Journal of Contemporary Management Sciences
Volume 1 (3) 25-33
JCMS Publication, 2014
Journal of
Contemporary
Management
Sciences
THE EFFICIENCY COMPARISON OF
INDONESIANUNIVERSITIES OF EDUCATION USING DATA
ENVELOPMENT ANALYSIS
MOCH ALIP & HANDARU JATI
moch_alip@uny.ac.id & handaru@uny.ac.id
Abstract
The increasing demand of the senior high school students to go to the university and the higher costs incurred to
pursue the higher education are the factors that encourage the Indonesian university to operate more efficiently. This
research aimsare to analyze and compare the efficiency of Indonesian Education universities by using Data
Envelopment Analysis.
This research consists of activities which are needs analysis, the assessment of variables, data collection mechanism,
design of efficiency model and implementation of the analysis for twelve Education Universities in Indonesia.
The more output is generated using the input bit will improve the efficiency of the university. The result of
calculations indicates that the Education Universities in Java have averaged better efficiency compared with the
Education Universities outside Java.
Keywords: Efficiency, Education University, Data Envelopment Analysis
I. INTRODUCTION
1.1 Background
Performance of Higher Education Institutions has been the subject of growing attention in recent years. The question
of how public resources should be allocated in higher education has resulted in the need for models and mechanisms
that will be able to measure the highereducationinstitution efficiency. Each country has its own policyon Higher
Education funding arrangements and resource allocation structure. The determination for the single university
tuition fee is also a matter to be raised at the Indonesia Ministry of Education and Culture. Several studies have been
conducted to address this issue at the international level. The experiment was conducted by using statistical analysis
to measure the performance, while others use non-statistical instruments.The analysis of efficiency of the education
sector varies substantially. Therefore it is necessary to be careful in determining the performance indicators for the
analysis of efficiency for higher education. There are two main aspects in the world of higher education: first,
educational institutionsoperate under different environmental conditions which are public and private university.
Second, the education production sector contains many inputs and outputs.According to data in 2013, there are 92
state universities , and thousands of private universities in Indonesia (www.dikti.go.id).
Higher education institutions should be treated as a unit with the same productive measurement as a business unitto
evaluate the performance which requires inputs to achieve a given level of output. Some examples of the
performance indicator used are the proportion of students in a given year and the tuition cost per student as an input
or the number of students who graduate every year and graduate waiting time for working in the first time as an
output . Input and output are needed to determine the efficiency in Higher Education Institutions. In addition, some
special things attached to the education sector should also be taken into account when calculates the efficiency. The
purpose of this study are to obtain the model and calculate the efficiency of higher education institutions and then
also rank from most to least efficient institution which will be used to provide an input to the government in order to
determine the optimal allocation of resources to the highereducation institution.In particular, the purpose of this
study also want to develop a model that can be used to calculate the efficiency in Indonesian education universities
using Data Envelopment Analysis method which will be developed as an important information for Indonesian
goverment to improve the quality and objectivity of performance appraisal in the University of Indonesia (Altbach,
2004).
II . LITERATURE REVIEW
2.1 Efficiency Assessment of Higher Education
The study of university efficiency assessment has been carried out in various countries, mostly by applying the
method of Data Envelopment Analysis (DEA). Result of the study using three inputs and three outputs carried outin
the United States shows that the State universities have higher efficiency than private universities (Ahn et al., 1988).
In a separate study by applying the DEA model with five inputs and six outputs factors ( Rhodes and Southwick,
1986) demonstrates the efficiency of public university 96 lower than the 54 private universities in the United States.
Data Envelopment Analysis (DEA) is also used to assess the efficiency of the 25 best U.S. universities (Breu and
Raab, 1994) and showed that DEA is the correct method for measuring the efficiency of higher education. DEA
method is also used in the calculation of the efficiency of several universities in Norway in 1994, 1995 and 1996
(Fø rsund and Kalhagen, 1999), the scale of the technical efficiency of Australian universities (Abbott and
Doucouliagos, 2003), the efficiency of public university of Portugal in 2003 (Afonso and Santos, 2005), the
technical efficiency of 45 England universities from 1980 to 1981 and 1992/93 (Flegg et al., 2003), differences in
the efficiency of the group 210 higher education institutions from eight European countries using a sample of
graduate students for more than three years (Joumady and Ris, 2005). Year period was chosen primarily because it is
a time of substantial change in public finance in the UK. This study noted a significant increase in technical
efficiency, especially between the years 1987-1988 and 1990-1991. Inputs used in the analysis come from the
number of lecturers and university tuition fees and the output are based on graduation rates and the number of
doctoral theses.The findings suggest the average efficiency index between 55.3% and 67.8%. The results showed the
homogeneity of performance for the university system as a whole.
