Multi-Temporal Factors to Analyze Indonesian Government Policies regarding Restrictions on Community Activities during COVID-19 Pandemic

Syafrial Fachri Pane - Telkom University, Terusan Buahbatu, Bandung, Jawa Barat, 40257, Indonesia
Adiwijaya Adiwijaya - Telkom University, Terusan Buahbatu, Bandung, Jawa Barat, 40257, Indonesia
Mahmud Dwi Sulistiyo - Telkom University, Terusan Buahbatu, Bandung, Jawa Barat, 40257, Indonesia
Alfian Akbar Gozali - Telkom University, Terusan Buahbatu, Bandung, Jawa Barat, 40257, Indonesia

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Concerning the implementation of the government policy regarding the Restriction of Community Activities (PPKM) during the COVID-19 pandemic era, there are still discrepancies in the economic sector and population mobility. This issue emerges due to irrelevant data and information in one region of Indonesia. The data differences should be carefully solved when implementing the PPKM policy. Besides, the PPKM must also pay attention to some specific factors related to the real conditions of a region, such as the data on the epidemiology of COVID-19, economic situations, and population mobility. These three are called Multi Factors. Then, based on the data, COVID-19 has a specific spreading period that cannot be repeated and thus is called temporal. Therefore, using the Multi-Temporal Factors approach to identify their correlation with the PPKM policy by applying Machine Learning, such as the Multiple Linear Regression model and Dynamic Factors, is essential. This research aims to analyze the characteristics and correlations of the COVID-19 pandemic data and the effectiveness of the government's policy on community activities (PPKM) based on the data quality. The results show that the accuracy of the multiple linear regression models is 84%. The Dynamic Factor shows that the five most important factors are idr_close, positive, retail_recreation, station, and healing. Based on the ANOVA test, all independent variables significantly influence the dependent one. The linear multiple regression models do not display any symptoms of heteroscedasticity. Thus, based on the data quality, the implementation of PPKM by the government has a practical impact.


COVID-19; pandemic model; Indonesian government; community restriction policy; multi-temporal.

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Public Health Emergency of International Concern, “WHO Coronavirus (COVID-19) Dashboard,” WHO, p. 1, 2021. [Online]. Available:

E. W. Mansi, “Environmental Health Aspect of The Novel Corona Virus Disease and Its Global Impact,” J. Appl. Sci. Eng. Technol. Educ., vol. 3, no. 2, pp. 151–159, 2021, doi: 10.35877/454ri.asci131.

P. Bose, S. Roy, and P. Ghosh, “A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research,” IEEE Access, vol. 9, pp. 78341–78355, 2021, doi: 10.1109/ACCESS.2021.3082108.

Public Health Emergency of International Concern, “WHO declared COVID-19 emergency a global pandemic,” WHO, 2021. (accessed October 20, 2021).

L. J. Muhammad, E. A. Algehyne, S. S. Usman, A. Ahmad, C. Chakraborty, and I. A. Mohammed, “Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset,” SN Comput. Sci., vol. 2, no. 1, pp. 1–13, 2021, doi: 10.1007/s42979-020-00394-7.

Q. Lin et al., “A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action,” Int. J. Infect. Dis., vol. 93, pp. 211–216, 2020, doi: 10.1016/j.ijid.2020.02.058.

S. Lee, T.-Q. Peng, M. K. Lapinski, M. M. Turner, Y. Jang, and A. Schaaf, “Too stringent or too Lenient: Antecedents and consequences of perceived stringency of COVID-19 policies in the United States,” Heal. Policy OPEN, vol. 2, no. July, p. 100047, 2021, doi: 10.1016/j.hpopen.2021.100047.

SATGAS COVID 19 Indonesia, “Peta Sebaran COVID 19,” SATGAS COVID 19 Indonesia, 2021.

Rohmi Aida Nur, “Daftar 10 Negara dengan Kasus Covid-19 Tertinggi di Asia, Indonesia Nomor 4,” Kompas, 2020.

K. D. N. R. Indonesia, “Pemberlakuan Pembatasan Kegiatan Masyarakat Darurat COVID 19 Di Wilayah Jawa dan BALI,” 2021.

L. Thunström, S. C. Newbold, D. Finnoff, M. Ashworth, and J. F. Shogren, “The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19,” J. Benefit-Cost Anal., vol. 11, no. 2, pp. 179–195, 2020, doi: 10.1017/bca.2020.12.

