Quantitative Analysis of Training Completion Using Multivariate Linear Regression - Devianto, Yudo; Dwiasnati, Saruni; Gunawan, Wawan; Sumarto, Marco Alfan; Saputra, Dony Ramadhan (JURNAL RISET KOMPUTER (JURIKOM), 2025)
The urgency of this research stems from the strategic need to monitor and evaluate the achievements of digital training implemented by various academies under government coordination, including VSGA, FGA, DEA, TA, and GTA. In the context of the national digital transformation program, the availability of an analytical model that can predict the success of participants in completing training is critically crucial to support the achievement of the Ministry’s Key Performance Indicators (KPIs). The purpose of this study is to develop a predictive model based on multivariate linear regression that combines two main variables, the percentage of participants accepted and the percentage of participants who participate in onboarding, to project the level of training completion. This model is expected to provide a quantitative and objective assessment of the effectiveness of digital training implementation in each academy. The targeted outputs of this study include the development of a predictive model with performance validation through the calculation of R², which yielded a value of 0.9448, as well as the provision of technical reports and data-driven recommendations for enhancing digital training governance. The Technology Readiness Level (TKT) of this study is at TKT 3, and there is evidence of conceptual validation of the predictive model based on real data collected from the implementation of the training. This stage marks the readiness of the research to continue developing the system model and implementing it on the training evaluation platform in the next stage.