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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences
YILDIZ, Oktay
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Implementation of C5.0 Algorithm using Chi-Square Feature Selection for Early Detection of Hepatitis C Disease MAHMUD, Mahmud; BUDİMAN, Irwan; INDRİANİ, Fatma; KARTİNİ, Dwi; FAİSAL, Mohammad Reza; ROZAQ, Hasri Akbar Awal; YILDIZ, Oktay; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.384

Abstract

Hepatitis C, a significant global health challenge, affects 71 million people worldwide, with severe complications such as cirrhosis and hepatocellular carcinoma. Despite its prevalence and availability in rapid diagnostic tests (RDTs), the need for accurate early detection methods remains critical. This research aims to enhance hepatitis C virus classification accuracy by integrating the C5.0 algorithm with Chi-Square feature selection, addressing the limitations of current diagnostic approaches and potentially reducing diagnostic errors. This research explores the development of a machine learning model for hepatitis C prediction, utilizing a publicly available dataset from Kaggle. It encompasses preprocessing techniques such as label encoding, handling missing values, normalization, feature selection, model development, and evaluation to ensure the model's efficacy and accuracy in diagnosing hepatitis C. The findings of this study reveal that implementing Chi-Square feature selection significantly enhances the effectiveness of machine learning algorithms. Specifically, the combination of the C5.0 algorithm and Chi-Square feature selection yielded a remarkable accuracy of 96.75%, surpassing previous research benchmarks. This highlights the potent synergy between advanced feature selection techniques and machine learning algorithms in improving diagnostic precision. The study conclusively demonstrates that machine learning is an effective tool for detecting hepatitis C, showcasing the potential to enhance diagnostic accuracy significantly. As a future recommendation, adopting AutoML is suggested to periodically automate the selection of the optimal algorithm, promising further improvements in detection capabilities.
Applying XGBoost-ADASYN in the Classification Process of Bank Customers Who Will Take Time Deposits Abdilah, Muhammad Fariz Fata; Mazdadi, Muhammad Itqan; Farmadi, Andi; Muliadi, Muliadi; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.551

Abstract

Investment in the form of time deposits at banks offers stable returns. Identifying and attracting potential customers, however, poses challenges. This research enhances the predictive capabilities of deposit classification models by addressing data imbalance with a combination of XGBoost, ADASYN, and Random Search optimization techniques. The integration of ADASYN improves minority class representation, while Random Search efficiently optimizes model parameters. Our findings show a significant accuracy of 94.93%, benchmarked against baseline models, highlighting our method's effectiveness compared to traditional approaches. This hybrid model advances customer data analysis and achieves our research objectives. We discuss the integration challenges, including computational demands and technique selection. The research underscores the application of machine learning to address financial industry issues, emphasizing the impact of data preprocessing and feature engineering on performance. Future studies might explore AutoML to reduce complexity further and enhance model scalability, promising more innovation in customer data analysis.
Performance Comparison of AdaBoost, LightGBM, and CatBoost for Parkinson's Disease Classification Using ADASYN Balancing Anshari, Muhammad Ridha; Saragih, Triando Hamonangan; Muliadi, Muliadi; Kartini, Dwi; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4726

Abstract

Parkinson's disease is a neurodegenerative condition identified by the decline of neurons that produce dopamine, causing motor symptoms such as tremors and muscle stiffness. Early diagnosis is challenging as there is no definitive laboratory test. This study aims to improve the accuracy of Parkinson's diagnosis using voice recordings with machine learning algorithms, such as AdaBoost, LightGBM, and CatBoost. The dataset used is Parkinson's Disease Detection from Kaggle, consisting of 195 records with 22 attributes. The data was normalized with Min-Max normalization, and class imbalance was resolved with ADASYN. Results show that ADASYN-LightGBM and ADASYN-CatBoost have the best performance with 96.92% accuracy, 97.10% precision, 96.92% recall, and 96.92% F1 score. This improvement suggests that combining boosting methods and data balancing techniques can improve the accuracy of Parkinson's diagnosis. These results demonstrate the effectiveness of ADASYN in addressing data imbalance and improving the performance of boosting algorithms for medical classification problems. The findings contribute to the development of intelligent diagnostic systems in the field of medical informatics and computer science. These findings are essential for developing more accurate and efficient diagnostic tools, supporting early diagnosis and better management of Parkinson's disease.
Implementation of Ant Colony Optimization in Obesity Level Classification Using Random Forest Wardana, Muhammad Difha; Budiman, Irwan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4696

Abstract

Obesity is a pressing global health issue characterized by excessive body fat accumulation and associated risks of chronic diseases. This study investigates the integration of Ant Colony Optimization (ACO) for feature selection in obesity-level classification using Random Forests. Results demonstrate that feature selection significantly improves classification accuracy, rising from 94.49% to 96.17% when using ten features selected by ACO. Despite limitations, such as challenges in tuning parameters like alpha (α), beta (β), and evaporation rate in ACO techniques, the study provides valuable insights into developing a more efficient obesity classification system. The proposed approach outperforms other algorithms, including KNN (78.98%), CNN (82.00%), Decision Tree (94.00%), and MLP (95.06%), emphasizing the importance of feature selection methods like ACO in enhancing model performance. This research addresses a critical gap in intelligent healthcare systems by providing the first comprehensive study of ACO-based feature selection specifically for obesity classification, contributing significantly to medical informatics and computer science. The findings have immediate practical implications for developing automated diagnostic tools that can assist healthcare professionals in early obesity detection and intervention, potentially reducing healthcare costs through improved diagnostic efficiency and supporting digital health transformation in clinical settings. Furthermore, the study highlights the broader applicability of ACO in various classification tasks, suggesting that similar techniques could be used to address other complex health issues, ultimately improving diagnostic accuracy and patient outcomes.
Co-Authors Abdilah, Muhammad Fariz Fata Andi Farmadi Anshari, Muhammad Ridha Dodon Turianto Nugrahadi Dwi Kartini, Dwi Fatma Indriani Irwan Budiman Mahmud Mahmud Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Rozaq, Hasri Akbar Awal Saputro, Setyo Wahyu Saragih, Triando Hamonangan Wahyu Caesarendra Wardana, Muhammad Difha