A. Kaim, T. Gering, A. Moshaiov, and B. Adini, “Deciphering the covid-19 health economic dilemma (Hed): A scoping review,” Int. J. Environ. Res. Public Health, vol. 18, no. 18, 2021, doi: 10.3390/ijerph18189555.

A. Akbar Gozali, “Pre-Development Analysis of Truther Framework: A Platform to Verify Information Authenticity,” in 2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 2020, pp. 352–357. doi: 10.1109/ICRAMET51080.2020.9298635.

R. Wang, “Measuring the Effect of Government Response on COVID-19 Pandemic: Empirical Evidence from Japan,” COVID, vol. 1, no. 1, pp. 276–287, Aug. 2021, doi: 10.3390/covid1010022.

L. Chen, D. Raitzer, R. Hasan, R. Lavado, and O. Velarde, “What Works to Control COVID-19? Econometric Analysis of a Cross-Country Panel,” SSRN Electron. J., no. 625, p. 44, 2020, doi: 10.2139/ssrn.3785083.

S. F. Pane, R. R. Fajri, A. G. Putrada, N. Alamsyah, M. N. Fauzan, and R. M. Awangga, “Non-Academic Factors Analysis Impacting Students’ Online Learning During the COVID-19 Pandemic in Universitas Logistic and Bisnis Internasional,” in 2022 2nd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), 2022, pp. 144–149. doi: 10.1109/ICICyTA57421.2022.10037986.

S. F. Pane, Heriyanto, A. G. Putrada, N. Alamsyah, and M. N. Fauzan, “The Influence of The COVID-19 Pandemics in Indonesia On Predicting Economic Sectors,” in 2022 Seventh International Conference on Informatics and Computing (ICIC), 2022, pp. 1–6. doi: 10.1109/ICIC56845.2022.10006897.

S. F. Pane, Adiwijaya, M. D. Sulistiyo, and A. A. Gozali, “LSTM and ARIMA for Forecasting COVID-19 Positive and Mortality Cases in DKI Jakarta and West Java,” in 2022 Seventh International Conference on Informatics and Computing (ICIC), 2022, pp. 1–6. doi: 10.1109/ICIC56845.2022.10006959.

J. Rocklöv and A. A. Gayle, “Data Science and Machine Learning: Mathematical and Statistical Methods. D.P. Kroese, Z.I. Botev, T. Taimre, and R. Vaisman,” Int. J. Epidemiol., vol. 49, no. 6, pp. 2094–2095, 2020, doi: 10.1093/ije/dyaa072.

L. Liu, Z. Zhang, H. Wang, S. Wang, S. Zhuang, and J. Duan, “Comparing modelling approaches for the estimation of government intervention effects in COVID-19: Impact of voluntary behavior changes,” PLoS One, vol. 18, no. 2, p. e0276906, 2023.

S. F. Pane and J. Ramdan, “Pemodelan Machine Learning: Analisis Sentimen Masyarakat Terhadap Kebijakan PPKM Menggunakan Data Twitter,” J. Sist. Cerdas, vol. 5, no. 1, pp. 12–20, 2022.

E. Puniach, W. Gruszczyński, T. Stoch, D. Mrocheń, P. P. E. Ćwikakała Pawełand Sopata, and W. Matwij, “Determination of the coefficient of proportionality between horizontal displacement and tilt change using UAV photogrammetry,” Eng. Geol., vol. 312, p. 106939, 2023.

N. Marquez, J. A. Ward, K. Parish, B. Saloner, and S. Dolovich, “COVID-19 incidence and mortality in federal and state prisons compared with the US population, April 5, 2020, to April 3, 2021,” JAMA, vol. 326, no. 18, pp. 1865–1867, 2021.

J. Toor et al., “COVID-19 impact on routine immunisations for vaccine-preventable diseases: Projecting the effect of different routes to recovery,” vaccine, vol. 40, no. 31, pp. 4142–4149, 2022, doi:

A. Warsame et al., “Excess mortality during the COVID-19 pandemic: a geospatial and statistical analysis in Mogadishu, Somalia,” Int. J. Infect. Dis., vol. 113, pp. 190–199, 2021.

