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All Journal Media Statistika Statistika Jurnal Gaussian MIX : Jurnal Ilmiah Manajemen Indonesian Journal of Applied Statistics (JRAMB) Jurnal Riset Akuntansi Mercu Buana
Di Asih I Maruddani
Department Of Statistics, Faculty Of Science And Mathematics, Diponegoro University
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIA Puspita Kartikasari; Hasbi Yasin; Di Asih I Maruddani
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.44-55

Abstract

Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death. Therefore, fast and appropriate action is needed to reduce the spread of the COVID-19 pandemic. One of the way is to build a prediction model so that it can be a reference in taking steps to overcome them. Because of the nature of transmission of this disease which is so fast and massive cause extreme data fluctuations and between objects whose observational distances are far enough correlated with each other (long memory). The result of this determination is the best ARFIMA model obtained to predict additional of recovering cases of COVID-19 is (1,0,489.0) with an SMAPE value of 12,44%, while the case of death is (1.0.429.0) with SMAPE value of 13,52%. This shows that the ARFIMA model can accommodate well the long memory effect, resulting in a small bias. Also in estimating model parameters, it is also simpler. For cases of recovery and death, the number is increasing even though the case of death is still very high compared to cases of recovery.
Co-Authors Abdul Hoyyi Abdurakhman Abdurakhman Agus Rusgiyono Ahmad Badruttamam Alifia Hana Linda Rachmawati Atikah Mufidah Budi Warsito Dede Andrianto Dedi Baleo Pasaribu Delsy Nurutsaniyah Desi Nur Rahma Diah Safitri Dina Rahma Prihatiningsih Dwi Harti Pujiana Dwi Ispriyanti Eka Anisha Eny Sulistyowati Fatiya Nur Umma Fredy Yoseph Marianto Hanik Malikhatin Hasbi Yasin Hasbi Yasin Henny Setyowati Juria Ayu Handini Khoirul Anam Lina Tanasya Maria Odelia Mas'ad Mas'ad Meiliawati Aniska Mia Anastasia Sinulingga Mustafid Mustafid Mutik Dian Prabaning Tyas Nidaul Khoir Nur Fitriyah Nur Khofifah Purwati Ayuningtyas Puspita Kartikasari Puspita Kartikasari Putri Devitasari Reno Penggalih Surya Wardhani Reyuli Andespa Ria Epelina Situmorang Rita Rahmawati Rukun Santoso Salsabila Syifa Binsanno Setiani Setiani Sudarno Sudarno Sugito Sugito Suparti Suparti Tarno Tarno Tatik Widiharih Trimono Trimono Trimono Trimono Tutut Dewi Astuti Tutut Dewi Astuti Yustian Dwi Saputra