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All Journal Jurnal Transformatika Teknik: Jurnal Ilmu Teknik dan Informatika Journal Of Human And Education (JAHE) Jurnal Teknik Informatika dan Teknologi Informasi Journal of Technology Informatics and Engineering Jurnal Ilmiah Sistem Informasi
Supriadi, Candra
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Teknik: Jurnal Ilmu Teknik dan Informatika

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Adaptive Fuzzy Logic Integration for Optimizing Decision Support Systems under Data Uncertainty Supriadi, Candra
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 2 (2025): Oktober : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i2.1026

Abstract

Decision Support Systems (DSS) can become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodologies may be strong at handling vagueness, but are unable to adapt their behavior in response to different data distributions on their own. This paper recommends the creation of an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests were conducted on uncertainty-generated data sets to compare adaptive and conventional fuzzy models in terms of ISME (Root Mean Square Error), convergence stability, and decision accuracy. Previous results show that the adaptive model achieves a 21.4% increase in accuracy and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its ability to handle uncertainty. This book incorporates a self-tuning fuzzy decision model that converts static inference structures to dynamic evolving decision engines. The outcomes establish a foundation for next-generation smart DSS for real-time optimization in uncertainty.
Co-Authors Abdul Khalim, Muhammad Agus Priyadi ARDIYANTO, TRI Candra Supriadi Heri Kurniawan, Andreas Imaliya, Tri Irwan Sembiring Jin, Kim So Maya Utami Dewi, Maya Utami Nugroho, Aris Sarwo Nurmana, Ayyub Hamdanu Budi Priyadi Priyadi Purnomo, Hidriyanto Dwi Qosidah, Nanik S Siswanto SITI KHOLIFAH Sumaryanto Sumaryanto, Sumaryanto Wahyudi , Wiwid Yuli Fitrianto