PERBANDINGAN FUZZY INFERENCE SYSTEM TSUKAMOTO DAN MAMDANI DALAM MEMPREDIKSI TINGKAT STUNTING BALITA

ILHAM, RAYHAN MUHAMMAD and Rini, Dian Palupi (2025) PERBANDINGAN FUZZY INFERENCE SYSTEM TSUKAMOTO DAN MAMDANI DALAM MEMPREDIKSI TINGKAT STUNTING BALITA. Undergraduate thesis, Sriwijaya University.

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Abstract

Stunting is a growth disorder in toddlers characterized by a height below the age-standard due to chronic nutritional deficiencies. This study aims to design and compare prediction models for stunting levels using two Fuzzy Inference System (FIS) methods: Tsukamoto and Mamdani. The data used comes from the secondary dataset "data set clustering gizi" obtained from Kaggle, consisting of 495 toddler records with three main input variables including weight for age (W/A), height for age (H/A), and weight for height (W/H). The prediction output is the stunting level based on height, referring to anthropometric standards from the World Health Organization (WHO). The Tsukamoto method uses the Weighted Average defuzzification process, while the Mamdani method applies Mean of Maximum (MoM). Based on testing results, the Mamdani method correctly classified 381 records with a Mean Squared Error (MSE) of 14.2652. Meanwhile, the Tsukamoto method correctly classified 372 records but achieved a lower MSE of 14.2543. Although Mamdani had a higher number of accurate classifications, Tsukamoto produced predictions closer to actual values, indicating that the Tsukamoto method is more suitable for predicting stunting levels in toddlers.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Stunting, Fuzzy Inference System, Tsukamoto Inference, Mamdani Inference, Prediction
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Rayhan Muhammad Ilham
Date Deposited:	19 May 2025 03:39
Last Modified:	19 May 2025 03:39
URI:	http://repository.unsri.ac.id/id/eprint/173119

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