  The lowering of the energy threshold in the NaI detector is crucial not only
for comprehensive validation of DAMA/LIBRA but also for exploring new
possibilities in the search for low-mass dark matter and observing coherent
elastic scattering between neutrino and nucleus. Alongside hardware
enhancements, extensive efforts have focused on refining event selection to
discern noise, achieved through parameter development and the application of
machine learning. Acquiring pure, unbiased datasets is crucial in this
endeavor, for which a waveform simulation was developed. The simulation data
were compared with the experimental data using several pulse shape
discrimination parameters to test its performance in describing the
experimental data. Additionally, we present the outcomes of multi-variable
machine learning trained with simulation data as a scintillation signal sample.
The distributions of outcomes for experimental and simulation data show a good
agreement. As an application of the waveform simulation, we validate the
trigger efficiency alongside estimations derived from the minimally biased
measurement data.
