Algorithm Implementation C4.5 For Classification Food Menu to Prevent Stunting in Children

Rizki Fadhilah Ramadhani (Universitas Malikussaleh)
Bustami
Zahratul Fitri

Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

Stunting in childhood is one of the most significant obstacles to human development and globally affects about 162 million children under five. One effort to prevent stunting is a program to increase the nutritional intake of the community, especially children under five, by providing supplementary food (PMT). Classification is one of the data processing techniques that can be used in this process. The results obtained from the study show that the designed system can input training data and data for classification so that the health centre and guardians can determine the good and bad food menus according to the existing data of toddlers. Based on the results of testing with training data and testing data with a ratio of 80:20 from a dataset of 200 data, namely 160 training data, and 40 test data using the C4.5 algorithm obtained in dataset 1 obtained an accuracy value of 82,5%, precision value of 0.96, recall value of 0,8 and F1-score of 0,87273, then in dataset 2 obtained an accuracy value of 72,5%, precision value 0,75, recall value 0,84 and F1-score value 0,79245.