<![CDATA[Mutual funds are investment instruments that collect investors' funds, to be invested in securities within the mutual fund itself, with general parameters such as Net Asset Value (NAV) and time-bound returns. Both of these parameters have varying values, so they can act as a risk measure that can affect the profit of mutual funds. The effect of this risk makes people hesitate to invest in mutual funds because the level is not known based on those two parameters so that identification is involved to help determine the level of risk for mutual funds, which in this study used the integration of K-Means and Naive Bayes. The K-Means algorithm as a clustering algorithm is used to group mutual funds which then the results of the group into data classes to be classified by the Naive Bayes algorithm. The study used 250 mutual funds data on September 1, 2020, from the types of stock, money market, and mixed mutual funds. This study tested the number of clusters and the percentage amount of training data and test data. The test results showed that the optimal number of clusters was 4 with a global Silhouette Coefficient of 0,46448 and average of all classes from the evaluation of the classification model based on the best data amount percentage involving 4 classes in the form of precision of 0,9813, recall of 0,9818, and F-measure of 0,9808.]]>
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