<![CDATA[Election integrity is a key element in ensuring democratic legitimacy, yet election fraud remains a significant threat to this process. This research aims to evaluate the use of Bayesian statistics in detecting anomalies in vote distributions that may indicate potential election fraud. In the context of elections, whose reliability is often questioned, statistical analysis becomes crucial to ensure transparency and credibility in election results. This study uses national election data collected from various polling stations (TPS), focusing on vote distribution per candidate in each TPS. A Bayesian probabilistic model is applied to analyze the observed vote distribution compared to the expected distribution. By using Bayesian inference, researchers can update the probability distribution based on the obtained data, allowing for more accurate anomaly identification. The analysis results show significant deviations in several TPS, where the observed vote distribution exhibits patterns inconsistent with the expected distribution. These anomalies are suspicious as they may indicate manipulation or errors in the vote counting process. The implications of this research suggest that the Bayesian method can be used to improve election transparency and provide a more reliable tool for detecting fraud. The future application of this technique can help increase public trust in election results and support the integrity of the democratic process. This study also recommends further development of the analytical model to expand the scope of anomaly detection and enhance fraud detection accuracy in larger and more complex elections.]]>
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