<![CDATA[Clustering is a technique for grouping objects based on their similarities within clusters and their differences across clusters. In real-world, objects often have characteristics represented by a combination of numerical and categorical variables, requiring clustering techniques that can process mixed-type data. Model-based clustering is one of the approaches that can be utilized for such data. This study evaluates and compares two model-based clustering algorithms for mixed data type, Rmixmod, which employs a mixture model with maximum likelihood estimation and expectation-maximization, and KAMILA, which utilizes a semi-parametric approach. Both algorithms are implemented to cluster Indonesian higher education institutions based on their performance. The optimal number of clusters is determined using the Bayesian Information Criterion and the Silhouette Coefficient. Algorithms performance is evaluated using the Silhouette Coeeficient, the Calinski-Harabasz Index, and the Davies-Bouldin Index. The research results showed that the Rmixmod algorithm outperformed KAMILA in clustering Indonesian higher education institutions, with a Silhouette Coeeficient of 0.2878, a Calinski-Harabasz Index of 253.9433, and a Davies-Bouldin Index of 1.5321. The optimal number of clusters formed was five. Cluster interpretation is conducted by analyzing the mean values of PC and the distribution of categorical variables within each cluster. The clustering results are expected to serve as a foundation for the government in formulating strategic policies that are both effective and differentiated according to the characteristics of each group of higher education institutions.]]>
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