Comparative analysis of Fuzzy Tsukamoto's membership functions for determining irrigated rice field feasibility status


Ummi Syafiqoh(1*); Anton Yudhana(2); Sunardi Sunardi(3);

(1) STMIK PPKIA Tarakanita Rahmawati
(2) Universitas Ahmad Dahlan
(3) Universitas Ahmad Dahlan
(*) Corresponding Author

  

Abstract


The representation of the fuzzy set membership curve consisting of trapezoidal, triangular, and linear shapes, has an important role in the fuzzy logic system. The selection of the curve's shapes determines the useable membership function and affects the fuzzy output value. Previous studies generally used curves that had been employed in predecessors or other studies that did not explain the reason for choosing a fuzzy member curve. This condition became problem because there was not a guide in selecting the appropriate membership function model for the parameters used in the fuzzy process so that most researchers only use membership functions that are commonly used in previous studies or in the same case as their research. The purpose of this study was to determine the effect of selecting trapezoidal and triangular curves on the performance of Tsukamoto's fuzzy logic for determining the rice-fields suitability status. The research methodology comprised 3 main stages. The first stage was data collecting, to collect soil pH values, soil moisture, and air temperature in rice fields. The second stage was the implementation of the Tsukamoto fuzzy. At this stage, two membership function curves were used. The third stage was a comparative analysis of Tsukamoto's fuzzy's output of trapezoidal and triangular curves. The results obtained indicate that there is no significant performance difference between the two different membership functions. The results of the research with the trapezoidal membership function have a better accuracy rate of 93% while the triangular membership function has an accuracy rate of 90%.


Keywords


Membership Function; Fuzzy Logic; Fuzzy Tsukamoto; Fuzzy Inference System; Comparative Analysis

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 304 times
PDF view: 121 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i3.1156.255-263 		  

Cite

How to cite item

References


Z. Niswati, F. A. Mustika, and A. Paramita, “Fuzzy logic implementation for diagnosis of Diabetes Mellitus disease at Puskesmas in East Jakarta,” J. Phys. Conf. Ser., vol. 1114, no. 1, 2018, doi: 10.1088/1742-6596/1114/1/012107.

H. Thakkar, V. Shah, H. Yagnik, and M. Shah, “Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis,” Clin. eHealth, vol. 4, no. 2021, pp. 12–23, 2021, doi: 10.1016/j.ceh.2020.11.001.

A. De and S. P. Singh, “Analysis of fuzzy applications in the agri-supply chain: A literature review,” J. Clean. Prod., vol. 283, p. 124577, 2021, doi: 10.1016/j.jclepro.2020.124577.

D. Ibrahim, “An Overview of Soft Computing,” Procedia Comput. Sci., vol. 102, no. August, pp. 34–38, 2016, doi: 10.1016/j.procs.2016.09.366.

D. M. Sihotang, “Metode Skoring dan Metode Fuzzy dalam penentuan zona resiko malaria di Pulau Flores,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 5, no. 4, pp. 302–308, 2016.

N. E. Zendrato, O. Darnius, and P. Sembiring, “Perencanaan jumlah produksi mie instan dengan penegasan (defuzzifikasi) centroid fuzzy mamdani (Studi Kasus: Jumlah Produksi Indomie di PT. Indofood CBP Sukses Makmur, Tbk Tanjung Morawa),” Saintia Mat., vol. 2, no. 2, pp. 115–126, 2014.

O. A. M. Ali, A. Y. Ali, and B. S. Sumait, “Comparison between the effects of different types of membership functions on Fuzzy Logic Controller Performance,” Int. J. Emerg. Eng. Res. Technol., vol. 3, no. October, p. 76, 2015, [Online]. Available: https://www.researchgate.net/publication/282506091.

A. A. Khoiruddin, “Algoritma Genetika untuk Menentukan Jenis Kurva dan Parameter Himpunan Fuzzy,” 2007.

Amriana, A. A. Kasim, and Maghfirat, “Penentuan harga Tandan Buah Segar ( TBS ) Kelapa Sawit menggunakan metode Fuzzy Logic,” Ilk. J. Ilm., vol. 12, no. 3, pp. 236–244, 2020.

U. Mustofa, Y. Yanitasari, and Dedih, “Perencanaan anggaran pinjaman dengan prediksi regresi linier sederhana dan optimasi menggunakan metode Fuzzy Tsukamoto,” Ilk. J. Ilm., vol. 11, no. 28, pp. 206–213, 2019.

L. K. Wardhani and E. Haerani, “Analisis pengaruh pemilihan fuzzy membership function terhadap output sebuah sistem fuzzy logic,” SNTIKI III, pp. 326–333, 2011.

P. Harliana and R. Rahim, “Comparative analysis of membership function on mamdani fuzzy inference system for decision making,” J. Phys. Conf. Ser., vol. 930, no. 1, 2017, doi: 10.1088/1742-6596/930/1/012029.

S. Susanto, “Perbandingan fungsi keanggotaan tipe segitiga dan tipe G-Bell terhadap analisis risiko,” Ukarst, vol. 3, no. 2, pp. 57–67, 2019.

J. Gayathri Monicka, D. N.O.Guna Sekhar, and K. Ramash Kumar, “Performance evaluation of membership functions on fuzzy logic controlled AC voltage controller for speed control of induction motor drive,” Int. J. Comput. Appl., vol. 13, no. 5, pp. 8–12, 2011, doi: 10.5120/1778-2451.

A. Yudhana, H. K. Dewi, D. D. Fairus, Z. Salsabila, and H. Yuliansyah, “Moisture monitoring of rice fields in Jogotirto Sleman using Internet of Thing,” in International Conference of Science and Technology for the Internet of Things, 2019, pp. 3–8, doi: 10.4108/eai.20-9-2019.2292093.

U. Syafiqoh, “Dataset Soil Ph, Soil Moisture, Temperature,” 2022. https://www.kaggle.com/datasets/ummisyafiqoh/dataset-soil-ph-soil-moisture-and-temperature.

P. M. P. N. 7. T. 2013 123, Pedoman kesesuaian lahan pada komoditas tanaman pangan. Jakarta, 2013.

T. Sutojo, E. Mulyanto, and V. Suhartono, Kecerdasan Buatan. Andi Yogyakarta, 2011.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Ummi Syafiqoh, Anton Yudhana, Sunardi Sunardi

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