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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Hasan Ibrohim
Universitas Islam Nahdlatul Ulama Jepara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Pemodelan Hybrid untuk Prediksi Risiko Keparahan Penyakit Tuberkulosis Menggunakan Algoritma K-Means dan Random Forest Hasan Ibrohim; Harminto Mulyo; Gentur Wahyu Nyipto Wibowo
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 3 (2026): JULY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i3.6379

Abstract

Tuberculosis (TB) remains a major infectious disease in Indonesia, while the identification of patient severity levels in healthcare facilities is often time-consuming due to manual assessment of medical records. At Puskesmas Bonang 1, TB cases increased from 41 in 2023 to 57 in 2024, yet no data-driven analytical system is available to support rapid and objective risk evaluation. This study utilizes 2,546 TB patient medical records from 2023–2024 and applies preprocessing, normalization, encoding, clustering using K-Means, and the development of both baseline and hybrid models. The evaluation results indicate that the Hybrid K-Means + Random Forest model with hyperparameter tuning outperforms the standalone Random Forest model. The baseline Random Forest achieved an accuracy of 81.72% with an F1-Score of 80.98%, while the Hybrid + Tuning model obtained an accuracy of 82.51% and an F1-Score of 81.34%. This improvement demonstrates that cluster-based features extracted using K-Means successfully enhance data representation and improve the predictive performance of Tuberculosis severity risk classification.
Co-Authors Mulyo, Harminto Wibowo, Gentur Wahyu Nyipto