Construction is an activity undertaken by a group of people to create the physical building needed to meet human needs. Construction companies will not survive intense competition unless they can play efficiently. This study examines the efficient construction company with DEA (Data Envelopment Analysis) and continued by looking at the characteristics of efficient construction companies in Southeast Asia using classification analysis. Many domestic building construction companies in southeast Asia are inefficient than efficient companies. This is due to company inputs that are higher than company output. Efficient domestic building construction there are 12.6% of companies and inefficient there is 87.4% in Southeast Asia.
Published In:IJCSN Journal Volume 6, Issue 6
Date of Publication : December 2017
Pages : 771-776
Tables : 02
Ray Tamtama : graduate student Statistics, Bogor Agricultural University. His main interests are in data mining and time series.
Bagus Sartono : Department of Statistics, Bogor Agricultural University Bogor, Indonesia.
Asep Saefuddin : Department of Statistics, Bogor Agricultural University Bogor, Indonesia.
Construction, DEA, Classification Analysis
Many domestic building construction companies in southeast Asia are inefficient than efficient companies. This is due to company inputs that are higher than company output. efficient domestic building construction there are 12.6% of companies and inefficient there is 87.4% in Southeast Asia. Efficient building construction companies are less efficient than inefficient ones.
 F. Firmawan, F. Othman, and K. Yahya, “Framework for Green Construction Assessment: a Case Study of Government Institution Building Project in Jakarta, Indonesia”, Journal of Emerging Trends in Engineering and Applied Sciences, 3(4):576-580, 2012.  J. L. Park, S. S. Kim, S. Y. Choi, J. H. Kim, and J. J. Kim, “Measuring Relative Efficiency of Korean Construction Company Using DEA/Window”, International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, 5(12):691-695, 2011.  C. Langston, “The Application Of Data Envelopment Analysis to The Benchmarking of Construction Performance in Australian and American High-Rise Buildings”, International Journal of Construction Management, 13(3):55-75, 2013.  J. Nazarko, and E. Chodakowska, “Measuring productivity of Construction Industry in Europe with Data Envelopment Analysis”, Procedia Engineering, 122:204-212, 2015.  J. L. Park, S. S. Kim, S. K. Yoo, J. H. Kim, and J. J. Kim, “Comparing The Efficiency and Productivity of Construction Firms in China, Japan and Korea Using DEA and DEA-Based Malmquist”, Journal of Asian Architecture and Building Engineering, 14(1):57-64, 2015.  M. Arsyad, A. Wibowo. “Mengukur Efisiensi Kontraktor di Sektor Konstruksi Nasional Menggunakan Data Envelopment Analysis”, Jurnal Teknik Sipil, 22(2):115-126, 2015.  R. Ramanathan, “An Introduction to Data Envelopment Analysis”, Sage Publications, 2003.  A. Charnes, W. W. Cooper, E. Rhodes, “Measuring the Efficiency of Decision-Making Units”, European Journal of Operations Research, 2:429–44, 1978.  L Breiman, JH Friedman, RA Olshen, CJ Stone, Classification and Regression Trees, New York : Chapman & Hall/CRC, 1984.  V. N. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-Sampling Technique”, Journal of Artificial Intelligence Research, 16:321-357, 2002.
All rights reserverd @ IJCSN International Journal www.IJCSN.org