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PERBANDINGAN METODE INFERENSI FUZZY MAMDANI DAN TSUKAMOTO DALAM MELAKUKAN PREDIKSI RISIKO PENYAKIT BATU GINJAL

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Penilaian

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Kidney stones are one of the most common health problems and have the potential to cause chronic kidney failure if not properly addressed. To support early prevention efforts, this study aims to compare two fuzzy inference methods, namely Mamdani and Tsukamoto. The study will use 89 data points obtained from Kaggle under the title "Kidney Stones Dataset." In predicting the risk of kidney stones, this study will utilize pH, calcium, and urea as variables. The output variable represents the risk level, classified into low risk and high risk. In the prediction process, the Mamdani method will employ the Centroid method, while the Tsukamoto method will use the Weighted Average method for defuzzification. The results of the study indicate that the Mamdani method achieved prediction accuracy for 64 data, while the Tsukamoto method achieved prediction accuracy for 45 data. In terms of Mean Squared Error (MSE), the Tsukamoto method resulted in an MSE of 0.494, whereas the Mamdani method produced an MSE of 0.281. These findings demonstrate that the Mamdani inference method is more effective in predicting the risk of kidney stones.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000126	T164400	T1644002025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1644002025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, V-6 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

EM

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