Sensitivity Analysis of the New Model of Museum Education Performance Index with Machine Learning-Based Digital Technology Usage Parameters

Abstract

This paper presents the results of sensitivity analysis of a new model of museum educational performance index with machine learning-based digital technology usage parameters during the pandemic. Sensitivity analysis was aim to mmeasure the effectiveness of using machine learning-based digital technology to optimize museum educational performance during the COVID-19 pandemic until post-pandemic. A new model of the educational role performance index of the SMBII Palembang Museum is proposed in this study by adding parameters for the use of digital technology based on machine learning. Sensitivity analysis was conducted by proposing four scenarios of the parameter values, K1 to K4. The results of the sensitivity analysis show that with the addition of these new parameters, the performance index of the educational role of the SMBII museum has succeeded in improving. It is hoped that the use of machine learning-based digital technology to support the museum's educational role can continue to be improved after the pandemic because it has proven effective as a strategy to support museum performance during the pandemic.

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