The Comprehensive Mamdani Inference to Support Scholarship Grantee Decision

- Humaira - Department of Information Technology, Politeknik Negeri Padang, 21562, Indonesia
- Rasyidah - Department of Information Technology, Politeknik Negeri Padang, 21562, Indonesia
- Junaldi - Department of Electrical Engineering, Politeknik Negeri Padang, 21562, Indonesia
Indri Rahmayuni - Department of Information Technology, Politeknik Negeri Padang, 21562, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.5.2.449

Abstract


Fuzzy Mamdani has been mostly used in various disciplines of science. Its ability to map the input-output in the form of a surface becomes an interesting thing. This research took DSS case of a scholarship grantee. Many criteria in taking a decision need to be simplified so that the result obtained remains intuitive. The model completion by conducting two stages consisted of two phases. The first phase consists of four FIS blocks. The second phase consists of one FIS block. The FIS design in the first phase was designed in such a way so that the output obtained has a big score interval. FIS output at the first phase will become FIS input at the second phase. This big value range becomes good input at FIS in the second phase. Each FIS block has different total input. Until the surface formed must be seen from various dimensions to assure trend surface increasing or decreasing softly. This kind of thing is conducted by observing the movement of output dots kept for its soft surface form. The output dots change influenced by the membership function, the regulations used, total fuzzy set, and parameter value of membership function. This research used the Gaussian membership function. The Gaussian membership function is highly suitable for this DSS case. This article also explains the usage of a fuzzy set in each input, the parameter from the membership function, and the input value range. After observing the surface form with an intuitive approach, then this model needs to be evaluated. The evaluation was done to measure the model performance using Confusion Matrix. The result of model performance obtained accuracy in the amount of 85%.

Keywords


Fuzzy; Mamdani; DSS; scholarship; reasoning.

Full Text:

PDF

References


P. Georgieva, “Fuzzy Rule-Based Systems for Decision-Making,” no. May 2016, 2018.

S. D. A. N. Sbmptn, “Panduan pendaftaran beasiswa bidikmisi 2019,” 2019.

E. Turban, J. E. Aronson, and T.-P. Liang, “Decision Support Systems and Business Intelligence,” Decis. Support Bus. Intell. Syst. 7/E, pp. 1–35, 2007, doi: 10.1017/CBO9781107415324.004.

S. G. Fashoto, O. Amaonwu, and A. Afolorunsho, “Development of A Decision Support System on Employee Performance Appraisal using AHP Model,” JOIV Int. J. Informatics Vis., vol. 2, no. 4, p. 262, 2018, doi: 10.30630/joiv.2.4.160.

Z. T. Al-Ars and A. Al-Bakry, “A web/mobile decision support system to improve medical diagnosis using a combination of K-mean and fuzzy logic,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 6, pp. 3145–3154, 2019, doi: 10.12928/TELKOMNIKA.v17i6.12715.

C. B. M. T. Xyz, “Funding Eligibility Decision Support System Using Fuzzy Logic Tsukamoto,” no. March 2018, 2017, doi: 10.1109/IAC.2017.8280622.

M. Blej and M. Azizi, “Comparison of Mamdani-type and Sugeno-type fuzzy inference systems for fuzzy real time scheduling,” Int. J. Appl. Eng. Res., vol. 11, no. 22, 2016.

A. Hidayat and D. Putra, “Temperature and Soil Control Design with Fuzzy Method in Greenhouse for Cabe Seeding,” vol. 3, pp. 243–247.

M. N. Shodiq, D. H. Kusuma, M. G. Rifqi, A. R. Barakbah, and T. Harsono, “Adaptive Neural Fuzzy Inference System and Automatic Clustering for Earthquake Prediction in Indonesia,” JOIV Int. J. Informatics Vis., vol. 3, no. 1, pp. 47–53, 2019, doi: 10.30630/joiv.3.1.204.

T. Tung Khuat and M. H. Le, “An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem,” JOIV Int. J. Informatics Vis., vol. 1, no. 2, p. 40, 2017, doi: 10.30630/joiv.1.2.20.

J. M. Mendel, “Introduction to Type-2 Fuzzy Sets and Systems I . Type-2 Fuzzy Sets Especially Interval Type-2 Fuzzy Sets What is a T2 FS and How is it Different From a T1 FS ?”

W. Mendes, “Comparison of Fuzzy Type-2 and Conventional Fuzzy Controllers Tuned by Ant Colony Optimization,” no. January 2017, doi: 10.26678/ABCM.COBEM2017.COB17-1934.

I. Rahmayuni, “Tuning Parameters On Fuzzy Inference Based Decision Support System,” 2018 Int. Conf. Appl. Sci. Technol., pp. 35–38, 2018, doi: 10.1109/iCAST1.2018.8751539.

J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. 1997.

L. A. Zadeh, “Fuzzy Logic,” Computer (Long. Beach. Calif)., vol. 21, no. 4, pp. 83–93, 1988, doi: 10.1109/2.53.

P. N. Padang, “Determining the Appropiate Cluster Number Using Elbow Method for K-Means Algorithm,” 2018, doi: 10.4108/eai.24-1-2018.2292388.

T. M. Kodinariya and P. R. Makwana, “Review on determining number of Cluster in K-Means Clustering,” Int. J. Adv. Res. Comput. Sci. Manag. Stud., vol. 1, no. 6, pp. 2321–7782, 2013.

K. P. Chiao, “The multi-criteria group decision making methodology using type 2 fuzzy linguistic judgments,” Appl. Soft Comput. J., vol. 49, 2016, doi: 10.1016/j.asoc.2016.07.050.

P. N. Padang, P. N. Padang, and P. N. Padang, “Designing Mamdani Fuzzy Inference Systems for Decision Support Systems,” no. 1, pp. 111–115, 2019.

F. Gorunescu, Data Mining concepts, Models and Techniques. Springer, 2011.




Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

__________________________________________________________________________
JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
Published by Department of Information Technology - Politeknik Negeri Padang
W : http://joiv.org
E : joiv@pnp.ac.id, hidra@pnp.ac.id, rahmat@pnp.ac.id

View JOIV Stats

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.