Open Health Data Development for Machine Learning-based Resource Sharing: Indonesia Case Study

(1) * Vitri Tundjungsari Mail (Universitas Esa Unggul, Indonesia)
*corresponding author


Currently, data has a role as a vital requirement in research, especially in the health domain. Unfortunately, Indonesia does not yet have many quality datasets that can be obtained easily. Even though the central and regional governments in the Republic of Indonesia have started implementing Open Data Initiatives, the fact is that the data obtained is still not good enough to be used as datasets. This is where open data is required to enable researchers to perform data analytics and visualization, especially research that uses techniques in machine learning. This research fills the gap to solve the above-mentioned problems by providing resource-sharing datasets. We have built an Open Data Health portal from various trusted data sources so that 200 datasets have been collected. The architecture used in our proposed Open Health Data is a single model. This model is suitable for relatively small datasets and is controlled by a single agency. Our test results show that the portal we build can function properly, but lacks data quality and usability. This requires further work, especially in terms of improving the quality and adequacy of the amount of data to be used as datasets, especially for the purposes of resource sharing and machine learning-based research.


Open data, Health data, Open health data, data sharing, resource sharing



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