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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) BEES: Bulletin of Electrical and Electronics Engineering
Rahma Dhea Safitri
STIKOM Tunas Bangsa, Pematangsiantar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Improved Genetic Algorithm with Adaptive Operators and Elitism for Random Forest Feature Selection in Heart Disease Classification Rahma Dhea Safitri; Solikhun Solikhun; Timbo Faritcan P. Siallagan
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10372

Abstract

Heart disease is one of the leading causes of mortality worldwide, and accurate prediction models are essential to support early diagnosis. However, conventional Random Forest classifiers generally utilize all available features, although not all features contribute equally to classification performance, resulting in unnecessary model complexity. This study proposes an Improved Genetic Algorithm (IGA) that extends the conventional Genetic Algorithm through elitism, adaptive crossover, and adaptive mutation operators to optimize feature selection for Random Forest-based heart disease classification. The proposed method was evaluated using the Cardiovascular Disease Dataset from Kaggle, which consisting of 1,000 records and 14 variables, where 12 predictor features were used for model development. The experimental procedure included data preprocessing, train-test splitting, class imbalance handling using SMOTE on the training set, feature normalization, Random Forest modeling, feature selection using the proposed IGA, and model evaluation. The proposed IGA selected six important features slope, chestpain, restingBP, restingelectro, oldpeak, and gender. The optimized Random Forest model achieved an accuracy of 99.50%, precision of 99.15%, recall of 100.00%, F1-score of 99.57%, and AUC-ROC of 99.90%. These findings indicate that feature selection can simplify the model without compromising classification performance, making the Random Forest + IGA approach a viable alternative for developing more efficient heart disease prediction models.
Analisis Sistem Pendukung Keputusan Pemilihan Merek Pasta Gigi Terbaik Menggunakan Metode SERVQUAL dan ORESTE Rahma Dhea Safitri; Anjar Wanto
BEES: Bulletin of Electrical and Electronics Engineering Vol 6 No 1 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v6i1.7713

Abstract

This study aims to address the problem of selecting the best toothpaste brand by developing a decision support system using a combination of the SERVQUAL and ORESTE methods. The problem raised is the difficulty consumers face in determining the most suitable product based on service quality. The SERVQUAL method is used to measure performance based on five dimensions of service quality, while ORESTE is used to rank alternatives without explicit weights. The system is implemented in the form of a data-driven evaluation model designed to mimic real-world conditions. The data reflects the perceived and expected values of several toothpaste brands, with the difference (GAP) calculated and processed using the ORESTE method to generate rankings. The results show that the Ciptadent brand received the highest preference with the lowest total ranking (6), followed by Oral-B and Colgate. The integration of these two methods enables a systematic and objective evaluation of overall service quality and can be used to support accurate consumer decision-making.
Co-Authors Solikhun Solikhun Timbo Faritcan Parlaungan Siallagan Wanto, Anjar