Multi-Algorithm AI Mobile Application for the Convection Business

Abstract

Small and medium-sized garment enterprises often struggle with manual production recording and payroll estimation, leading to inefficiency. This study develops a mobile application integrating multiple artificial intelligence algorithms to improve business prediction accuracy. The research employed a Research and Development (R&D) method with a Rapid Application Development (RAD) approach. Eight years of historical production and payroll data were analyzed using three algorithms: Linear Regression, Random Forest Regression, and KMeans Clustering. The results indicate that the application enhanced recording efficiency, Random Forest outperformed Linear Regression on fluctuating data, and K-Means effectively recommended the best-performing employees. In conclusion, the system contributes to digitalization and data-driven decisionmaking in the garment sector.