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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Elkom: Jurnal Elektronika dan Komputer CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jesya (Jurnal Ekonomi dan Ekonomi Syariah) SOSCIED Jurnal Ilmiah Intech : Information Technology Journal of UMUS J-SAKTI (Jurnal Sains Komputer dan Informatika) Seminar Nasional Ilmu Terapan Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Kristiawan Nugroho
Universitas Stikubank
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : Journal of Future Artificial Intelligence and Technologies

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Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition De Rosal Ignatius Moses Setiadi; Kristiawan Nugroho; Ahmad Rofiqul Muslikh; Syahroni Wahyu Iriananda; Arnold Adimabua Ojugo
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-11

Abstract

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.
Co-Authors Ahmad Rofiqul Muslikh Ajeng Aquinia Aliyatul Munna Arnold Adimabua Ojugo De Rosal Ignatius Moses Setiadi De Rosal Igniatus Moses Setiadi Edy Winarno Eko Prasetyo Endang Tjahjaningsih Eri Zuliarso Isworo Nugroho Kasmari . Kristhoporus Hadiono Kristophorus Hadiono Lie Liana Lie Liana . Mamet Adil Araaf Niken Puspitasari Omar Farooq Raden Mohamad Herdian Bhakti Raden Mohamad Herdian Bhakti Suhana Suhana Sunardi Sunardi Syahroni Wahyu Iriananda Wiwien Hadi Kurniawati Yoga Ryan Fatony