Community Service Increasing Lecturer Competence Through Data Mining Training Using Rapidminer Tools in the Master of Public Health Study Program, Faculty of Health, Hang Tuah University, Pekanbaru

Eka Sabna (Universitas Hang Tuah Pekanbaru, Indonesia)
Azlina (Universitas Hang Tuah Pekanbaru, Indonesia)
Arif Arrafi (Universitas Hang Tuah Pekanbaru, Indonesia)

RECORD Journal of Loyality and Community Development
Vol. 1 No. 3 (2024): September - December 2024

Technological developments in the Industry 4.0 era open up enormous opportunities in data collection and processing. Currently, health data makes up around 30% of all global data, and by 2025, this figure will reach 36%. The ability to understand such segmented data can provide a major strategic advantage to medical organizations everywhere. This large amount of data can be carried out in research using various approaches, including using the Data Mining Approach. Data Mining can be applied to find knowledge patterns from patient profiles and health history data (patient history data). The knowledge gained can be used for analysis and decision making, including to predict the type of disease, determine the pattern of disease spread, and see the effectiveness of treatment. Some Lecturers in the Master of Public Health Study Program do not know much about the basic concepts of Data Analysis using Data Mining concepts. This activity aims to provide knowledge about analyzing health data using a Data Mining Approach to lecturers in the Master of Public Health Study Program. The Data Mining technique discussed is the prediction of diabetes using the Decision Tree Algorithm. The data used was obtained from public data, namely Kaggle.