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All Journal snimed Tanra : Jurnal Desain Komunikasi Visual Fakultas Seni dan Desain Universitas Negeri Makassar Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Hexatech: Jurnal Ilmiah Teknik Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Informatika Polinema (JIP) Jurnal Multidisiplin Indonesia
Helen Sastypratiwi, Helen
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Journal : Jurnal Teknik Informatika (JUTIF)

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Improving Imbalanced Data Classification Using Stacked Ensemble Learning with Naïve Bayes Variants and Random Forest Sastypratiwi, Helen; Yulianti, Yulianti; Muhardi, Hafiz
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5308

Abstract

Classification in imbalanced and heterogeneous datasets poses significant challenges in informatics, particularly in agricultural domains where minority classes are often underrepresented and feature redundancy affects model performance. This research aims to improve classification performance by developing a stacked ensemble learning framework that integrates probabilistic and tree-based learners to address class imbalance and enhance model interpretability. The framework combines Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), and Random Forest (RF) as base learners with Logistic Regression as the meta-learner. Feature selection was performed using Chi-Square and ReliefF to identify the most relevant predictors, while SMOTE was applied to balance the dataset. Two ensemble configurations were evaluated: Ensemble A (GNB + MNB) and Ensemble B (GNB + RF). Experimental results demonstrate that Ensemble B achieved 97% accuracy and a macro F1-score of 0.97, with a 5.7% accuracy improvement over the best individual classifier and an 18% improvement in minority-class recall. The integration of probabilistic and tree-based models within a stacked architecture provides an interpretable and effective solution for data-driven decision systems in informatics, particularly valuable for domains requiring both high accuracy and model explainability in handling imbalanced datasets.
Co-Authors Akmal, Wildan Andriani, Feny Anggi Perwitasari Anggi Srimurdianti Sukamto, Anggi Srimurdianti Anggi Srimurdianto Sukamto, Anggi Srimurdianto Aprillita Dwiyani Arif Bijaksana Putra Negara Arina Nurfianti Butar, All Binardo V B Desti Mawarni, Desti Enda Esyudha Pratama Enda Esyudha Pratama, Enda Esyudha Fatma Agus Setyaningsih Fortunatus Adhiethera Tuah Putra Gunawan, Billy Hafiz Muhardi Helfi Nasution Hengky Anra Heri Priyanto, Heri Herry Sujaini Hervica Marsha Valentine, Hervica Marsha Kariman, Ridho Martaleli Bettiza Maulana, Ikhwan Meilani Mega Juita, Meilani Mega Melifa Gardenia, Melifa Milda Surgani Firdania, Milda Surgani Muhammad Azhar Irwansyah Muhammad Insan Kamil, Muhammad Insan Narti Prihartini, Narti Niken Candraningrum Nikentari, Nerfita Novi Safriadi Panji Kurniawan Prakarsa, Pandhe Rahmadani, Dwisuci Rahmah, Ulfatur Raka Umbara, Muhammad Regina Suci Wahyuningtyas, Regina Suci Rudy Dwi Nyoto, Rudy Dwi Siti Hadianti, Siti Suharjono Then, Suharjono Tursina Tursina Yulianti Yulianti Yulianti