2.1.2. Data Envelopment Analysis
Data Envelopment Analysis (DEA) is a non-parameter approach for evaluating the performance of a set of
homogeneous entities called Decision Making Units (DMU) where there are many inputs and outputs, each of which
has different weight(weighted multiple inputs and weighted multiple outputs). This method was first discovered by
Charnes, Cooper and Rhodes (CCR) (Charnes et al., 1978) with the aim to maximize the efficiency of the DMU is
assessed from some set of entities. The term decision making units can be used to represent non-profit institutions
such as schools, hospitals, government. The output of the institution will usually be difficult to quantify the value of
money. In this study, the DMU ill represent the university. The weight of each input and output is highly variable
whose value is determined from the significance of the inputs and outputs of the DMU. DMU will have a value of
100% efficiency when it is in the most forward position. Comparative efficiency value will be given by the
following conclusions and DEA efficiency score of each DMU. Efficiency assessment factors are influenced by the
amount of input and output variables. The calculation of the efficiency of universities largely apply methods Data
Envelopment Analysis (DEA)(Ahn et al., 1988).
III. METHODS
3.1. Research Design
This research is carried out in three stages. The first stage is the development of efficiencyassessment model, the
second stage is the evaluation of the efficiency by using the software, and the third stage is the implementation of
efficiency assessment model in 12 universities education in Indonesia. This study will focus on the efficiency
analysis of the university education in Indonesia by using DEA method.
3.2 Samples
12 public universities Education in Indonesia was used in this study, including six universities in Jawa (The most
developed part in Indonesia) and six universities in other island (less developed). The universities are namely : UPI
Bandung , UNJ Jakarta , UNNES Semarang , Surabaya UNESA , UNY Yogyakarta , Malang and six other from
outside Java, which are UNP Padang , UNM Makassar, UnimedMedan , Manado State University,
UndhiksaSingaraja and UNG Gorontalo .
3.3 Data Collection Procedures
The data was collected by using the instruments available on the Internet and the primary data from the Higher
Education institutions, the following table 1 shows the number of variables, the category boundaries of these
indicators, as well as the tool being used to measure the indicator. The process of data retrieval from the web is
using some of the official sites owned by the Higher Education and the Ministry of Education and also Google
Scholar. Some output variables are also obtained through the Google search engine to obtain the number of papers
and journals that are published for each university with regard to productivity of academia. In Table 1 explains the
variables, types of variables, constraints and tools to collect data.
Table 1. Variables Research and Measurement Tools
No
1
types of
variable
variables
number of professors
Input
(Coelli , 1998)
2
Input
The number of tenured
Data Dikti
faculty
http://pdpt.dikti.go.id/
Data from Dikti
undergrade and
http://pdpt.dikti.go.id/
graduate students
The productivity of the
The number of teaching
learning process
output
(flegg, 2003)
4
tools
The number of
The number of active
students (flegg, 2003)
3
Constraints
materials and
Scholar.google.com
publications
The number of
The number of
accredited study
output
program in
accredited Study
program held in
ban-pt.kemdiknas.go.id
university
3.3.1 Model Calculation of Efficiency
3.3.1.2 Data Envelopment Analysis
If there are four inputs of the DEA then there will be four linear programming equations to be solved for each DMU
(in this case the university) to determine the distance :
D0t x t 1 , u t 1 / CRTS   Max 
1
subjectuntuk
 x
t
i
 x t 1
 u
t
i
 u t 1
i
i
----- (1)
0
D0t 1 x t , u t / CRTS   Max 
1
subjectuntuk
 i xit  x t
 u
i
t
i
 u t
0
D0t 1 x t 1 , u t 1 / CRTS  Max 
1
----- (2)
subjectuntuk
 x
 u
i
t 1
i
 x t 1
i
t 1
i
 u t 1
------ (3)
0
D0t x t , u t / CRTS  Max 
1
subjectuntuk
 x
 u
i
t
i
 xt
i
t
i
 u t
------ (4)
0
Where K , N , M , and T represent the number of Universities, input, output and timeperiods sampled.In this study,
K = 12 , N = 2 , M = 2 and T = 1 , Time Period : t = 2013, and  ' s is the intensity parameter. Fourth of linear
equations required for each unit of production ( total production from each of the university's research ) .
IV. RESULTS
DEA uses the ratio of total factor productivity to measure the performance (a single ratio with all inputs and
outputs). DEA gives weight to each input and output variables. Each entity of the DMU is measured by using a
linear optimization process that tries to get the maximum value of the ratio of each entity by finding the best weight
value for each entity. The data were obtained by using a page of information provided by the Higher Education:
http://pdpt.dikti.go.id,
http://ban-pt.kemdiknas.go.id,
and
http://scholar.google.com.
Table 2 below are the data that were obtained from official sources about the input and output variables needed in
the assessment of the efficiency of a university.
