Evaluasi Kinerja Model Random Forest dan LightGBM untuk Klasifikasi Status Imunisasi Hepatitis B (HB-0) pada Balita

Ummul Auliyah Syam; Irdayanti Irdayanti; Indah Magfirrah; Bagus Sartono; Aulia Rizki Firdawanti

doi:10.37905/euler.v13i1.29762

Evaluasi Kinerja Model Random Forest dan LightGBM untuk Klasifikasi Status Imunisasi Hepatitis B (HB-0) pada Balita

Ummul Auliyah Syam, Irdayanti Irdayanti, Indah Magfirrah, Bagus Sartono, Aulia Rizki Firdawanti

Abstract

Hepatitis B (HB-0) immunization in infants is an important step in preventing the transmission of hepatitis B from an early age and improving public health. This study aims to classify the HB-0 immunization status of infants in West Java Province. The method used is the Random Forest and LightGBM algorithms. The research results showed that the Random Forest model had a balanced accuracy of 0.8443, which was slightly higher than LightGBM (0.8357). This indicated that Random Forest performed better in classifying the HB-0 immunization status of infants in West Java Province, accurately distinguishing between those who received and did not receive the immunization without bias toward either class. The global analysis using the Random Forest model identified six feature importance that contributed the most to the model’s performance: BCG immunization status, ownership of the KIA/KMS book, mother’s age, household head’s age, age at first pregnancy, and regency or city classification of residence. The feature importance analysis using SHAP for the first observation showed that BCG immunization status, ownership of the KIA/KMS book, and regency or city classification of residence increased the likelihood of infants receiving immunization. Conversely, the number of children (4), mother’s age (37 years), and household head’s age (40 years) increased the likelihood of infants not receiving immunization. This study is expected to provide data-driven insights for the government to design more effective interventions to improve immunization coverage and child health in Indonesia while also supporting the achievement of global health targets.

Keywords

Classification; Interpretability; SHAP

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DOI: https://doi.org/10.37905/euler.v13i1.29762