Proceedings of the 4th International Conference on Social Science, Humanity and Public Health, ICoSHIP 2023, 18-19 November 2023, Surabaya, East Java, Indonesia

Research Article

Optimizing Credit Score Assessment for Supervision and Administrative Control of E-Government-Based Personnel Analysts

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  • @INPROCEEDINGS{10.4108/eai.18-11-2023.2342550,
        author={Syamsul  Arifin and Pramuditha Shinta Dewi Puspitasari and Faisal Lutfi Afriansyah},
        title={ Optimizing Credit Score Assessment for Supervision and Administrative Control of E-Government-Based Personnel Analysts},
        proceedings={Proceedings of the 4th International Conference on Social Science, Humanity and Public Health, ICoSHIP 2023, 18-19 November 2023, Surabaya, East Java, Indonesia},
        publisher={EAI},
        proceedings_a={ICOSHIP},
        year={2024},
        month={1},
        keywords={e-governance credit score assessment data analytics risk assessment models decision making},
        doi={10.4108/eai.18-11-2023.2342550}
    }
    
  • Syamsul Arifin
    Pramuditha Shinta Dewi Puspitasari
    Faisal Lutfi Afriansyah
    Year: 2024
    Optimizing Credit Score Assessment for Supervision and Administrative Control of E-Government-Based Personnel Analysts
    ICOSHIP
    EAI
    DOI: 10.4108/eai.18-11-2023.2342550
Syamsul Arifin1,*, Pramuditha Shinta Dewi Puspitasari1, Faisal Lutfi Afriansyah1
  • 1: Politeknik Negeri Jember
*Contact email: syamsul.arifin@polije.ac.id

Abstract

In the rapidly evolving landscape of e-governance, effective personnel management is essential for ensuring the efficiency and integrity of public service delivery. This study explores the optimization of credit score assessment as a crucial tool for supervisory and administrative control of personnel analysts operating within e-governance frameworks. The research investigates the key factors and methodologies involved in credit score assessment, specifically tailoring these techniques to the unique requirements of e-governance settings. By leveraging data analytics, machine learning, and risk assessment models, this study aims to enhance the decision-making process for personnel analysts' performance evaluation, resource allocation, and overall management. The findings of this research have the potential to significantly contribute to the improvement of e-governance practices by facilitating more informed and data-driven decisions regarding personnel analysts' roles and responsibilities.