Table 2 . Results Collecting data on the input and output of the University
Universitas
The number of
The number of
Number of
Number of
tenured
students (input )
publications /
accredited study
university
bibliometric
programs (
staff (input )
(output )
outputs )
Padang State University
992
31.426
13500
12/69 = 0,1739
Malang State University
901
23.653
43100
30/78 = 0,3846
Indonesia University of
1.301
28.948
122000
56/114 = 0,4912
Manado State University
943
14.881
2980
0/43 = 0
Makassar State University
874
23.540
6630
8/69 = 0,1159
Jakarta State University
944
21.003
79000
22/81 = 0,2716
1.024
22.692
61800
29/82 = 0,3537
837
23.237
28400
15/72 = 0,2083
Education
Yogyakarta State
University
Surabaya State University
State University of Medan
State University of
942
15.661
29800
8/48 = 0,1667
1.026
29.316
56900
22/77 = 0,2857
623
16.773
6260
0/53 = 0,1739
419
13.347
4030
1/42 = 0,3846
Semarang
State University of
Gorontalo
Universitas Pendidikan
Ganesha
Calculation to obtain the level of efficiency of the University Education in Indonesia performed using
OSDEA software and this software capablesin calculating several types of DEA method . Figure 1 is the initial view
of the OSDEA software.
Figure 1 . Open Source DEA ( OSDEA )
The fourth variable are the basis for the calculations, and those are the number of professors, the number of active
students (undergraduate and graduate students) as the input and the number of publications produced by
academician on the internet and a percentage of the accredited study program in the university as the output. The
calculation of the university efficiency is approach by implementing CCR method the main model. Figure 2 shows
the calculation process to the data for 12 universities by DEA method.
Figure 2 . Process with the DEA calculation OSDEA
All universities that do not have the level 1 of efficiency should strive to be efficient in a way : reducing inputs
while maintaining a constant output (this is an input-oriented approach), increase output while maintaining a
constant input ( this is an output-oriented approach, or a third model which seeks to reduce input and increase
output.
Table 2 . Results LPTKs University Efficiency Calculation
Category of
DMU Name
Objective Value
Eficiency
Padang State University
0,410679875
Malang State University
1
Yes
Indonesia University of Education
1
Yes
Manado State University
0,047516329
Makassar State University
0,310661557
Jakarta State University
0,892492329
Yogyakarta State University
0,918591644
Surabaya State University
0,596574393
State University of Medan
0,627301486
State University of Semarang
0,706778217
From the calculation results in Table 2 shows that the Malang State University Indonesia and Indonesia University
of Education are the two universities with the highest efficiency rating in Indonesia, by consecutive followed by
Yogyakarta State University, State University of Jakarta, Semarang State University, and the State University of
Medan. Sixth place is occupied by Surabaya State University and the seventh to twelfth occupied by the University
Education outside of Java, it shows the average efficiency University Education in Java is still better than the
average efficiency University Education outside Java .
IV . CONCLUSION
The calculation of the efficiency of a university is strongly influenced by the ratio of output and input. The more
output are generated by using the available input will improve the efficiency of the university.Calculation of the
efficiency shows that the efficiency of the University Education in Java is generally better than the University
Education
outside
Java.
REFERENCES
Abbott, M. & Doucouliagos, C. (2003). The efficiency of Australian universities: a data envelopment analysis.
Economics of Education review, Vol. 22, No. 1, pp. 89-97.
Afonso, A. & Santos, M. (2005). Students and teachers: a DEA approach to the relative efficiency of Portuguese
universities. NEP: New Economics Papers Education.
Ahn, T., Charnes, A. & Cooper, W.W. (1988). Some statistical and DEA evaluations of relative efficiencies of
public and private institutions of higher learning. Socio-Economic Planning Sciences, Vol. 22, No. 6,
pp. 259-269.
Altbach, P.G. (2004). The costs and benefits of world-class universities. Academe, Vol. 90, No. 1, pp. 20-23.
Breu, T.M. & Raab, R.L. (1994). Efficiency and perceived quality of the nation's “top 25― National
Universities and National Liberal Arts Colleges: An application of data envelopment analysis to higher
education. Socio-Economic Planning Sciences, Vol. 28, No. 1, pp. 33-45.
Coelli, T., Prasada Rao, D. S., & Battese, G. E. (1998). An introduction to efficiency and productivity analysis.
Boston: Kluwer Academic.
Charnes, A.W., Cooper, W.W. & Rhodes, E. (1978). Measuring Efficiency of Decision Making Units. European
Journal of Operational Research, Vol. 2, pp. 429-444.
Førsund, F.R. & Kalhagen, K.O. (1999). Efficiency and productivity of Norwegian colleges. Memorandum,
Department of Economics, University of Oslo.
Flegg, A.T., Allen, D.O., Field, K. & Thurlow, T.W. (2003). Measuring the efficiency and productivity of
British Universities: an application of DEA and the Malmquist approach. University of the West of
England, Department of Economics, series Discussion Papers, No. 304.
Joumady, O. & Ris, C. (2005). Determining the relative efficiency of European Higher Education institutions
using DEA. University of New Caledonia, ROA Maastricht University.
Rhodes, E.L. & Southwick, L. (1986). Determinants of efficiency in public and private universities. Department
of Economics, University of South Carolina.
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of
operational research, Vol. 130, No. 3, pp. 498-509.