Combination of Decision Support Systems and Geographic Information Systems in Determining Undernutrition Status Using Deep Learning And K-Means Clustering - Mohammad Ilham Bahri; Muhammad Aswin; Mohammad Fauzan Purnomo (Universitas Trunojoyo Madura, 2023)
The nutritional status of toddlers is a factor that needs to be considered in maintaining their health because toddlerhood is a developmental period that is vulnerable to nutrition. Death cases in toddlers are one factor in the lack of monitoring by local governments. It is necessary to carry out activities to observe and anticipate problems to take action as early as possible. This problem can be solved using the help of Decision Support Systems and Geographic Information Systems. Machine learning-based deep learning methods are employed as alternative algorithms for Decision Support Systems. Deep learning is considered a very promising approach due to its ability to analyze and extract patterns from data, afterwards applying these patterns to address following challenges. The selection of a Geographic Information Systems was conducted by employing the k-means clustering technique in order to create a visual representation of the zone groupings within each region. The accuracy of the deep learning method for determining nutritional status was found to be 95.24%. In the context of mapping regional zone groupings, it is noteworthy that the k-means clustering method exhibits a remarkable accuracy rate of 100% in relation to the true value. Based on the obtained findings, it can be inferred that the deep learning and k-means clustering techniques exhibit a high level of accuracy in discerning nutritional status and delineating regional zone groupings.