Prediksi dan visualisasi penyakit COVID-19 menggunakan kombinasi Prophet dan GeoPandas
Keywords:COVID-19, Prophet, Forecasting, Seasonal, GIS
Covid-19 is spreading very rapidly. Indonesia is one of the countries with the highest cases in Southeast Asia. The purpose of this research is to use machine learning models with the help of tools such as Prophet to predict the trend of the Covid-19 outbreak in Indonesia. Obtained data will be visualized using a Geographic Information System (GIS) with Geopandas, which is used to visualize the spread of Covid-19 in Indonesia. Predictions with three tuning methods using Prophet with trend flexibility and holiday effects scored the best, with 0.68 for RMSLE and 1070 for MAE. Based on the use of Geopandas for Covid-19 cases in Indonesia, Geopandas can be used to visualize geospatial data effectively.
C. B. A. Satrio, W. Darmawan, B. U. Nadia, and N. Hanafiah, “Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET,” Procedia Computer Science, vol. 179, pp. 524–532, 2021.
S. Siami-Namini, N. Tavakoli, and A. S. Namin, “A comparison of ARIMA and LSTM in forecasting time series,” in 2018 17th IEEE international conference on machine learning and applications (ICMLA), IEEE, 2018, pp. 1394–1401.
Z. Ye, “Air pollutants prediction in shenzhen based on arima and prophet method,” in E3S Web of Conferences, EDP Sciences, 2019, p. 05001.
T. Chafiq, M. Ouadoud, and K. Elboukhari, “Covid-19 forecasting in morocco using fbprophet facebook’s framework in python,” Int J, vol. 9, no. 5, 2020.
G. A. Papacharalampous and H. Tyralis, “Evaluation of random forests and Prophet for daily streamflow forecasting,” Advances in Geosciences, vol. 45, pp. 201–208, 2018.
T. Bashir, C. Haoyong, M. F. Tahir, and Z. Liqiang, “Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN,” Energy Reports, vol. 8, pp. 1678–1686, 2022.
E. Zunic, K. Korjenic, K. Hodzic, and D. Donko, “Application of facebook’s prophet algorithm for successful sales forecasting based on real-world data,” arXiv preprint arXiv:2005.07575, 2020.
V. K. R. Chimmula and L. Zhang, “Time series forecasting of COVID-19 transmission in Canada using LSTM networks,” Chaos, Solitons & Fractals, vol. 135, p. 109864, 2020.
N. Wu, B. Green, X. Ben, and S. O’Banion, “Deep transformer models for time series forecasting: The influenza prevalence case,” arXiv preprint arXiv:2001.08317, 2020.
S.-Y. Shih, F.-K. Sun, and H. Lee, “Temporal pattern attention for multivariate time series forecasting,” Machine Learning, vol. 108, pp. 1421–1441, 2019.
S. Shastri, K. Singh, S. Kumar, P. Kour, and V. Mansotra, “Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study,” Chaos, Solitons & Fractals, vol. 140, p. 110227, 2020.
B. M. Pavlyshenko, “Machine-learning models for sales time series forecasting,” Data, vol. 4, no. 1, p. 15, 2019.
M. Dârdală, T. F. Furtună, and C. Ioniță, “Design and implementation of a software component for geospatial data visualization in Excel,” in Proceedings of the IE 2019 International Conference, 2019, pp. 293–298.
C. Rojas, R. Linfati, R. F. Scherer, and L. Pradenas, “Using Geopandas for locating virtual stations in a free-floating bike sharing system,” Heliyon, p. e12749, 2023.
C. Kavuma, D. Sandoval, and H. K. J. de Dieu, “Analysis of power generating plants and substations for increased Uganda’s electricity grid access,” AIMS Energy, vol. 9, no. 1, pp. 178–192, 2021.
S. J. Taylor and B. Letham, “Forecasting at scale,” The American Statistician, vol. 72, no. 1, pp. 37–45, 2018.
C. Chandra and S. Budi, “Analisis Komparatif ARIMA dan Prophet dengan Studi Kasus Dataset Pendaftaran Mahasiswa Baru,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 6, no. 2, 2020.
How to Cite
Copyright (c) 2023 AITI
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in AITI: Jurnal Teknologi Informasi is licensed under a Creative Commons Attribution 4.0 International License.