<![CDATA[Data Envelopment Analysis (DEA) is a method considered to evaluate a company's performance. DEA applies multiplies the input and output variables for analyzing the efficiency but does not provide guidance in selecting those variables. As a rule, researchers use several methods. If the number of variables used is too many, it will affect the efficiency value. This will reduce the strength of the efficiency value, which can cause all DMU values to be efficient. DEA and variable selection are important in performance evaluation because DEA aids in determining relative efficiency, whereas variable selection guarantees that the evaluation is based on the most relevant and significant aspects. The purpose of this study is to suggest the variable combination method for subtracting the number of variables that will be utilized in implementing the DEA. The method used in this study is the Average Input Variable Combinations (VCs)-Variable Returns-to-Scale (VRS) DEA. The data were classified, defined, and processed with a view to computing efficiency scores and DMU classifications. The research result indicated that the proposed method (VCs-DEA) treats the variable reduction factor and the average calculation factor to obtain the final result of the efficiency score. These two factors contribute to the accuracy of the efficiency value. Some real-world implications of these findings, such as making better use of resources, streamlining operations, and coming up with new plans, Furthermore, the evidence may be used to benchmark performance as well as help decision-makers in creating more effective policy. This study finds that only 1 out of 12 DMUs is efficient (8%), while the remaining 11 are inefficient (92%). Indonesia quarrying establishment can be classified into 3 categories such as Optimal Category (S-Sand); Middle Category (LS-Lime-Stone; F-Feldspars; Gr-Granite; SA-Stone and Andesite; K-Kaolin; Q-Quartz; and G-Gravel); and Less Category (So-Soil; C-Clay; M-Marble; and O-Others).]]>
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