ABSTRACT
In February 2021, the Malaysian government launched a vaccination campaign against coronavirus disease 2019 (COVID-19). However, there is a problem in identifying suitable location for vaccination centre should be allocated. At the same time, there are population that living in the rural area and has difficulty to travel to the nearest vaccination centres. Therefore, based on the data of vaccination rate collected by Ministry of Health, the proposed project aims to classify and visualise the data based on number of COVID-19 vaccination rate and centre in Malaysia for the adult and adolescent populations. This project uses machine learning technique called Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The system is developed in Python language platform for back-end development, and PyCharm is utilised for front-end development in web-based platform. This project follows four phases in Waterfall model: requirement analysis, design, implementation, and testing. The system is evaluated for functionality and usability based on user satisfaction and the accuracy of the model. The results of the testing shows that all the functionality of the system have been implemented successfully in the system. The system also rated good according to SUS Questionnaire in usability testing with score of 88.5%. The model of machine learning also achieved a good accuracy score which is greater than 0.3. In conclusion, the data visualization web-based application helps the Malaysian government to identify location for additional vaccination centres in strategic locations and it helps Malaysian people to locate nearby vaccination centres in their area. © 2022 IEEE.