How to cite this paper
Yulita, I., Ardiansyah, F., Siska, A & Suryana, I. (2023). Time series prediction of novel coronavirus COVID-19 data in west Java using Gaussian processes and least median squared linear regression.Decision Science Letters , 12(2), 291-296.
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Fadhlullah, M. U., Resahya, A., Nugraha, D. F., & Yulita, I. N. (2018, May). Sleep stages identification in patients with sleep disorder using k-means clustering. In Journal of Physics: Conference Series, 1013(1), p. 012162). IOP Publishing.
Gitt, A. K., Karcher, A. K., Zahn, R., & Zeymer, U. (2020). Collateral damage of COVID-19-lockdown in Germany: decline of NSTE-ACS admissions. Clinical Research in Cardiology, 109(12), 1585-1587.
Guo, J., Feng, X. L., Wang, X. H., & van IJzendoorn, M. H. (2020). Coping with COVID-19: Exposure to COVID-19 and Negative Impact on Livelihood Predict Elevated Mental Health Problems in Chinese Adults. International Journal of Environmental Research and Public Health, 17(11), 3857.
Hayes, A. F., & Montoya, A. K. (2017). A tutorial on testing, visualizing, and probing an interaction involving a multicategorical variable in linear regression analysis. Communication Methods and Measures, 11(1), 1-30.
Hui, D. S., Azhar, E. I., Memish, Z. A., & Zumla, A. (2020). Human Coronavirus Infections—Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and SARS-CoV-2. Reference Module in Biomedical Sciences.
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Liu, H., Ong, Y. S., Shen, X., & Cai, J. (2020). When Gaussian process meets big data: A review of scalable GPs. IEEE transactions on neural networks and learning systems, 31(11), 4405-4423.
Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., ... & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International journal of surgery (London, England), 78, 185.
Olivia, S., Gibson, J., & Nasrudin, R. A. (2020). Indonesia in the Time of Covid-19. Bulletin of Indonesian Economic Studies, 56(2), 143-174.
Pati, K. D. (2020, April). Using standard error to find the best robust regression in presence of multicollinearity and outliers. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 266-271). IEEE.
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017). Gaussian process regression for forecasting battery state of health. Journal of Power Sources, 357, 209-219.
Schulz, E., Speekenbrink, M., & Krause, A. (2018). A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology, 85, 1-16.
Sheng, H., Xiao, J., Cheng, Y., Ni, Q., & Wang, S. (2017). Short-term solar power forecasting based on weighted Gaussian process regression. IEEE Transactions on Industrial Electronics, 65(1), 300-308.
Toharudin, T., Caraka, R. E., Chen, R. C., Nugroho, N. T., Tai, S. K., Sueb, M., ... & Pardamean, B. (2020). Bayesian Poisson Model for COVID-19 in West Java Indonesia. Sylwan, 164(6), 279-290.
Weigend, A. S. (2018). Time series prediction: forecasting the future and understanding the past. Routledge.
Wildani, I. M., & Yulita, I. N. (2019, March). Classifying botnet attack on internet of things device using random forest. In IOP Conference Series: Earth and Environmental Science (Vol. 248, No. 1, p. 012002). IOP Publishing.
Yu, C., & Yao, W. (2017). Robust linear regression: A review and comparison. Communications in Statistics-Simulation and Computation, 46(8), 6261-6282.
Yulita, I. N., & Wasito, I. (2013, March). gCLUPS: Graph clustering based on pairwise similarity. In 2013 International Conference of Information and Communication Technology (ICoICT) (pp. 77-81). IEEE.
Yulita, I. N., Fanany, M. I., & Arymurthy, A. M. (2017, September). Combining deep belief networks and bidirectional long short-term memory: Case study: Sleep stage classification. In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 1-6). IEEE.
Yulita, I. N., Fanany, M. I., & Arymurthy, A. M. (2017, October). Sleep stage classification using convolutional neural networks and bidirectional long short-term memory. In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 303-308). IEEE.
Yulita, I. N., Hidayat, A., Abdullah, A. S., & Awangga, R. M. (2018). Feature extraction analysis for hidden Markov models in Sundanese speech recognition. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16(5), 2191-2198.
Yulita, I. N., Julviar, R. R., Triwahyuni, A., & Widiastuti, T. (2019, July). Multichannel Electroencephalography-based Emotion Recognition Using Machine Learning. In Journal of Physics: Conference Series (Vol. 1230, No. 1, p. 012008). IOP Publishing.
Zhao, J., & Han, M. (2016). An efficient model for the prediction of polymerisation efficiency of nano-composite film using Gaussian processes and Pearson VII universal kernel. International Journal of Materials and Product Technology, 52(3-4), 226-237.
Zuhairoh, F., & Rosadi, D. (2020). Indonesian Journal of Science & Technology. Indonesian Journal of Science & Technology, 5(3), 456-462.
