Like many other innovations, randomization has been a key factor in various fields such as games, science, art, statistics, and other areas. But although treated as common knowledge, randomization is often misunderstood by users when they start to perceive relationships or connections between randomly distributed data, this tendency is known as apophenia. It is observed mostly when a randomized dataset shows the same elements in a row or in multiple repetitions, an occurrence that is plausible given the nature of randomization. Nevertheless, this may appear as biased or non-random to humans, thinking that since an element had appeared once or a couple of times then it should not appear next to or within a set amount of time, another user misunderstanding known as gambler’s fallacy.The Planned Random algorithm (PR algorithm) was created as a solution to this problem, as it introduces a non- random process to lessen the repetitions of data in the returned dataset and making the result less random to appear more random.This study aims to enhance the Planned Random algorithm by increasing its apparent randomness by further minimizing the repetition of data in a resulting dataset. 5 final test simulations were done for both the existing PR algorithm and enhanced PR algorithm wherein the enhanced algorithm showed a promising 22.22% decrease in both total and average repetitions compared to the existing algorithm.In conclusion, the enhanced algorithm had seen a significant increase in apparent randomness when compared to the existing PR algorithm.
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