ANALYSIS OF COVID-19 GROWTH TRENDS THROUGH DATA MINING APPROACH AS DECISION SUPPORT

Authors

  • Mohamad Ilyas Abas Universitas Muhammadiyah Gorontalo
  • Irawan Ibrahim Universitas Muhammadiyah Gorontalo
  • Syahrial Universitas Muhammadiyah Gorontalo
  • Rizal Lamusu Universitas Muhammadiyah Gorontalo
  • Umar Sako Baderan Universitas Muhammadiyah Gorontalo
  • Riklan Kango Politeknik Balikpapan, Indonesia

DOI:

10.33395/sinkron.v8i1.11861

Keywords:

Forecasting, Data Mining Algorithm, Covid-19

Abstract

This study aims to analyze the growth trend of covid-19 using prediction algorithms in data mining for covid-19 data throughout Indonesia. This can be used as a decision support to analyze several government policies towards regulatory intervention so far. The method used is the best prediction method in time series data, including Neural Network, SVM, Linear Regression, K-Neirest Neighborn and optimizes it with optimization algorithms. This research is focused on the application of these applications. It is hoped that this research will produce an analysis of the growth trend of Covid cases every day, in addition to its contribution so that it can assist the government in determining the best policy direction and also as an education to the public. in addition, the research will contribute to science in the field of predictive analysis by finding the best RMSE formulation. The results of this study show that Neural Network-Particle Swarm Optimization has the smallest Roort Mean Square Error which is 265,326, and the two Neural Network Genetic Algorithm are 266.801, Neural Network Forward Selection is 275,372 and Neural Network without optimization has the largest RMSE which is 297.204. These results can be used as a reference for the use of similar algorithms in time series data, both Covid-19 data and other data.

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How to Cite

Abas, M. I., Ibrahim, I. ., Syahrial, S., Lamusu, R. ., Baderan, U. S. ., & Kango, R. . (2023). ANALYSIS OF COVID-19 GROWTH TRENDS THROUGH DATA MINING APPROACH AS DECISION SUPPORT. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(1), 101-108. https://doi.org/10.33395/sinkron.v8i1.11861

Issue

Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023

Section

Articles

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Copyright (c) 2023 Mohamad Ilyas Abas, Irawan Ibrahim, Syahrial, Rizal Lamusu, Umar Sako Baderan, Riklan Kango

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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