Web-Based Application for Toddler Nutrition Classification Using C4.5 Algorithm
Health is something that is important for everyone, from year to year various efforts have been developed to get better and quality health. Good nutritional status for toddlers will contribute to their health and also the growth and development of toddlers. Fulfillment of nutrition in children under five years old (toddlers) is a factor that needs to be considered in maintaining health, because toddlerhood is a period of development that is vulnerable to nutritional problems. There are more than 100 toddler data registered at the Integrated Healthcare Center in Peresak Village, Narmada District, West Lombok Regency. The book contains data on toddlers along with the results of weighing which is carried out every month. However, to classify the nutritional status of toddlers, they are still going through the process of recording in a notebook by recording the measurement results and then looking at the reference table to determine their nutritional status. This method is still conventional or manual so it takes a long time to determine the nutritional status. Therefore, the solution in this study is to develop a web-based application for the classification of the nutritional status of children under five using the C4.5 method. The stages of this research consisted of problem analysis, collection of 197 instances of nutritional status datasets obtained from Integrated Healthcare Center Presak, analysis of system requirements, use case design, implementation using the C4.5 method, and performance testing based on accuracy, sensitivity, and specificity. The results of this study are a website-based application for the classification of the nutritional status of children under five using the C4.5 method. The performance of the C4.5 method in the classification of the nutritional status of toddlers using testing data as much as 20% gets an accuracy of 95%, sensitivity of 100%, and specificity of 66.6%. Thus, the C4.5 method can be used to classify the nutritional status of children under five, because it has a very good performance.
 Y. R. Kaesmitan and J. A. Johannis, “Klasifikasi Status Gizi Balita Di Kelurahan Oesapa Barat Menggunakan Metode K-Nearest Neigbor,” Multitek Indonesia, vol. 11, no. 1, pp. 42–50, Aug. 2017, doi: 10.24269/mtkind.v11i1.506.
 A. S. Hutasoit, P. Tarigan, and E. R. Siagian, “Implementasi Data Mining Klasifikasi Status Gizi Balita Pada Posyandu Medan Timur Dengan Menggunakan Metode C4.5,” Jurnal Pelita Informatika, vol. 7, no. 2, pp. 120–125, 2018.
 H. Hairani, A. S. Suweleh, and D. Susilowaty, “Penanganan Ketidak Seimbangan Kelas Menggunakan Pendekatan Level Data,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 1, pp. 109–116, 2020, doi: 10.30812/matrik.v20i1.846.
 A. S. Suweleh, D. Susilowati, and Hairani Hairani, “Aplikasi Penentuan Penerima Beasiswa Menggunakan Algoritma C4.5,” Jurnal BITe, vol. 2, no. 1, pp. 12–21, 2020.
 H. Hairani, N. A. Setiawan, and T. B. Adji, “Metode Klasifikasi Data Mining dan Teknik Sampling SMOTE Menangani Class Imbalance Untuk Segmentasi Customer Pada Industri Perbankan,” in Prosiding SNST Fakultas Teknik, 2016, vol. 1, no. 1, pp. 168–172, doi: 978-602-99334-5-1.
 D. Kurniawan, A. Anggrawan, and H. Hairani, “Graduation Prediction System On Students Using C4.5 Algorithm,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 358–365, 2020, doi: 10.30812/matrik.v19i2.685.
 M. I. Ula M., Ulva A. F., Saputra I., Mauliza M., “Implementation of Machine Learning Using the K-Nearest Neighbor Classification Model in Diagnosing Malnutrition in Children,” Multica Science And Technology (Mst) Journal, vol. 2, no. 1, pp. 94–99, 2022, doi: 10.47002/mst.v2i1.326.
 K. Pinaryanto, R. A. Nugroho, and Y. Basilius, “Classification of Toddler Nutrition Using C4.5 Decision Tree Method,” International Journal of Applied Sciences and Smart Technologies, vol. 3, no. 1, pp. 131–142, 2021, doi: 10.24071/ijasst.v3i1.3366.
 M. Ula, A. Ulva, Mauliza, I. Sahputra, and Ridwan, “Implementation of Machine Learning in Determining Nutritional Status using the Complete Linkage Agglomerative Hierarchical Clustering Method,” Jurnal Mantik, vol. 5, no. 3, pp. 1910–1914, 2021.
 T. M. Mulyono, F. Natalia, and S. Sudirman, “A Study of Data Mining Methods for Identification Undernutrition and Overnutrition in Obesity,” in Proceedings of the 2019 3rd International Conference on Software and E-Business, 2019, pp. 6–10, doi: 10.1145/3374549.3374565.
 S. S. Nagari and L. Inayati, “Implementation of Clustering using K-Means Method to Determine Nutritional Status,” Jurnal Biometrika dan Kependudukan, vol. 9, no. 1, pp. 62–68, 2020, doi: 10.20473/jbk.v9i1.2020.62.
 W. Wiguna and D. Riana, “Diagnosis Of Coronavirus Disease 2019 (Covid-19) Surveillance Using C4.5 Algorithm,” Jurnal Pilar Nusa Mandiri, vol. 16, no. 1, pp. 71–80, Mar. 2020, doi: 10.33480/pilar.v16i1.1293.
This work is licensed under a Creative Commons Attribution 4.0 International License.