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IJID (International Journal on Informatics for Development)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 22527834     EISSN : 25497448     DOI : -
Core Subject : Science,
Computer Science & IT
One important point in the accreditation of higher education study programs is the availability of a journal that holds the results of research of many investigators. Since the year 2012, Informatics Department has English language. Journal called IJID International Journal on Informatics for Development. IJID Issues accommodate a variety of issues, the latest from the world of science and technology. One of the requirements of a quality journal if the journal is said to focus on one area of science and sustainability of IJID. We accept the scientific literature from the readers. And hopefully these journals can be useful for the development of IT in the world. Informatics Department Faculty of Science and Technology State Islamic University Sunan Kalijaga.
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Search results for , issue "2026" : 1 Documents clear
Comparative Analysis of The Combination of Metaheuristic and Machine Learning Algorithms Sirmayanti; Rahman, Farhan; Hendro Prastyo, Pulung; Mahyati
IJID (International Journal on Informatics for Development) 2026
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2026.4888

Abstract

Diabetes affects about 1.9% of the global population, mainly through Type 2 diabetes. Machine learning (ML) serves a pivotal role in enhancing diabetes prediction by analyzing complex datasets. Feature selection, a crucial ML pre-processing step, improved prediction accuracy by identifying relevant data and discarding irrelevant features. This study investigates the combination of metaheuristic algorithms and ML techniques to enhance diabetes prediction accuracy and computational efficiency. Utilizing the PIMA, Early Stage, and Vanderbilt datasets, experiments evaluated ten algorithm-model combinations based on metrics like accuracy, precision, the Wilcoxon test, and convergence curves. Key findings included that Firefly Algorithm-Logistic Regression, Bat Algorithm-Logistic Regression, and Cuckoo Search-Logistic Regression achieved 74.72% accuracy on PIMA; Firefly Algorithm-Support Vector Machine and Cuckoo Search-Naïve Bayes achieved 83.39% accuracy and 96.15% precision on Early Stage; and Firefly Algorithm-Naïve Bayes achieved 92.88% accuracy and precision on Vanderbilt. These results highlighted the potential of integrating metaheuristics with ML methods to improve clinical diagnostics. Future research is recommended to validate algorithm robustness across diverse datasets to further optimize diabetes prediction strategies.

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