WEBSITE DESIGN FOR NUTRITION STATUS CLASSIFICATION OF TODDLERS USING UI/X WITH K-MEDOIDS ALGORITHM
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Ester Arisawati(1)
Universitas Bina Sarana Informatika
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Rinawati Rinawati(2)
Universitas Bina Sarana Informatika
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Erene Gernaria Sihombing(3)
Universitas Bina Sarana Informatika
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Frisma Handayanna(4*)
Universitas Nusa Mandiri
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Linda Sari Dewi(5)
Universitas Bina Sarana Informatika
(*) Corresponding Author
Abstract
Nutritional needs in Indonesia vary based on age, gender, physical activity, and an individual's health condition. According to Regulation of the Minister of Health of the Republic of Indonesia No. 28 of 2019, the Recommended Dietary Allowance (RDA) issued by the Ministry of Health provides guidelines on daily energy (calorie) requirements. For infants aged 0 to 12 months, the required intake is 550–725 kcal. The toddler phase (0–5 years old) is a golden period of growth, during which physical and brain development occurs rapidly. Malnutrition during this period can lead to growth disorders such as stunting, which has long-term effects on a child's health and intelligence. To determine a toddler's nutritional status, it is essential to classify their status based on weight and height ratio, commonly measured using Body Mass Index (BMI). BMI is used to determine whether a child's weight falls into the normal, underweight, or obese category. Therefore, regular monitoring is necessary to detect nutritional problems early, enabling proper intervention. This study aims to develop a website using the k-medoids algorithm to assess toddlers' nutritional status. The calculation process in this study, which involves 30 toddler data samples, determines the number of toddlers in each cluster: normal nutrition status, undernutrition, and obesity. The study also applies a Confusion Matrix to evaluate the clustering performance, including accuracy, precision, and recall. The evaluation results show that the k-medoids algorithm performs perfectly, achieving 100% accuracy for all clusters. This indicates that k-medoids successfully classifies the data into clusters without errors.
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Copyright (c) 2025 Ester Arisawati, Rinawati Rinawati, Erene Gernaria Sihombing, Frisma Handayanna, Linda Sari Dewi
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