0471722146
S. Imaslihkah, et al., "Analisis Regresi Logistik Ordinal Terhadap Predikat Kelulusan Mahasiswa S1 di ITS Surabaya," in Jurnal Sains dan Seni POMITS, vol. 2, no. 2, pp. 177-182, 2013.
F.R. Iskandar, et al., "Dampak Permainan Mobile Legends Terhadap Motivasi Belajar Siswa Sekolah Dasar," in EduBasic Journal: Jurnal Pendidikan Dasar, vol. 1, no. 2, pp. 116-122, 2019. DOI: https://doi.org/10.17509/ebj.v1i2.26599
P.K. Ozili, “The Acceptable R-Square in Empirical Modelling for Social Science Research,” in Social Research Methodology and Publishing Results, 5 June, 4128165, 2022. DOI: https://doi.org/10.2139/ssrn.4128165
Playstore, "Games Kategori," 2022. [Online, Accessed 26 July 2022]. Available: https://play.google.com/store/games.
B. Simamora, Panduan Riset Prilaku Konsumen. Gramedia, Jakarta, 2004.
Sugiyono, Metode Penelitian Kuantitatif. Bandung, Alfabeta, 2018.
J. Supratno, Statistika: Teori dan Aplikasi. Erlangga, Jakarta, 2000.







