Estimation Model of Dry Docking Duration Using Data Mining

Maintenance is one of the most important activities in
the shipyard industry. However, sometimes it is not supported by
adequate services from the shipyard, where inaccuracy in estimating
the duration of the ship maintenance is still common. This makes
estimation of ship maintenance duration is crucial. This study uses
Data Mining approach, i.e., CART (Classification and Regression
Tree) to estimate the duration of ship maintenance that is limited to
dock works or which is known as dry docking. By using the volume
of dock works as an input to estimate the maintenance duration, 4
classes of dry docking duration were obtained with different linear
model and job criteria for each class. These linear models can then be
used to estimate the duration of dry docking based on job criteria.

• Isti Surjandari
• Riara Novita

• Classification and regression tree (CART)
• data mining
• dry docking
• maintenance duration.

<p>[1] A. Srdoc, I. Bratko,and A. Sluga,&ldquo;Machine learning applied to quality
management - a study in ship repair domain,&rdquo;Computers in Industryvol.
58, pp. 464-473, 2007.
[2] D.J. Hand, H. Mannila, and P. Smyth, Principles of data mining.
Cambridge, MA: MIT Press, 2001.
[3] D.T. Larose, Discovering knowledge in data: an introduction to data
mining. New Jersey: John Wiley &amp; Sons, Inc., 2005.
[4] Shih-Yen Lin, Jo-Ting Wei, Chih-ChienWeng, and Hsin-Hung Wu, &ldquo;A
case study of using classification and regression tree and LRFM model
in pediatric dental clinic,&rdquo; International Conference on Innovation,
Management and Service,IPEDR vol.14. Singapore: IACSIT Press,
2011.
[5] I. H. Witten and E. Frank, Data mining: practical machine learning
tools and techniques &ndash; 2ndEdition, San Francisco: Elsevier, 2005.
[6] R.J. Lewis, &ldquo;An introduction to classification and regression tree
(CART) analysis,&rdquo; Annual Meeting for Academic Emergency Medicine
in San Francisco, 2000.
[7] P. Cunningham and S.J. Delany, &ldquo;K-nearest neighbor classifiers,&rdquo;
Technical Report UCD-CSI-2007-4, 2007.</p>