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Unstacking the Stack: Synthesis of Optimization Strategies for Stacked Ensemble Models in Multi-Domain Contexts Widiyatmoko, Carolus Borromeus; Gernowo, Rahmat; Warsito, Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2545

Abstract

Stacked ensemble models (SEMs) remain widely used for integrating multiple learning algorithms into a single predictive system. However, SEMs continue to face challenges such as accuracy limitations, overfitting, high computational expenses, and limited interpretability. This study conducts a systematic review of 269 peer-reviewed papers published between 2020 and 2025, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure transparency and rigor in article selection. The review identifies key technical issues in SEM implementations and synthesizes their corresponding optimization strategies. To address these challenges, a method-engineering-based modular three-stage framework is proposed, consisting of pre-processing, processing, and post-processing phases. Each stage targets specific weaknesses by improving data quality, optimizing models and hyperparameters, and enhancing interpretability and adaptability. The framework provides a structured foundation that links SEM optimization approaches with their development stages, supporting the design of robust, efficient, and interpretable ensemble models for practical applications.