Data Envelopment Analysis with Lower Bound on Input to Measure Efficiency Performance of Department in Universitas Malikussaleh - Dahlan Abdullah; Cut Ita Erliana; Muhammad Fikry (International Journal of Artificial Intelligence Research, 2020)
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