Fadhlullah, M. U., Resahya, A., Nugraha, D. F., & Yulita, I. N. (2018, May). Sleep stages identification in patients with sleep disorder using k-means clustering. In Journal of Physics: Conference Series, 1013(1), p. 012162). IOP Publishing.
Gitt, A. K., Karcher, A. K., Zahn, R., & Zeymer, U. (2020). Collateral damage of COVID-19-lockdown in Germany: decline of NSTE-ACS admissions. Clinical Research in Cardiology, 109(12), 1585-1587.
Guo, J., Feng, X. L., Wang, X. H., & van IJzendoorn, M. H. (2020). Coping with COVID-19: Exposure to COVID-19 and Negative Impact on Livelihood Predict Elevated Mental Health Problems in Chinese Adults. International Journal of Environmental Research and Public Health, 17(11), 3857.
Hayes, A. F., & Montoya, A. K. (2017). A tutorial on testing, visualizing, and probing an interaction involving a multicategorical variable in linear regression analysis. Communication Methods and Measures, 11(1), 1-30.
Hui, D. S., Azhar, E. I., Memish, Z. A., & Zumla, A. (2020). Human Coronavirus Infections—Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and SARS-CoV-2. Reference Module in Biomedical Sciences.
Lai, C. C., Shih, T. P., Ko, W. C., Tang, H. J., & Hsueh, P. R. (2020). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): the epidemic and the challenges. International journal of antimicrobial agents, 105924.
Liu, H., Ong, Y. S., Shen, X., & Cai, J. (2020). When Gaussian process meets big data: A review of scalable GPs. IEEE transactions on neural networks and learning systems, 31(11), 4405-4423.
Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., ... & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International journal of surgery (London, England), 78, 185.
Olivia, S., Gibson, J., & Nasrudin, R. A. (2020). Indonesia in the Time of Covid-19. Bulletin of Indonesian Economic Studies, 56(2), 143-174.
Pati, K. D. (2020, April). Using standard error to find the best robust regression in presence of multicollinearity and outliers. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 266-271). IEEE.
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017). Gaussian process regression for forecasting battery state of health. Journal of Power Sources, 357, 209-219.
Schulz, E., Speekenbrink, M., & Krause, A. (2018). A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology, 85, 1-16.
Sheng, H., Xiao, J., Cheng, Y., Ni, Q., & Wang, S. (2017). Short-term solar power forecasting based on weighted Gaussian process regression. IEEE Transactions on Industrial Electronics, 65(1), 300-308.
Toharudin, T., Caraka, R. E., Chen, R. C., Nugroho, N. T., Tai, S. K., Sueb, M., ... & Pardamean, B. (2020). Bayesian Poisson Model for COVID-19 in West Java Indonesia. Sylwan, 164(6), 279-290.
Weigend, A. S. (2018). Time series prediction: forecasting the future and understanding the past. Routledge.
Wildani, I. M., & Yulita, I. N. (2019, March). Classifying botnet attack on internet of things device using random forest. In IOP Conference Series: Earth and Environmental Science (Vol. 248, No. 1, p. 012002). IOP Publishing.
Yu, C., & Yao, W. (2017). Robust linear regression: A review and comparison. Communications in Statistics-Simulation and Computation, 46(8), 6261-6282.
Yulita, I. N., & Wasito, I. (2013, March). gCLUPS: Graph clustering based on pairwise similarity. In 2013 International Conference of Information and Communication Technology (ICoICT) (pp. 77-81). IEEE.
Yulita, I. N., Fanany, M. I., & Arymurthy, A. M. (2017, September). Combining deep belief networks and bidirectional long short-term memory: Case study: Sleep stage classification. In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 1-6). IEEE.
Yulita, I. N., Fanany, M. I., & Arymurthy, A. M. (2017, October). Sleep stage classification using convolutional neural networks and bidirectional long short-term memory. In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 303-308). IEEE.
Yulita, I. N., Hidayat, A., Abdullah, A. S., & Awangga, R. M. (2018). Feature extraction analysis for hidden Markov models in Sundanese speech recognition. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16(5), 2191-2198.
Yulita, I. N., Julviar, R. R., Triwahyuni, A., & Widiastuti, T. (2019, July). Multichannel Electroencephalography-based Emotion Recognition Using Machine Learning. In Journal of Physics: Conference Series (Vol. 1230, No. 1, p. 012008). IOP Publishing.
Zhao, J., & Han, M. (2016). An efficient model for the prediction of polymerisation efficiency of nano-composite film using Gaussian processes and Pearson VII universal kernel. International Journal of Materials and Product Technology, 52(3-4), 226-237.
Zuhairoh, F., & Rosadi, D. (2020). Indonesian Journal of Science & Technology. Indonesian Journal of Science & Technology, 5(3), 456-462.