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Maori, Nadia Anissa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Penerapan SMOTE dan XGBoost untuk Klasifikasi Penyakit Ginjal Kronis pada Data yang Tidak Seimbang Syifa, Muhammad Ghufron; Maori, Nadia Anissa; Sucipto, Adi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 1 (2026): JANUARY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i1.5544

Abstract

Chronic kidney disease (CKD) is a global health issue that requires accurate diagnosis to prevent errors and unwanted side effects. This study aims to develop a reliable classification model using the XGBoost algorithm and to explore the effectiveness of the SMOTE method in addressing data imbalance. The dataset is sourced from the UCI Machine Learning Repository, consisting of 400 patient records with 25 attributes. The results indicate that the developed model performs well, with evaluation metrics (Accuracy, Precision, Recall, F1-Score, and AUC-ROC) nearing 1.0. The research findings reveal that the model without SMOTE is slightly superior, achieving an accuracy of 0.9874 compared to 0.9811 for the model with SMOTE. Analysis shows that the data imbalance is not significant, and XGBoost is resilient to unbalanced data. This study also identifies key factors influencing CKD diagnosis, such as hemoglobin and albumin, which can assist medical professionals in making more accurate diagnoses.
Pengaruh Optimasi Hyperparameter Random Forest terhadap Akurasi Prediksi Magnitudo Gempa Bumi Berdasarkan Hasil Klasterisasi DBSCAN Prasetyo, Rizky Dwi; Maori, Nadia Anissa; Zyen, Akhmad Khanif
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 1 (2026): JANUARY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i1.5555

Abstract

Indonesia is a country with high seismic activity due to its location at the convergence of three major tectonic plates. This condition creates a strong need for earthquake pattern analysis and magnitude prediction to support disaster mitigation. This study aims to cluster earthquake data using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and to predict earthquake magnitude using the Random Forest algorithm optimized through hyperparameter tuning. The Indonesian earthquake dataset was obtained from Kaggle with a total of 92,887 valid entries. The DBSCAN clustering results revealed several active seismic zones, particularly in Sumatra, Java, Sulawesi, and Papua. The comparison of R² between the Baseline Random Forest and the Tuned Random Forest shows a significant improvement after the parameter tuning process. The Tuned Random Forest model achieves an R² value of 0.478, which is higher than the Baseline Random Forest's 0.442. This indicates that the tuned model is better able to explain the variance in the data and provides more accurate predictions.
Co-Authors Adi Sucipto, Adi Akhmad Khanif Zyen, Akhmad Khanif Prasetyo, Rizky Dwi Syifa, Muhammad Ghufron