Design of Company Management Dashboard With Machine Learning Analysis For Optimization of Arcade Game Centre Operations - Dedy Andriyanto; Antonius Darma Setiawan; Sinka Wilyanti (Al-Kharaj: Journal of Islamic Economic and Business, 2025)
The growth of the digital entertainment industry, especially arcade game centers, demands an effective and data-driven management system to improve operational efficiency and customer experience. This study aims to design a company management dashboard integrated with machine learning analysis to optimize arcade game center operations. This dashboard is designed to provide real-time data visualization and support strategic decision-making through predictive analysis of machine performance, usage trends, and customer behavior. The research methods used include collecting primary and secondary data from the arcade game center transaction system, designing the system using a waterfall approach, and implementing machine learning algorithms such as K-Means for customer segmentation and Random Forest for machine failure prediction. The results of the study show that the developed dashboard is able to provide relevant and accurate information efficiently, and supports data-driven decision-making. With this system, the company can minimize machine downtime, increase customer satisfaction, and design more targeted promotional strategies. This study proves that the integration between management dashboards and machine learning technology can be an innovative solution for operational optimization in the arcade game center industry. Further implementation is recommended for the development of financial analysis features and integration with customer loyalty systems.