Fuzzy Logic using Tsukamoto Model and Sugeno Model on Prediction Cost

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Adriyendi 1,*

1. IAIN Btusangkar, Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2018.06.02

Received: 31 Jul. 2017 / Revised: 6 Nov. 2017 / Accepted: 20 Dec. 2017 / Published: 8 Jun. 2018

Index Terms

Fuzzy logic, external cost, tsukamoto model, sugeno model


This paper aims to implement Fuzzy Logic for cost prediction. Fuzzy Logic using Tsukamoto Model and Sugeno Model. Predicted costs consist of communication cost, transportation cost, and social cost as the external cost. The external cost is one component of living cost. High external cost becomes one of the causes of the high cost of living. The high cost of living is one of the factors causing high-cost economy. In this case, the cost prediction using Fuzzy Logic. Experimental results show that Fuzzy Logic using Tsukamoto Model with value is 1891. Fuzzy Logic using Sugeno Model with value 1621. Both models produce a feasible cost prediction. Feasible is meaning accurate and proper (value cost between low cost and high cost from all of cost). There are 46.56 % of the population of middle class in Indonesia. This means that 46.56% of the population of Indonesia has the potential to reduce the high cost economy. High cost economy (living cost) can be reduced, it can drive economic growth (social cost) and be able to improve social welfare (social cost).

Cite This Paper

Adriyendi, "Fuzzy Logic using Tsukamoto Model and Sugeno Model on Prediction Cost", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.6, pp.13-21, 2018. DOI:10.5815/ijisa.2018.06.02


[1]H. Schmiedel, G. Kostova, and W. Ruttenberg, “The social and private costs of retail payment instruments: a European perspective”, Occasional Paper Series, no. 137, pp. 1-49, 2012.
[2]H. Kharas, “The unprecedented expansion of the global middle class: an update”, Global Economy & Development, Working Paper 100, pp. 1-32, 2017.
[3]BPS Statistic Indonesia, “Indonesia population projection 2010-2035”, Sub-directorate of Statistical Demographic, Jakarta, pp. 1-470, 2013.
[4]M.A. Nizar, “Middle class and its implications to economy of Indonesia”, Chapter 8, Nagakusuma Media Kreatif Publisher, Jakarta, pp. 171-192, 2015.
[5]Wasiur Rhmann, Vipin Saxena,"Fuzzy Expert System Based Test Cases Prioritization from UML State Machine Diagram using Risk Information", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.3, No.1, pp.17-27, 2017.DOI: 10.5815/ijmsc.2017.01.02
[6]G. Singh, P. Kunai, and D. Goyal, “A review fuzzy logic and its application”, International Journal of Engineering and Technical Research, pp. 61-66, 2014.
[7]S. R. Chaudhari and M. E. Patil, “Comparative analysis of fuzzy inference systems for air conditioner”, International Journal of Advanced Computer Research, vol. 4, no. 4, iss. 17, pp. 922-927, 2014.
[8]A. Saepullah, and R. S. Wahono, “Comparative analysis of mamdani, sugeno and tsukamoto method of fuzzy inference system for air conditioner energy saving”, Journal of Intelligent Systems, vol. 1, no. 2, pp. 143-147, 2015.
[9]M. R. Mohanraj, M. Balamurugan, V.P. Suresh, and R. Gobu, “Design of air-conditioning controller by using mamdani and sugeno fuzzy inference systems”, South Asian Journal of Engineering and Technology, vol.2, no.16, pp. 7-16, 2016.
[10]W. E. Sari, O. Wahyunggoro, and S. Fauziati, “A comparative study on fuzzy mamdani-sugeno-tsukamoto for the childhood tuberculosis diagnosis”, Advances of Science and Technology for Society, AIP Conference Proceedings 1755, 070003, pp. 1-6, 2016.
[11]A T. Cahyono, S. Salu, and N. Nikentari, “Comparison analysis decision support system using fuzzy sugeno and fuzzy tsukamoto”, Repository of Final Project, Informatics Dept., Faculty of Engineering, Univ. Maritim Raja Ali Haji, Riau, pp. 1-9, 2013.
[12]M. Sampor, S. Kamali, L. Mortazavifar, and A. Khoramian, “Study on fuzzy systems and concepts: review papers”, Journal of Current Research in Science, vol. 4, no. 1, pp. 175-183, 2016.
[13]Adriyendi, "Fuzzy Logic using Tahani Model on Food Commodity", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.7, pp.1-11, 2017. DOI: 10.5815/ijisa.2017.07.01
[14]R. A. Priyono and K. Surendro, “Nutritional needs recommendation based on fuzzy logic”, Procedia Technology, vol. 11, pp. 1244-1251, 2013.
[15]F. Cavallaro, “A takagi-sugeno fuzzy inference system for developing a sustainability index of biomass”, Sustainability, vol. 7, pp. 12359-12371, 2015.