Indonesian Journal of Electrical Engineering and Computer Science Vol. 33, No. 1, January 2024, pp. 303~313 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v33.i1.pp303-313  303 Journal homepage: http://ijeecs.iaescore.com Determination of children's nutritional status with machine learning classification analysis approach Musli Yanto 1 , Febri Hadi 1 , Syafri Arlis 2 1 Department of Informatics Engineering, Faculty of Computer Science, Universitas Putra Indonesia YPTK, Padang, Indonesia 2 Department of Information System, Faculty of Computer Science, Universitas Putra Indonesia YPTK, Padang, Indonesia Article Info ABSTRACT Article history: Received Jul 18, 2023 Revised Nov 1, 2023 Accepted Nov 4, 2023 Malnutrition is a problem that is often faced by every country around the world. Various facts show that malnutrition is of particular concern to many researchers. To can overcome this problem, every effort has been made such as developing analytical models in identification, classification, and prediction. This study aims to determine the nutritional status of children using the machine learning (ML) classification analysis approach. The methods used in the ML analysis process consist of cluster K-Means, artificial neural network (ANN), sum square error (SSE), pearson correlation (PC), and decision tree (DT). The dataset for this study uses data on child nutrition cases that occurred in the previous and was sourced from the provincial general hospital (RSUP) M. Djamil, Padang, West Sumatera. Based on the research presented, ML performance in the nutritional status classification analysis gave maximum results. These results are reported based on the level of precision with an accuracy of 99.23%. The results of the analysis can also present a knowledge-based nutritional status classification. This research can contribute to and update the analytical model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children. Keywords: Analysis model Classification Knowledge based system Machine learning Malnutrition This is an open access article under the CC BY-SA license. Corresponding Author: Musli Yanto Department of Informatics Engineering, Faculty of Computer Science, Universitas Putra Indonesia YPTK 25145 Lubuk Begalung, Padang, Indonesia Email: musli_yanto@upiyptk.ac.id 1. INTRODUCTION Malnutrition is a case of imbalance that occurs between food intake and energy needs in the human body [1]. Cases of malnutrition can affect anyone, including children, adolescents, adults and even the elderly [2]. Malnutrition cases also provide gaps or opportunities to be attacked by various diseases such as stunting, anemia, and others [3], [4]. This case is also indicated as a result of a lack of information in understanding the causes of malnutrition [5]. So with this it is necessary to have a process of analyzing malnutrition status which is measured based on indicators determining the level of nutritional status [6]. Indicators in determining nutritional status basically use parameters of human size, shape, and proportion [7]. Previous research also explained that nutritional status is determined by taking physical measurements based on body mass index (BMI) [8]. Furthermore, physical examination or what is known as the anthropometric approach including measuring height and weight is an indicator in determining malnutrition status [9]. The form of measurement by calculating the weight index based on age, weight based on height and height based on age through the Z-Score is a process in determining nutritional status [10]. With this explanation, the determination of nutritional status can be implemented into the classification process. Previous research explained that the classification process for malnutrition status using