A Decision Support System using ANFIS to Determine the Major of Prospective Students in A Vocational School of Indonesia
 |
International Journal of Computer Trends and Technology (IJCTT) | 
|
| © 2015 by IJCTT Journal | ||
| Volume-27 Number-2 |
||
| Year of Publication : 2015 | ||
| Authors : Andri Pranolo, Faiz In’ammurrohman, Yana Hendriana , Dewi Octaviani | ||
| DOI : 10.14445/22312803/IJCTT-V27P117 |
Andri Pranolo, Faiz In’ammurrohman, Yana Hendriana , Dewi Octaviani "A Decision Support System using ANFIS to Determine the Major of Prospective Students in A Vocational School of Indonesia". International Journal of Computer Trends and Technology (IJCTT) V27(2):100-105, September 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
A decision support system (DSS) plays an
important role in accurately determining optimal solutions
or decisions in a variety of ways, including the activity of
selecting most appropriate major for prospective students.
This work aims to develop a computer-based DSS the most
appropriate major using Adaptive Neuro-Fuzzy Inference
System (ANFIS) based on the following determinant
variables, the first is national exam scores (mathematics,
Bahasa Indonesia, English, and Natural Science); the
second, Interesting to the majors (prospective-students
choice); and the third, test question scores. The results show
that the computer-based DSS has worked properly, effective
and accurate to determine major of the prospective student
in a vocational school.
References
[1] H. Hamdani and R. Wardoyo, “A review on fuzzy multicriteria
decision making land clearing for oil palm plantation,”
Int. J. Adv. Intell. Informatics, vol. 1, no. 2, pp. 75–83, 2015.
[2] C. García-Diéguez, M. Herva, and E. Roca, “A decision
support system based on fuzzy reasoning and AHP–FPP for
the ecodesign of products: Application to footwear as case
study,” Appl. Soft Comput., vol. 26, pp. 224–234, Jan. 2015.
[3] N. Lei and S. K. Moon, “A Decision Support System for
market-driven product positioning and design,” Decis. Support
Syst., vol. 69, pp. 82–91, Jan. 2015.
[4] E. Giusti and S. Marsili-Libelli, “A Fuzzy Decision Support
System for irrigation and water conservation in agriculture,”
Environ. Model. Softw., vol. 63, pp. 73–86, Jan. 2015.
[5] L. Rosén, P.-E. Back, T. Söderqvist, J. Norrman, P. Brinkhoff,
T. Norberg, Y. Volchko, M. Norin, M. Bergknut, and G.
Döberl, “SCORE: A novel multi-criteria decision analysis
approach to assessing the sustainability of contaminated land
remediation.,” Sci. Total Environ., vol. 511, pp. 621–38, Apr.
2015.
[6] A. Pranolo and S. M. Widyastuti, “Simple Additive Weighting
Method on Intelligent Agent for Urban Forest Health
Monitoring,” in Computer, Control, Informatics and Its
Applications (IC3INA), 2014 International Conference on,
2014, pp. 132–135.
[7] D. Pratiwi, J. P. Lestari, and D. Agushita, “Decision Support
System to Majoring High School Student UsingSimple
Additive Weighting Method,” Int. J. Comput. Trends
Technol., vol. 10, no. 3, pp. 153–159, 2014.
[8] A. S. Aribowo and N. H. Cahyana, “Feasibility study for
banking loan using association rule mining classifier,” Int. J.
Adv. Intell. Informatics, vol. 1, no. 1, pp. 41–47, 2015.
[9] M.-Y. Chen, “A hybrid ANFIS model for business failure
prediction utilizing particle swarm optimization and
subtractive clustering,” Inf. Sci. (Ny)., vol. 220, pp. 180–195,
Jan. 2013.
[10] A. Saghaei and H. Didehkhani, “Developing an integrated
model for the evaluation and selection of six sigma projects
based on ANFIS and fuzzy goal programming,” Expert Syst.
Appl., vol. 38, no. 1, pp. 721–728, Jan. 2011.
[11] J.-G. Yang, J.-K. Kim, U.-G. Kang, and Y.-H. Lee, “Coronary
heart disease optimization system on adaptive-network-based
fuzzy inference system and linear discriminant analysis
(ANFIS–LDA),” Pers. Ubiquitous Comput., vol. 18, no. 6, pp.
1351–1362, Oct. 2013.
[12] K. POLAT and S. GUNES, “Automatic determination of
diseases related to lymph system from lymphography data
using principles component analysis (PCA), fuzzy weighting
pre-processing and ANFIS,” Expert Syst. Appl., vol. 33, no. 3,
pp. 636–641, Oct. 2007.
[13] A. H. Setyaningrum and P. M. Swarinata, “Weather prediction
application based on ANFIS (Adaptive neural fuzzy inference
system) method in West Jakarta region,” in Cyber and IT
Service Management (CITSM), 2014 International Conference
on, 2014, pp. 113–118.
[14] Z. Wang, Q. Pan, L. Yang, C. Xu, F. Yu, L. Li, and J. He,
“Recognition of outer membrane proteins using adaptive
neuro-fuzzy inference systems,” in Fuzzy Systems and
Knowledge Discovery (FSKD), 2014 11th International
Conference on, 2014, pp. 262–267.
[15] S. Khanmohammadi, C.-A. Chou, H. W. Lewis, and D. Elias,
“A systems approach for scheduling aircraft landings in JFK
airport,” in Fuzzy Systems (FUZZ-IEEE), 2014 IEEE
International Conference on, 2014, pp. 1578–1585.
[16] L. Yifan, L. Ning, and L. Shaoyuan, “ANFIS modeling of the
PMV thermal comfort index based on prior knowledge,” in
Industrial Electronics and Applications (ICIEA), 2014 IEEE
9th Conference on, 2014, pp. 214–219.
[17] G. Özkan and M. ?nal, “Comparison of neural network
application for fuzzy and ANFIS approaches for multi-criteria
decision making problems,” Appl. Soft Comput., vol. 24, pp.
232–238, Nov. 2014.
[18] T. R. Kiran and S. P. S. Rajput, “An effectiveness model for
an indirect evaporative cooling (IEC) system: Comparison of
artificial neural networks (ANN), adaptive neuro-fuzzy
inference system (ANFIS) and fuzzy inference system (FIS)
approach,” Appl. Soft Comput., vol. 11, no. 4, pp. 3525–3533,
Jun. 2011.
[19] R. R. Janghel, A. Shukla, and R. Tiwari, “Decision Support
System for Fetal Delivery Using Soft Computing
Techniques,” in Computer Sciences and Convergence
Information Technology, 2009. ICCIT ’09. Fourth
International Conference on, 2009, pp. 1514–1519.
[20] J.-S. R. Jang, “ANFIS: adaptive-network-based fuzzy
inference system,” IEEE Trans. Syst. Man. Cybern., vol. 23,
no. 3, pp. 665–685, 1993.
[21] A. Malathi and N. Karthikeyan, “Performance analysis of
acoustic echo cancellation using Adaptive Neruo Fuzzy
Inference System,” in Advanced Communication Control and
Computing Technologies (ICACCCT), 2014 International
Conference on, 2014, pp. 1132–1136.
Keywords
Decision support system, ANFIS, prospective
student, vocational school, Indonesia.