<![CDATA[Accreditation is currently the main focus for all universities. Each institution strives to get superior accreditation. The evaluation and assessment process carried out by BAN-PT is based on data reported by universities to PDDikti. This research aims to assist universities in achieving superior accreditation, by providing recommendations regarding the most influential attributes and clustering to find patterns or data structures from PDDikti. This research uses two feature selection methods AHP and Chi-Square are used separately to identify the most influential attributes. The results of each method were used as input features for the clustering process using the BIRCH algorithm. The purpose of this approach is to evaluate the effect of feature selection from both methods on the quality of clustering results. The evaluation is done using the Davies-Bouldin Index (DBI) metric. The results showed that the Lecturer attribute has the highest eigenvalue in AHP which is 0.379, indicating its significant role in accreditation assessment. Meanwhile, the Year of Establishment Decree attribute has the highest Chi-Square value of 290.625 which indicates a strong correlation with accreditation results. In addition, based on the cluster DBI value, it shows that AHP is superior to chi-square, so AHP is considered more effective in this context. With the best Davies Bouldin Index (DBI) value of 0.73603 in cluster 7 with a threshold of 0.05 and a branching factor of 50.]]>
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