2009-2015 ©
             Publication
Journal Publication
Research Title Variable Selection in Data Envelopment Analysis Using Stepwise Modeling Approach: A Case Study of Tourism Sector in Indonesia 
Date of Distribution 9 August 2018 
Conference
     Title of the Conference 2018 International Conference on “Physics and Mechanics of New Materials and their Applications” (PHENMA 2018) 
     Organiser Korea Maritime and Ocean University, Busan, Republic of Korea; Southern Federal University, Russia; and National Kaohsiung Marine University, Taiwan, ROC. 
     Conference Place Korea Maritime and Ocean University  
     Province/State Busan, Republic of Korea 
     Conference Date 9 August 2018 
     To 11 August 2018 
Proceeding Paper
     Volume PHENMA 2018 
     Issue August 2018 
     Page 120 
     Editors/edition/publisher Korea Maritime and Ocean University, Busan, Republic of Korea 
     Abstract Data Envelopment Analysis (DEA) is a non-parametric technique for measuring the relative efficiency of a set of similar units, usually referred to as Decision Making Units (DMUs), which convert multiple inputs to multiple outputs. One of the interesting research subjects in DEA is to choose appropriate input and output indicators. Besides the choice of DEA technology (model), selecting inputs and outputs is the other crucial consideration, which the analyst must keep in mind. Unfortunately, there has been inadequate attention to this issue, while most attention has been given to building models. Several researchers have just chosen variables subjectively or calculated efficiency based directly on others’ selection results. In this paper will apply the stepwise modelling approach to conduct the variable selection in DEA. This method is intended to produce DEA models that include only variables with the largest impact on the DEA results. The backwards approach of stepwise modeling starts by considering all possible input and output variables in the DEA model. At each step, one variable is dropped from the model by analyzing the efficiency scores of the DMUs. Theoretically, the method can continue until only one input and one output variable remain in the model. From a practical viewpoint, stopping rules can be incorporated using the decision criterion to create a parsimonious DEA model. In this study, we illustrate these methods using datasets of a case study of tourism sector in Indonesia. 
Author
577040044-7 Mrs. ERNI PUSPANANTASARI PUTRI [Main Author]
Engineering Doctoral Degree

Peer Review Status มีผู้ประเมินอิสระ 
Level of Conference นานาชาติ 
Type of Proceeding Abstract 
Type of Presentation Oral 
Part of thesis true 
Presentation awarding false 
Attach file
Citation 0