F. Bräuning and S. J. Koopman, “Forecasting macroeconomic variables using collapsed dynamic factor analysis,” Int. J. Forecast., vol. 30, no. 3, pp. 572–584, 2014, doi:

C. Doz and P. Fuleky, “Dynamic factor models,” Macroecon. Forecast. Era Big Data Theory Pract., pp. 27–64, 2020.

M. Shintani and Z.-Y. Guo, “Improving the finite sample performance of autoregression estimators in dynamic factor models: A bootstrap approach,” Econom. Rev., vol. 37, no. 4, pp. 360–379, 2018.

F. Blasques, M. H. Hoogerkamp, S. J. Koopman, and I. van de Werve, “Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data,” Int. J. Forecast., vol. 37, no. 4, pp. 1426–1441, 2021, doi:

D. J. Eck, “Bootstrapping for multivariate linear regression models,” Stat. Probab. Lett., vol. 134, pp. 141–149, 2018, doi:

S. Rath, A. Tripathy, and A. R. Tripathy, “Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model,” Diabetes & Metab. Syndr. Clin. Res. & Rev., vol. 14, no. 5, pp. 1467–1474, 2020.

D. V. Gradov et al., “Modelling of a continuous veneer drying unit of industrial scale and model-based ANOVA of the energy efficiency,” Energy, vol. 244, p. 122673, 2022, doi:

A. Korgal, S. Upadhyaya, and A. T, “Grain refinement of aluminium 4032 alloy with the impact of vibration using Taguchi technique and analysis of variance (ANOVA),” Mater. Today Proc., vol. 54, pp. 507–512, 2022, doi:

Y. Feng, Y. Huang, and X. Ma, “The application of Student’s t-test in internal quality control of clinical laboratory,” Front. Lab. Med., vol. 1, no. 3, pp. 125–128, 2017, doi:

R. Dhanya, I. R. Paul, S. S. Akula, M. Sivakumar, and J. J. Nair, “F-test feature selection in Stacking ensemble model for breast cancer prediction,” Procedia Comput. Sci., vol. 171, pp. 1561–1570, 2020, doi:

H. Luepsen, “ANOVA with binary variables: the F-test and some alternatives,” Commun. Stat. Comput., pp. 1–25, 2021.

I. N. T. Sutaguna, M. Yusuf, R. Ardianto, and P. Wartono, “The Effect Of Competence, Work Experience, Work Environment, And Work Discipline On Employee Performance,” Asian J. Manag. Entrep. Soc. Sci., vol. 3, no. 01, pp. 367–381, 2023.

P. Mugebe, M. Kizil, M. Yahyaei, and R. K. Y. Low, “Regression Analysis Applicability in Investigating Mining Technology and Macroeconomic Impact on Mining Companies’ Share Price--Case Study: Iron Ore Suppliers,” Available SSRN 4354878.

A. F. Schmidt and C. Finan, “Linear regression and the normality assumption,” J. Clin. Epidemiol., vol. 98, pp. 146–151, 2018, doi:

J. Uttley, “Power Analysis, Sample Size, and Assessment of Statistical Assumptions—Improving the Evidential Value of Lighting Research,” LEUKOS, vol. 15, no. 2–3, pp. 143–162, 2019, doi: 10.1080/15502724.2018.1533851.

M. Schwarz, J. Trippel, M. Engelhart, and M. Wagner, “Dynamic alpha factor prediction with operating data-a machine learning approach to model oxygen transfer dynamics in activated sludge,” Water Res., p. 119650, 2023.

S. Adrianto, I. H. N. Balqis, C. Z. N. Soetanto, and M. Ohyver, “Cochrane orcutt method to overcome autocorrelation in modeling factors affecting the number of hotel visitors in Indonesia,” Procedia Comput. Sci., vol. 216, pp. 630–638, 2023, doi:

F.-K. Sun, C. Lang, and D. Boning, “Adjusting for autocorrelated errors in neural networks for time series,” Adv. Neural Inf. Process. Syst., vol. 34, pp. 29806–29819, 2021.

J. Runge, “Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets,” in Conference on Uncertainty in Artificial Intelligence, 2020, pp. 1388–1397.

Y. Yang and T. Mathew, “The simultaneous assessment of normality and homoscedasticity in linear fixed effects models,” J. Stat. Theory Pract., vol. 12, pp. 66–81, 2018.