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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Intelligent Computing and Health Informatics (JICHI)
Farikhin Farikhin
Diponegoro University
Computer Science & IT Dentistry Electrical & Electronics Engineering Medicine & Pharmacology Public Health

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A Robust Hybrid Cost Sensitive Stacking Ensemble Model for Hepatitis Survival Prediction and Clinical Decision Support Muhammad Sam'an; Farikhin Farikhin
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.17519

Abstract

Chronic hepatitis continues to pose a significant global health challenge, frequently advancing to liver cirrhosis and hepatocellular carcinoma if not managed with precise prognostic interventions. The capacity to accurately predict patient survival is essential for optimizing resource allocation and treatment planning. Although Machine Learning (ML) has shown promise in medical diagnostics, standard algorithms often underperform when applied to hepatitis datasets characterized by severe class imbalance and high dimensionality. Conventional models tend to bias predictions toward the majority class (survival), resulting in a high rate of False Negatives for the minority class (mortality), which is clinically unacceptable. Moreover, single-classifier approaches often lack the generalization capability necessary for robust clinical deployment. To address these deficiencies, this study proposes a Hybrid Cost-Sensitive Stacking Ensemble Model (HCS-SEM). The framework integrates three strategic components: (1) a rigorous Split-First Synthetic Minority Oversampling Technique (SMOTE) protocol to resolve class skewness without data leakage; (2) a Chi-Square feature ranking mechanism to eliminate redundant clinical attributes; and (3) a Two-Tier Stacking Architecture employing Random Forest, SVM, and Gradient Boosting as base learners, optimized by a Logistic Regression meta-learner. Experimental validation on the UCI Hepatitis dataset demonstrates that HCS-SEM significantly outperforms standalone classifiers and traditional ensemble methods. The model achieves superior performance metrics, particularly in Sensitivity and F1-Score, confirmed by the Friedman Rank Test and Nemenyi post-hoc analysis. These findings suggest that the proposed HCS-SEM provides a robust, clinically viable tool for hepatitis prognosis, offering high-precision decision support for medical practitioners managing high-risk patients.
K-MEANS-BASED TRAINING DATA PROCESSING FOR IMPROVING TOURISM RECOMMENDATION ACCURACY Candra Agustina; Purwanto Purwanto; Farikhin Farikhin; Eka Rahmawati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7274

Abstract

This study investigates the enhancement of tourism destination recommendation systems through the use of K-Means clustering to improve training data quality and model accuracy. The rapid advancement of information technology has increased the demand for personalized and accurate recommendation systems within the tourism industry. Despite this, achieving high prediction accuracy remains a significant challenge. This study employs K-Means clustering to segment training data into homogeneous clusters, thereby improving data representation and enhancing the predictive accuracy of recommendation models. The research methodology includes a comprehensive literature review, data collection, preprocessing, clustering, and model testing using K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms. The results show that after applying K-Means clustering, KNN's accuracy increased by 2.27%, and its kappa and precision values also improved, indicating enhanced reliability and prediction accuracy. Naive Bayes exhibited substantial improvements with a 9.09% increase in accuracy, alongside significant enhancements in kappa and precision metrics. Conversely, the Decision Tree algorithm experienced a decline in performance after clustering. Therefore, clustering techniques are not suitable for application to the Decision Tree algorithm.
Co-Authors Candra Agustina Eka Rahmawati Muhammad Sam'an Purwanto Purwanto