<![CDATA[Employee turnover intention (TOI) is a significant challenge that affects an organization financially, particularly in the context of Indonesian academic sector, where turnover rate is notably high. This study uses a primary Indonesian academic dataset and proposes a novel framework for Indonesian academic turnover intention (TOI) encompassing Analytic hierarchy process (AHP), Modified TOPSIS combined with Pareto principle and compares the proposed frame- work with existing framework of entropy-based weighted method, traditional TOPSIS and interval scaling for categorizing academic employees according to productivity. The AHP procedure encompasses hybrid logarithmic linear normalization integrating linear as well as logarithmic normalization, consequently ensuring consistency and robustness for categorization of TOI. The proposed framework integrates Euclidean, Manhattan, Chebyshev distance for resolving the issues of traditional TOPSIS for ranking alternatives. The modified TOPSIS incorporates Information Gain, Recursive feature elimination (RFE) and Select K-best for finding Indonesian academic TOI. Random forest was implemented as the baseline classifier model for both the proposed and existing scheme. Experimental results revealed that proposed approach achieved higher predictive accuracy in contrast to the existing approach for categorizing employees into enthusiastic, behavioral and distressed. Therefore, this study establishes a robust approach for employee categorization outperforming the existing approach.]]>
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