WebGIS Visualization of Infectious Disease Clustering with a Hybrid Sequential Approach

Infectious diseases are diseases that can be transmitted through various media. Based on the results of research that has been conducted at several Health Center in Gowa Regency, it is found that from 2020 to 2022, Health Center of Bontomarannu is one of the Health Center in Gowa Regency that handles many cases of infectious diseases, namely 212 cases of tuberculosis, 224 cases of dengue fever, and 427 cases of typhoid fever. This makes it difficult for the Health Center to classify what factors cause the increase in these diseases every day for one reason, namely the limitations of medical personnel to manually detect the indicators that cause the emergence of these diseases through medical record data. Based on data obtained through questionnaires given to patients of the Bontomarannu Health Center, there are several variables associated with infectious diseases. Therefore, this study aims to visualize the results of clustering infectious disease data (tuberculosis, dengue fever, and typhoid) per village in Gowa Regency in the form of WebGIS. The method used is K-means clustering which will be optimized using the Particle Swarm Optimization (PSO) algorithm to obtain better results. Moreover in the WebGIS visualization section, the frontend will be made using NextJS, the backend using Flask Py-thon, and for DBMS using SQLAlchemy. This WebGIS visualization will display cluster information for each village in Gowa Regency. Through quantitative analysis and clustering, the study aims to visualize the data and identify patterns and trends associated with infectious diseases in Gowa Regency, ultimately aiding in better decision-making and resource allocation for disease prevention and control.