Article Per Year (5 Year)
p-Index From 2020 - 2025
1.686
P-Index
This Author published in this journals
All Journal JPT : JURNAL PROTEKSI TANAMAN (JOURNAL OF PLANT PROTECTION) Jurnal Ilmu dan Teknologi Kayu Tropis Menara Ilmu Building of Informatics, Technology and Science Sainstek : Jurnal Sains dan Teknologi Journal of Informatics Management and Information Technology Journal of Social Responsibility Projects by Higher Education Forum Jurnal Teknologi Komputer dan Informasi
Desyanti Desyanti
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Integrasi K-Modes dalam Analisis Data Gizi Balita untuk Model Klasifikasi Risiko Stunting Desyanti, Desyanti; Renaldi, Reno; Mesran, Mesran
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8323

Abstract

The nutritional status of toddlers is an important indicator in assessing child growth and development and is closely related to the risk of stunting. However, the process of recording and classifying nutritional status at the Bukit Kapur Community Health Center is still done manually, making it prone to analysis delays and data processing errors. This study aims to implement the K-Modes algorithm in classifying toddler nutritional status based on categorical data, such as age, weight, and height. Toddler data were collected from the Bukit Kapur Community Health Center and underwent pre-processing, data transformation, and the application of the K-Modes algorithm to determine toddler nutritional groups. The results showed that the K-Modes algorithm was able to group toddler data into three main categories: well-nourished, at-risk of overnutrition, and overnourished. The majority of toddlers fell into the well-nourished category (98 toddlers), while only a small proportion fell into the at-risk of overnutrition and overnourished categories (1 toddler each). These findings indicate that the K-Modes method is effective in classifying toddler nutritional status based on categorical data and can assist health workers in monitoring child growth and preventing stunting.
Co-Authors Erlin Windia Ambarsari Fauzan Fauzan Laksono Trisnantoro Mesran, Mesran Novi Andra renaldi, reno Rini Yuliani Rosi Fitri Ramadani Sari, Febrina Suarlin, John Sulaeman Yusuf Tri Handayani Utama, Dian Welly Desriyati Yulia Saputri Yusrizal Yusrizal Yusuf Sudo Hadi Zulhilda Yenti Zulmardi Zulmardi Zulyusri Zulyusri