Fuzzy C-Means Clustering Untuk Pengelompokan Bahan Makanan Berdasarkan Kandungan Zat Gizi

Abstract

Fuzzy C-Means Clustering (FCM) is a data clustering technique where each data point belongs to a cluster by membership degree. FCM starts with the concept of cluster centers that mark the mean location of each cluster. By iteratively updating the cluster centers and the membership degree for each data point, then the cluster centers move to the right location. FCM can be applied to group the nutrients of foods based on three functions of nutrients which are food as energy provider, body functions regulator and growth developer. Each nutrients are grouped into three cluster. The information of food group can used as reference or recomendation for someone who

Implementation of Agglomerative Hierarchical Clustering Based on The Classification of Food Ingredients Content of Nutritional Substances

“…This study, using the Agglomerative Hierarchical Clustering algorithm with the Average Linkage method. Based on research conducted by [3] about classifying food ingredients based on nutrient content in food. Based on these problems, the solution taken is to classify the food material data consisting of food ingredients with nutritional content as a source of combustion substances, a source of regulatory substances, and a source of building substances.…”

Clustering of COVID-19 Provinces in Indonesia Using Fuzzy Means Cluster Methods

“…In this study, center of cluster, v kj , ordered center of cluster in each variable that used categorical label from the lowest to the largest [1]. The categorical define characteristic of data which is low, medium, high, and others [12] depending on number of clusters.…”

“…The number of iterations in this study are 36 times for three group of clusters, 34 times for four group of clusters and 27 times for five group of clusters. Category COVID-19 is based on the value of cluster center [12], to simplify interpretation, then the sequenced of each variable has some labels.…”