ABSTRACT
Tuberculosis (TB) remains a public health problem in the world. Second disease causing death after Covid-19. In 2020, case findings of TB cases in Indonesia slightly decreased compared to 2019, from 568.987 to 351.936 cases. To combat the disease, Indonesia has adopted the End TB program, targeting to reduce TB incidence to 65 cases per 100,000 population by 2030. At the same time, many challenges need to be overcome, such as low coverage of TB treatment, delay of diagnosis and treatment, and other factors associated. This paper aims to propose a model of an Integrated Surveillance System of Tuberculosis Based on the Internet of Things (IoT). The research will employ the End-to-End Life Cycle Automation System approach. Data collection will use two sources of data, primary and secondary data. The various research instruments (Questionnaire, interview guidelines, checklist observation, and IoT) will be used to capture primary data in this research. Secondary data sources will use reports of TB in multilevel (district/city, province, and national level), medical records of TB patients, news of TB prevention and treatment programs, demography and geography information, and poverty level. The data will produce a model of an integrated surveillance system. The field test will be conducted on the design and continuously improved based on the result. The information provided by the system will be available on a dashboard as a data visualization that can be easily accessed. This system will provide rapid and precise analysis to help the government achieve the Free TB agenda 2030. The system will help develop an effective and efficient TB prevention program in the community for health services based on their need. © 2022 IEEE.