Analitik Prediktif Terintegrasi: Regresi Linear Berganda untuk Keputusan Pembelian dan MA-3 untuk Peramalan Pendapatan

Novita Sambo Layuk (Universitas Dipa Makassar, Makassar)
Asrul Syam (Universitas Dipa Makassar, Makassar)
Samsu Alam (Universitas Dipa Makassar, Makassar)
Santi Santi (Universitas Dipa Makassar, Makassar)

TIN: TERAPAN INFORMATIKA NUSANTARA
Vol 7 No 3 (2026): August 2026

This study integrates purchase-decision analysis and short-term revenue forecasting to support data-driven management at the Thrift Store. The population consisted of 1,536 customers; the minimum sample size calculated using Slovin’s formula at a 10% margin of error was 94, while 100 respondents were analyzed. Questionnaire data were examined using Multiple Linear Regression, and monthly revenue data from July to December 2025 were analyzed using a 3-Month Moving Average (MA-3). All questionnaire items were valid (rhitung=0.7590–0.8569 > rtable=0.1966) and reliable (Cronbach’s Alpha=0.855–0.890). The residuals were normally distributed (Asymp. Sig.=0.605), multicollinearity was not detected (VIF=2.0460–2.5422), and the Durbin-Watson statistic was 2.002; however, the Glejser test indicated heteroscedasticity for price (p=0.001), so conventional inference for this predictor should be interpreted cautiously. OLS estimates showed positive coefficients for product quality (β=0.1998; p=0.0146), price (β =0.2255; p=0.0044), and service (β =0.4740; p<0.001), while the overall model was significant (F=61.084; p<0.001; R²=0.656). Service had the strongest bivariate correlation with purchase decisions (r=0.742). MA-3 produced a January 2026 revenue forecast of IDR 41,464,733, with a historical Mean Absolute Deviation (MAD) of IDR 18,522,744, indicating substantial forecast uncertainty. The contribution of this study lies in integrating cross-sectional analysis using Multiple Linear Regression and time-series analysis using MA-3 within a unified data-driven decision-making framework that combines perspectives on consumer behavior and revenue dynamics. Managerially, service improvement and transparent product information should be prioritized, while MA-3 is more appropriate as an early monitoring tool than as a high-precision forecasting model.