KLASTERISASI BANK-BANK UNIT PENYALUR KUR BERBASIS KINERJA KEUANGAN MENGGUNAKAN PRINCIPAL COMPONENT BIPLOT - Farida Farida (JABE (Journal of Applied Business and Economic), 2023)
This study aims to determine each BRI unit bank's group or relative position that distributes people’s business credit (KUR) internally based on their financial performance. This grouping is useful as a mapping of BRI unit banks as a reference for determining targets and competitive advantages. Bank units are grouped based on the similarity of characteristics between objects and the diversity of variables using a biplot principal component analysis. The location of research was carried out at BRI unit banks in Pati Regency, Central Java. Saturated sampling means that the population used in this study is 35 BRI unit banks in Pati Regency. The result of this research is that four groups/clusters are formed. The diversity between the four clusters is high, while the diversity within the clusters is small or homogeneous. Unit banks in each group have fairly close similarities compared to unit banks in other groups. None of the bank units in Cluster I and II dominate the performance parameters. Cluster III consists of 9 bank units, namely Dukuhseti (DS), Karaban (KB), Sukolilo (SL), Tambakromo (TK), Kayen (KY), Jaken (JK), Juwono II (J2), Pati 2 (PK2) and Kajar (KJ). In cluster 3, unit banks dominate the parameters of variables such as KUR disbursed, number of customers, and income. Meanwhile, unit banks in cluster IV dominate the cost and NPL parameters. The implication of this clustering is that unit banks in cluster 3 have good performance as a model for other unit banks. Meanwhile, banks that are included in cluster 4 should be more careful in attracting customers so that bad credit does not occur