Visualisasi Data Pemetaan Daerah Hipertensi Menggunakan Algoritma K-Means - Arbi, Haris Andika; Putri, Raissa Amanda (Program Studi Teknik Informatika Universitas Pamulang, 2023)
According to data from the Ministry of Health in 2019, the number of hypertension patients continues to rise and is projected to reach approximately 1.5 billion by 2025. Additionally, an estimated 9.4 million people die each year due to hypertension and its complications. The current challenge is the difficulty faced by the public in understanding the information contained in the data, leading to a decrease in awareness of the importance of maintaining health to prevent and control hypertension. This research proposes the use of the K-Means clustering algorithm with spatial data visualization techniques to communicate information from the data more effectively to the public. The proposed method allows the K-Means clustering algorithm to group regions based on hypertension characteristics such as prevalence and risk factors, while spatial data visualization techniques are used to display the data in the form of a geographic map of hypertension areas. The results of applying this method show that the K-Means clustering algorithm produces five clusters that reflect different characteristics of hypertension, and spatial data visualization techniques generate a geographic map of hypertension areas with five different colors representing the highest to lowest characteristic groups of hypertension. This research provides a clear and easily understandable way to communicate information from the data, thereby assisting the public in efforts to prevent and control hypertension and its risk factors.