Apply Monte Carlo Simulation to Synthesize Data
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This research presents the data synthesization by using monte carlo simulation. Six datasets were synthesized and categorized into two types: (1) datasets with more categorical variables than numerical variables, and (2) datasets with more numerical variables than categorical variables. Synthesize data 1500 rows for each dataset then compared between real data and data synthesization using 1) The Kolmogorov-Smirnov Two-Sample Test, 2) T-test, 3) Cosine Similarity Test, 4) Multiple Linear Regression Analysis, and 5) Direct Data Comparison. The results showed that the Monte Carlo method was the most efficient for synthesizing data, especially for categorical variable data. Based on the coefficients of determination, the Monte Carlo simulation was 60.47% more efficient than Generative Adversarial Networks (GANs) and 52.41% more efficient than Variational Autoencoders (VAEs). Additionally, the Monte Carlo simulation method allows for adjustments to better represent the population in cases where the sample group does not fully cover it.
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