Optimalisasi Algoritma K-Means Menggunakan Metode Elbow Dalam Pengelompokan Data Stunting

Rifa Safira(1*), Castaka Agus Sugianto(2),

(1) Politeknik TEDC Bandung, Jawa Barat, Indonesia
(2) Politeknik TEDC Bandung, Jawa Barat, Indonesia
(*) Corresponding Author


Stunting is a disease related to malnutrition and results in a lack of growth in toddlers, especially in growing children's height that is not in accordance with their age. Based on observations made by the author at the North Cimahi Health Centre, there are many stunting toddlers, especially in the Cibabat Cimahi North - Cimahi village area. This study aims to optimise the clustering of stunting data using the K-Means algorithm with the elbow method in Cibabat Cimahi Utara Village, the stunting toddler data used is 320. elbow method is used to determine the best number of clusters.  The results of this study indicate that the best cluster from the results of the elbow method is 4: Cluster_0 as many as 98 children under five, Cluster_1 as many as 89 children under five, Cluster_2 as many as 70 children under five, Cluster_3 as many as 63 children under five. Performance value obtained based on average avg. within centroid distance_Cluster_0 as much as 8.844, Cluster_1 as much as 9.793, Cluster_2 as much as 9.818, Cluster_3 as much as 17.726 and Davies Bouldin Index results as much as 0.151. The results of this study can be used as a basis for formulating better policies and suppressing stunting rates in the Cibabat Cimahi Utara Village area

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DOI: https://doi.org/10.30645/brahmana.v5i2.396

DOI (PDF): https://doi.org/10.30645/brahmana.v5i2.396.g392


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