<![CDATA[Tuberculosis (TB) is a major health problem in many parts of the world, including in West Java Province, Indonesia. Accurate assessment of TB risk factors can improve overall TB control efforts. This study introduces modelling by integrating Negative Binomial Mixed Models (NBMM) and Random Forest (RF) called the Negative Binomial Mixed Model Random Forest (NBMMRF) model. This model is used to identify and assess risk factors associated with the incidence of tuberculosis. Firstly, using NBMM to add fixed effects and random effects in the model and compensate for overdispersion. Afterwards, we included a Random Forest component in the model, which helped us detect relevant predictive features and change model weights accordingly. The resulting Negative Binomial Mixed Model Random Forest has a high accuracy value of up to 0.915. After obtaining the appropriate model, forecasting of tuberculosis cases in 2024 was carried out and the forecasting result was 1904 cases, which increased from the number of cases in the previous year. The results of this study show the importance of various related parties to continue to be vigilant, pay attention to various related risk factors, and continue to make various efforts to study, prevent, and control Tuberculosis disease effectively.]]>
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