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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References

Abas, Mohamad Ilyas, and Alter Lasarudin. 2019. “Prediction of Arrival Domestic and Foreign Tourists Based on Regions Using Neural Network Algorithm Based on Genetic Algorithm Prediction of Arrival Domestic and Foreign Tourists Based on Regions Using Neural Network Algorithm Based on Genetic Algorithm.” doi: 10.1088/1742-6596/1175/1/012045.

Abas, Mohamad Ilyas, Abdul Syukur, and Moch. Arief Soeleman. 2017. “Prediksi Rentet Waktu Jumlah Penumpang Bandara Menggunakan Algoritma Neural Network Berbasis Genetic Algorithm.” Jurnal Teknologi Informasi 13:101–14.

Aggarwal, Charu C. 2015. Data Mining: The Textbook.

Arianto, Fra Siskus Dian. 2020. “Prediksi Kasus COVID-19 Di Indonesia Menggunakan Metode Backpropagation Dan Fuzzy Tsukamoto.” Jurnal Teknologi Informasi 4(1):120–27. doi: 10.13140/RG.2.2.34286.02885.

Hikmawan, Sisferi, Amsal Pardamean, and Siti Nur Khasanah. 2020. “Sentimen Analisis Publik Terhadap Joko Widodo Terhadap Wabah Covid-19 Menggunakan Metode Machine Learning.” Jurnal Kajian Ilmiah 20(2):167–76. doi: 10.31599/jki.v20i2.117.

Maimon, O. and Last M. 2000. Knowledge Discovery and Data Mining. Kluwer Acamdemic.

North, Matthew. n.d. Data Mining for the Masses.

Pitoyo, Edy Prihantoro, Noviawan Rasyid Ohorella. 2021. “Makna Zona Merah Covid 19 Di Dki Jakarta ( Studi Semiotika Charles Sander Peirce Berita Kompas . Com ) Meaning of the Red Zone Covid 19 in Dki Jakarta.” 15(1):85.

R, Turban, Rainer R. and Potter R. 2005. Introduction to Informatio Technology. USA: John Wiley & Sons, Inc.

Sheikh, F., S. Karthick, D. Malathi, and ... 2016. “Analysis of Data Mining Techniques for Weather Prediction.” Indian Journal of ….

Sudipa, I. Gede Iwan, I Nyoman Alit Arsana, and Made Leo Radhitya. 2020. “Penentuan Tingkat Pemahaman Mahasiswa Terhadap Social Distancing Menggunakan Algoritma C4.5.” SINTECH (Science and Information Technology) Journal 3(1):1–7. doi: 10.31598/sintechjournal.v3i1.562.

Sulistyowati, Daning Nur, Norma Yunita, Siti Fauziah, and Risca Lusiana Pratiwi. 2020. “Implementation of Data Mining Algorithm for Predicting Popularity of Playstore Games in the Pandemic Period of Covid-19.” 6(1):95–100. doi: 10.33480/jitk.v6i1.1425.

Tan, p et all. 2006. Introduction to Data Mining. Boston: Pearsion Education.

Watratan, Alvina Felicia, Arwini Puspita, and Dikwan Moeis. 2020. “Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia.” Journal of Applied Computer Science and Technology ( Jacost ) 1(1):7–14.

Wei, W. W. S., and James D. Hamilton. 1994. “Time Series Analysis.” Prentice Hall New Jersey 1994 SFB 373(Chapter 5):837–900.

Winata, Koko Adya, Qiqi Yuliati Zaqiah, Supiana, and Helmawati. 2021. “Kebijakan Pendidikan Di Masa Pandemi.” Https://Jurnal.Um-Palembang.Ac.Id/Jaeducation/Article/View/3338 4:1–6.

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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, 8(1), 101-108. https://doi.org/10.33395/sinkron.v8i1.11861