HARIJANTO, BINAR EKO (2022) Implementation Analysis of Synthetic Data Vault for Medical Workforce Number Prediction. Other thesis, Universitas Katholik Soegijapranata Semarang.
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Abstract
The Covid 19 pandemic has proven us the importance of medical workforce distribution, as insufficiency of health professionals may lead into patient abandonments, eventually casualties. Moreover, healthcare would be more effective if the government have access to future medical workforce numbers. Unfortunately, the implementation of prediction algorithm within the field is not yet present, and high quality medical workforce data in Indonesia are rare. This research approaches said problem by utilizing Support Vector Regression, and Random Forest algorithm to predict future numbers of medical workforce within Semarang city. To fight data scarcity, Synthetic Data Vault technique is implemented to substitute the real dataset. The results are in the form of time series data prediction and accuracy tests using MSE (Mean Square Error) and MAPE (Mean Absolute Percentage Error) to compare the performance of presented methods.
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | mr AM. Pudja Adjie Sudoso |
| Date Deposited: | 26 Oct 2022 09:28 |
| Last Modified: | 26 Oct 2022 09:28 |
| URI: | http://repository.unika.ac.id/id/eprint/30030 |
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