Published: 2026-01-01

Penerapan SMOTE dan XGBoost untuk Klasifikasi Penyakit Ginjal Kronis pada Data yang Tidak Seimbang

DOI: 10.35870/jtik.v10i1.5544

Muhammad Ghufron Syifa, Nadia Anissa Maori, Adi Sucipto
  • Muhammad Ghufron Syifa: Universitas Islam Nahdlatul Ulama Jepara
  • Nadia Anissa Maori: Universitas Islam Nahdlatul Ulama Jepara
  • Adi Sucipto: Universitas Islam Nahdlatul Ulama Jepara
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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.

Keywords

Chronic Kidney Disease ; XGBoost ; SMOTE

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How to Cite

Syifa, M. G., Maori, N. A., & Sucipto, A. (2026). Penerapan SMOTE dan XGBoost untuk Klasifikasi Penyakit Ginjal Kronis pada Data yang Tidak Seimbang. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 10(1), 398-404. https://doi.org/10.35870/jtik.v10i1.5544
Article Information

This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 10 No. 1 (2026)

  • Section: Computer & Communication Science

  • Published: %750 %e, %2026

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Author Biographies
Muhammad Ghufron Syifa
Google Scholar ResearchGate ORCID

Program Studi Tekni Informatika, Fakultas Sains dan Teknologi, Universitas Islam Nahdlatul Ulama Jepara, Kota Jepara, Provinsi Jawa Tengah, Indonesia.

Nadia Anissa Maori
Google Scholar ResearchGate ORCID

Program Studi Tekni Informatika, Fakultas Sains dan Teknologi, Universitas Islam Nahdlatul Ulama Jepara, Kota Jepara, Provinsi Jawa Tengah, Indonesia.

Adi Sucipto
Google Scholar ResearchGate ORCID

Program Studi Tekni Informatika, Fakultas Sains dan Teknologi, Universitas Islam Nahdlatul Ulama Jepara, Kota Jepara, Provinsi Jawa Tengah, Indonesia.

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