Data-Driven Forecasting of Lecturer Needs for a Growing Master’s Program at Universitas Lancang Kuning, Indonesia

\nMany higher education institutions in Indonesia struggle to maintain the ideal lecturer-to-student ratio, which is essential for ensuring educational quality and compliance with government regulations. This issue is particularly pressing for the Master of Management program at the Graduate School of Universitas Lancang Kuning, where rapid student growth poses challenges in aligning faculty resources with demand. This study aims to forecast the number of permanent lecturers needed by 2027 to support sustainable program development. This applied research provides strategic recommendations for policymakers regarding faculty recruitment to maintain educational quality. Secondary time series data from the past 10 periods, sourced from the Higher Education Database (PDDikti), were analyzed using the semi-average method to identify trends. The findings indicate that by the even semester of 2027, the projected student enrollment in the program will reach approximately 860, necessitating an ideal number of 43 permanent lecturers to meet the government-mandated lecturer-tostudent ratio of 1:20 for academic master’s programs. This research introduces a forward-looking, data-driven framework for planning faculty recruitment, enabling institutions to align their academic resources with projected student growth effectively. Ensuring an optimal lecturer-tostudent ratio is critical for upholding educational standards and preventing potential regulatory sanctions that could impact program accreditation and quality.\n