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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Chowdhury, Shefayatuj Johara
International Islamic University Chittagong, Chittagong, BANGLADESH
Computer Science & IT Electrical & Electronics Engineering

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An AHP-Modified TOPSIS and Pareto Model for Employee Turnover Intention Analysis Al Abid, Faisal Bin; Bakri, Aryati Binti; Chowdhury, Shefayatuj Johara; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.6506

Abstract

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.
Co-Authors Al Abid, Faisal Bin Bakri, Aryati Binti Uddin, Jia