Inaugurated as Professor, This Academic Introduces Decision-Making Concept in the AI Era

Amid the rapid development of analytics technology and artificial intelligence, many organisations still face a fundamental challenge: how to translate data-based insights into precise and impactful decisions. As predictive capabilities become increasingly sophisticated, the decision-making process does not always become more certain. To address this challenge, BINUS University has reaffirmed its commitment to scientific development through the inauguration of a professor. The university inaugurated Prof Tuga Mauritsius, MSi., PhD., as a permanent professor in the field of Decision Support Systems (DSS), which focuses on developing data-based decision support systems and analytical models. In his scientific oration titled “A New Generation of Decision Support System: Integrating Machine Learning, Scenario Analysis, and System Dynamics”, Prof Tuga highlighted that current decision-making systems still face a gap between insights and actions. He explained that although technologies like machine learning can produce predictions with high accuracy, many organisations still struggle to translate those results into real strategic decisions. In this context, conventional DSS approaches are deemed still reactive and limited to descriptive analysis, without being able to systematically guide optimal and impactful decisions. As a solution, Tuga introduced the concept of a New Generation DSS that integrates three main approaches: predictive analytics through machine learning, scenario analysis to anticipate various future possibilities, and system dynamics to simulate policy impacts in complex systems. This approach is designed to form a more comprehensive decision-making flow, from prediction to prescriptive policy recommendations. Furthermore, he emphasised that the integration of these three elements not only improves the quality of analysis but also strengthens the decision-makers’ ability to understand the long-term consequences of each policy. Thus, the system no longer stops at “what will happen”, but evolves into “what should be done” in various possible scenarios.