<![CDATA[The one of the trading instruments is forex or foreign exchange. The forex market provides various commodities, one of which is XAU/USD. XAU/USD dataset with daily, h4, h1 time frames obtained from the MetaTrader 5 application via the FBS broker. Predicting forex prices is difficult because there are various factors that influence it, so data mining methods are needed to predict increases or decreases. Naïve Bayes is a method and logic that can be applied in making predictions. So the research objective of this final project is to apply naïve Bayes methods and logic in predicting the price of XAU/USD on the daily, h4, h1 time frames. The application of the Naïve Bayes method uses several libraries to support research, namely pandas, jcopml, and sklearn. In naïve Bayesian logic research, this is called from the sklearn library using gaussianNB. In this study, the performance reference uses an f1 score matrix because the number of false positives and false negatives is not tight (symmetrical). This study produces values for each time frame obtained from the confusion matrix formula with f-scores of 49.99% (daily), 53.52% (h4), 55.44% (h1).]]>
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