Analysis of the Assistance Model for LKPM Through the Zoom Application at the DPMPTSP of South Lampung Regency using XGBoost
DOI:
https://doi.org/10.30871/jaic.v10i2.12435Keywords:
XGBoost, LKPM, Zoom, DPMPTSP, Public Service AssistanceAbstract
This study aims to analyze the success of LKPM mentoring through the Zoom application at DPMPTSP South Lampung Regency using the XGBoost model. The research identifies key features influencing the success of mentoring, including session duration, number of questions, and user satisfaction. The success of mentoring in this study is defined operationally by the extent to which participants engage with the mentoring process, as measured by the duration of interaction, frequency of questions asked, and the satisfaction levels reported by the participants regarding the mentoring quality. Additionally, the study applies Optuna hyperparameter optimization to improve model performance. The results show a significant increase in model accuracy, achieving 87.16%, with improvements in Precision, Recall, and F1-Score compared to the basic model. The findings suggest that longer Zoom sessions, greater participant interaction through questions, and higher user satisfaction contribute to more effective mentoring outcomes. These results provide actionable insights for DPMPTSP South Lampung Regency to refine mentoring strategies, improve session interactivity, and enhance public service delivery. This study establishes a foundation for data-driven strategies to improve the LKPM mentoring process. Furthermore, the study highlights the importance of continuous optimization and feature engineering to improve model predictions. By leveraging advanced machine learning techniques like XGBoost and Optuna, the model demonstrated high adaptability and predictive power. The research contributes to improving mentoring systems in public services by applying data science methodologies to real-world government programs.
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