Pemodelan Tren Pendapatan Game Gacha Menggunakan Regresi Linier
DOI:
https://doi.org/10.31294/icej.v6i1.11840Keywords:
simple linear regression, game revenue, gacha gamesAbstract
The rapid growth of the digital gaming industry, particularly games with gacha monetization systems, has resulted in highly fluctuating revenue patterns over time. Game revenue does not always follow a linear trend, as it is influenced by various factors such as in-game events, content updates, and player behavior. This study aims to analyze the effect of time variables (months) on the revenue of three popular gacha games, namely Genshin Impact, Honkai: Star Rail, and Zenless Zone Zero. This research employs a quantitative approach using simple linear regression analysis. The data used in this study are secondary data in the form of monthly revenue collected from AppMagic, covering the period from January to December 2025. The results indicate that all three games have negative regression coefficients, suggesting a declining revenue trend over time. However, the coefficient of determination values are relatively low, indicating that the time variable has a very limited ability to explain revenue variation. These findings suggest that gacha game revenue is not significantly influenced by time alone but is driven by other non-linear factors. Therefore, simple linear regression can only provide a general trend and is insufficient to fully describe the dynamics of gacha game revenue.
References
Dominggo, H., & Nengsih, M. K. (2024). Pengaruh lingkungan kerja, disiplin kerja, motivasi dan komunikasi terhadap kinerja karyawan (studi empiris pada PT. Putra Maga Naditama). *Jurnal Akuntansi, Manajemen Dan Ekonomi Islam (JAM-EKIS), 7*(1), 44–58. https://doi.org/10.36085/jam-ekis.
Hamari, J., Hanner, N., & Koivisto, J. (2017). Service quality explains why people use freemium services but not if they go premium: An empirical study in free-to-play games. International Journal of Information Management,37(1),1449–1459.https://doi.org/10.1016/j.ijinfomgt.2016.09.004.
Laudon, K. C., & Traver, C. G. (2021). E-commerce: Business, technology, society (16th ed.). Pearson.
Marchand, A., & Hennig-Thurau, T. (2013). Value creation in the video game industry: Industry economics, consumer benefits, and research opportunities. Journal of Interactive Marketing, 27(3), 141–157.https://doi.org/10.1016/j.intmar.2013.05.001.
Montgomery, D.C., Peck, E.A. and G Geoffrey Vining (2013). Introduction to Linear Regression Analysis. [online] Chicester Wiley Ann Arbor, Michigan Proquest. Available at: at: https://www.wiley.com/en-us/Introduction+to+Linear+Regression+Analysis%2C+5th+Edition-p-9780470542811
Sipayung, S. P., Siagian, N. A., & Ginting, A. K. B. (2024). Analisis prediksi jumlah penerimaan mahasiswa baru dengan metode regresi linier sederhana. SNISTIK: Seminar Nasional Inovasi Sains Teknologi Informasi Komputer, 1(2), 382–387.
Sulung, U., & Muspawi, M. (2024). Memahami sumber data penelitian: Primer, sekunder, dan tersier. Jurnal Edu Research, 5(3), 110–116.
Zendle, D., Meyer, R., & Over, H. (2020). A behavioral and psychological analysis of adolescent interactions with loot box systems and in-game randomized reward mechanisms. Royal Society Open Science, 7(6), 190049.https://doi.org/10.1098/rsos.190049
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Advanced multivariate data analysis techniques for empirical research in information systems and business analytics (7th ed.). Pearson Education.
Gujarati, D. N., & Porter, D. C. (2009). Fundamental econometric methods for linear regression modeling and statistical inference (5th ed.). McGraw-Hill.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Yosua Tambunan, Yudi Yohannes, Sardo Sipayung
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
