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All Journal Jurnal Masyarakat Informatika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Indonesian Journal of Public Administration Review
Kurniawan, Defri
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Status Gizi Balita Sabrina, Della; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8668

Abstract

Nutritional status in children under five years of age serves as a key indicator in assessing the overall health, growth, and development of children. Conventionally, nutritional status is determined through manual measurements and interpretation of anthropometric tables, which is time-consuming and prone to human error. With advances in technology, machine learning-based approaches can be used to help classify nutritional status more quickly, objectively, and accurately, thereby supporting decision-making in public health. This study focuses on analyzing and comparing the performance of three machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree (DT) in classifying the nutritional status of toddlers using anthropometric data that includes variables such as age, gender, weight, and height. In this study, the nutritional status categories classified for the toddler weight dataset include: Severely Underweight, Underweight, Normal, and Overweight. The categories for the height dataset include Severely Stunted, Stunted, Normal, and Tall. The research stages included data preprocessing, data splitting into training and testing, and model performance assessment through accuracy, precision, recall, and F1-score matrices. Based on the evaluation results of the toddler height dataset, the K-Nearest Neighbors (KNN) algorithm proved to be the model with the best performance, with an accuracy of 99.91%. This value exceeded that of the Decision Tree, which achieved an accuracy of 99.89%, and the SVM (RBF) algorithm, which achieved 98.48%.
Co-Authors Abu Salam Ainul Yaqin Amartha, Putri Ananta, Devanda Radya Azizah, Hidayatul Dzaky, Azmi Abiyyu Dzaky, Fauzan Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fauziah, Mitha Fikri Budiman Haresta, Alif Agsakli Ida Ayu Putu Sri Widnyani Ika Novita Dewi Junta Zeniarja Kamarudin, Fatkhurridlo Pranoto Kuncoro, Aneira Vicentiya Muljono Muljono Ni'mah, Laila Maulin Ningrum, Novita Kurnia Nurrohman, Slamet Hudha Prajanto Wahyu Adi, Prajanto Wahyu Pranata, Eka Arya Purwanto Purwanto Putro Utomo, Satrio Allam Ramadhan Rakhmat Sani Rosi Subhiyakto, Egia Sabrina, Della Septian Enggar Sukmana Sindhu Rakasiwi Sri Winarno Utomo, Danang Wahyu