Prediksi Keuntungan Harian dari Investasi Kripto Menggunakan Model Regresi Berganda Time Series - Sanjaya, Leonard Putra; Alam, Syariful; Shahidzinda, Rezha; Nurzainah, Nia Siti; Hermawan, Muhammad Rizki (Universitas Multi Data Palembang, 2025)
Crypto investment is gaining traction with students thanks to the ease of access through digital platforms like Mobee, especially the Flexi Earn feature that offers APR-based daily returns. Unfortunately, many investment decisions are still intuitive without quantitative analysis. This study aims to predict daily returns on crypto investments using linear time series multiple regression models on four assets: TRX, ENA, HBAR, and SUI. Data was collected for 30 days from the Mobee app, with the variables day n and coin price as predictors, and daily profit as the response. Results show that the regression model has high accuracy and meets the classical assumption test, although some assets violate normality within reasonable limits. The findings provide a simple yet effective analytical basis for students to make more targeted and data-driven investment decisions.