Data Envelopment Analysis with Lower Bound on Input to Measure Efficiency Performance of Department in Universitas Malikussaleh

International Journal of artificial intelligence research

View Publication Info
Field Value
Title Data Envelopment Analysis with Lower Bound on Input to Measure Efficiency Performance of Department in Universitas Malikussaleh
Creator Abdullah, Dahlan
Erliana, Cut Ita
Fikry, Muhammad

Description DEA has become one of the most appropriate methods for comparing the various Decision-Making Units (DMU) associated with public services such as universities.  There are two primary outputs that can be used to measure college performance, namely: the number of graduates and the number of publications.  While the primary input of college efficiency measurement is the number of teaching staff and the number of students.  The higher learning institution is  Universitas Malikussaleh, located in Lhokseumawe, a city in the Aceh province of Indonesia. This paper develops a method to evaluate the efficiency of all departments in Universitas Malikussaleh using DEA with bounded input. The extreme dissimilarity between the weights often found in DEA applications. In this paper, we develop a new DEA model, which can be transformed into a particular case of the bi-level linear program to calculate the lower boundary of input from the pessimistic viewpoints based on the shortcomings of the existing approaches. In the case of having a single input, providing lower bounds for the input weights by imposing the conditions that it uses for the average input level of the DMU being assessed uses per unit of output.  Accordingly, we present some essential differences inefficiency of those departments. Finally, we discuss the effort that should be made by these departments in order to become efficient
Publisher STMIK Dharma Wacana
Date 2020-04-09
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source International Journal of Artificial Intelligence Research; Vol 4, No 1 (2020): June; 58-64
Language eng
Rights Copyright (c) 2020 International Journal of Artificial Intelligence Research

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us


Copyright © 2015-2018 Simon Fraser University Library