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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002748	T173119	T1731192025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1731192025

Publisher

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

Collation

viii, 39 hlm.; ill.; 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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