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All Journal RECORD Journal of Loyality and Community Development
Eka Sabna
Universitas Hang Tuah Pekanbaru, Indonesia
Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Industrial & Manufacturing Engineering

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Journal : RECORD Journal of Loyality and Community Development

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Analyzes Health Data By Applying Data Science For Students in The Undergraduate Study Program in Public Health Faculty of Health Hang Tuah University Pekanbaru Eka Sabna; Zupri Henra Hartomi; Yayang Sahira
RECORD: Journal of Loyality and Community Development Vol. 1 No. 1 (2024): January - April 2024
Publisher : Medikun Publisher

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Abstract

Health services are one of the rapidly growing public service sectors currently, resulting in large piles of patient medical record data. This pile of data can provide valuable knowledge if processed in the right way. Data Science is a series of processes for exploring hidden knowledge patterns in large data sets. Data Science can be applied to discover knowledge patterns from patient profiles and health 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. So far, students from the Hang Tuah University Pekanbaru Public Health Study Program have been carrying out the data analysis process using statistics. Community Service Activities aim to enable students to use Data Science as an alternative in analyzing health data. So students can use Data Science to help analyze health data in their research. Data Science techniques discussed include Basic Concepts and Data Science Algorithms. The output of this PkM activity is increasing partner skills, publication in mass media and scientific publications
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; Azlina; Arif Arrafi
RECORD: Journal of Loyality and Community Development Vol. 1 No. 3 (2024): September - December 2024
Publisher : Medikun Publisher

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Abstract

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.
Co-Authors Arif Arrafi Azlina Yayang Sahira Zupri Henra Hartomi