Implementation of Weighted Product Method for Teacher Selection at an Islamic Boarding School

Service teachers play a crucial role in Islamic boarding schools by imparting the knowledge and values they acquired during their studies. At Al-Kamil Islamic Boarding School, the selection of these service teachers is conducted annually to ensure only the most competent and ethically grounded candidates are chosen. Traditionally, this selection has been performed manually, which introduces several challenges, including subjectivity, inefficiency, and inconsistencies in evaluation. These limitations can compromise the fairness and transparency of the process, potentially leading to biased outcomes and reduced institutional credibility. To address these issues, this study proposes the implementation of a web-based Decision Support System (DSS) utilizing the Weighted Product (WP) method to support the teacher selection process. The WP method is particularly effective in multi-criteria decision-making, as it applies multiplicative weights to criterion scores, enabling a comprehensive and balanced evaluation of each candidate. In this application, 14 candidates are assessed based on eight criteria: morality, academic performance, craftsmanship, Quran memorization, achievements, understanding of classical Islamic texts, language skills, and computer literacy. The system calculates a preference score for each candidate using vector S and vector V normalization processes. The top three candidates identified are Arizkia Aulia (V = 0.07377), Zaskia Faliza (V = 0.07279), and Zulfa Nisa (V = 0.07231). The integration of this WP-based DSS enhances the objectivity, fairness, and efficiency of decision-making. Furthermore, it provides a replicable and scalable framework for educational institutions seeking to implement structured and data-driven staff selection processes.