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All Journal JTERA (Jurnal Teknologi Rekayasa) Jurnal Teknik Informatika (JUTIF)
Sumardin, A.
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Comparison of Random Forest and XGBoost for Diabetes Classification with SHAP and LIME Interpretation Mubaraqah, Mubaraqah; Puteri, Annisa Nurul; Sumardin, A.
JTERA (Jurnal Teknologi Rekayasa) Vol 9, No 2: December 2024
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v9.i2.2024.121-130

Abstract

Diabetes Mellitus (DM) merupakan tantangan kesehatan global yang membutuhkan pendekatan inovatif untuk deteksi dini dan manajemen yang efektif. Studi ini bertujuan untuk membandingkan algoritma Random Forest  dan XGBoost  dalam klasifikasi diabetes sambil meningkatkan interpretabilitas model menggunakan teknik AI yang Dapat Dijelaskan (XAI)  seperti SHAP dan LIME. Metodologi ini melibatkan pemrosesan kumpulan data publik yang berisi 70.000 entri dengan 34 fitur medis, melatih model dengan parameter yang dioptimalkan, dan melakukan analisis interpretatif. Hasil menunjukkan bahwa XGBoost mencapai akurasi yang lebih tinggi (90,6%) dengan generalisasi yang lebih baik, sementara Random Forest unggul dalam efisiensi pelatihan. Analisis fitur mengidentifikasi faktor-faktor utama seperti Usia, Kadar Glukosa Darah, dan Penambahan Berat Badan Selama Kehamilan sebagai kontributor signifikan terhadap prediksi. Temuan ini memberikan panduan model yang akurat dan transparan untuk mendukung pengambilan keputusan medis.
Enhanced Lung Cancer Detection Using ANN with Random Oversampling, RFE-Based Feature Selection, and GridSearchCV Hyperparameter Tuning Nurwafiqah, Nurwafiqah; Al Fiqran, M. Yudi; Puteri, Annisa Nurul; Arafah, Muhammad; Maslihatin, Tatik; Sumardin, A.
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.2026.7.2.5391

Abstract

Amid the most predominant mortality factors on a global scale, Lung cancer constitutes one of the most significant oncological burdens, chiefly because most patients receive a diagnosis only at later stages. The limitations of conventional diagnostic approaches underscore the urgent need for artificial intelligence–based detection systems that can improve both diagnostic accuracy and efficiency. This study aims to develop a lung cancer prediction model using an Artificial Neural Network (ANN) optimized through an integrated strategy that includes data preprocessing, class balancing via Random Oversampling (ROS), feature selection using Recursive Feature Elimination (RFE), and hyperparameter tuning with Grid Search. The evaluation of model effectiveness employs accuracy, precision, recall, F1-score, along with a confusion matrix. Experimental results demonstrate an accuracy of 98%, with average precision, recall, and F1-score values of 0.95. Statistical validation using McNemar’s test confirms a significant performance improvement over the baseline model (χ² = 18.05, p < 0.001), accompanied by a large effect size (Cohen’s h = 0.82). Furthermore, the model exhibits balanced performance in identifying both lung cancer and non-cancer cases, reflecting the effectiveness of the data balancing and feature selection strategies. These findings suggest that the optimized ANN model has strong potential as a foundation for a medical decision support system for early lung cancer detection, contributing to more reliable diagnoses and more accurate clinical decision-making.
Co-Authors Al Fiqran, M. Yudi Annisa Nurul Puteri Maslihatin, Tatik Mubaraqah, Mubaraqah Muhammad Arafah Nurwafiqah, Nurwafiqah