Analisis Prediksi Data Kasus Covid-19 di Provinsi Lampung Menggunakan Recurrent Neural Network (RNN)

  • Akhdan Aziz Ghozi Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera
  • Ayu Aprianti Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera
  • Ahmad Dzaki Putra Dimas Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera
  • Rifky Fauzi Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera, Lampung Selatan 35365, Indonesia

Abstract

This study aims to examine the architectural performance of the Recurrent Neural Network (RNN) model in predicting Covid-19 cases in Lampung Province. The RNN method is part of Deep Learning which will be used to model data on Covid-19 cases in Lampung Province from March 26, 2020 to March 28, 2021. The RNN model was chosen because the Covid-19 data is in the form of a time series and the advantages of RNN are that it can capture information on the data time series using multiple network layers which allow better modeling and resulting in high prediction accuracy. The data is divided into 3, namely active cases, recovered cases, and dead cases. After preparing the data, the 368 data were divided into 294 initial latih data and 74 test data. After latih on the data for each data, then a test is carried out on the data for each data as a reference for predicting the latest data. The most optimal results show the cumulative active case model with RMSE=0.0022; for cumulative recovery cases obtained RMSE = 0.0007; while the cumulative death cases obtained RMSE = 0.0012. Based on the modeling error, then make predictions on the three cases which results in RMSE = 0.001 for cumulative active cases; RMSE=0.0027 for cumulative recovery cases; and RMSE=0.001 for cumulative death cases.

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Author Biographies

Akhdan Aziz Ghozi, Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera

Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera

Ayu Aprianti, Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera

Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera

Ahmad Dzaki Putra Dimas, Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera

Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera

Rifky Fauzi, Program Studi Matematika, Jurusan Sains, Institut Teknologi Sumatera, Lampung Selatan 35365, Indonesia

Pusat Riset Prediksi dan Pemodelan Risiko Bahaya dan Bencana Institut Teknologi Sumatera

References

[1] Sutaryo dkk, BUKU PRAKTIS VIRUS CORONA 19 (COVID-19). Gadjah Mada University Press, 2020.
[2] Wang Zhou (Ed.), Buku Panduan Pencegahan Corona Virus. Wuhan Center for Disease Control and Prevention, 2020.
[3] “Who coronavirus (COVID-19) dashboard,” World Health Organization, [Online]. Tersedia: https://covid19.who.int/ [Dipetik 28 Agustus 2021].
[4] Hendratno, “Covid-19 Indonesia data,” Kaggle, 10 Juli 2021. [Online]. Tersedia: https://www.kaggle.com/hendratno/covid19-Indonesia [Dipetik 23 Agustus 2021].
[5] S. Zahara, Sugianto, dan M. Bahril Ilmiddafiq, “Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 3, no. 3, pp. 57-36, 2019.
[6] M. Abdul Dwiyanto Suyudi, Esmeralda C. Djamal, dan Asri Maspupah, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network”, Seminar Nasional Aplikasi Teknologi dan Informasi (SNATi), 3 Agustus 2019.
[7] Sabita, Harry, Riko Herwanto, “Pantauan Prediktif Covid-19 Dengan Menggunakan Metode SIR dan Model Statistik Di Indonesia,” Jurnal Teknika, vol.14, no. 02, pp.145-150, 2020.
[8] Hardiyanti, S. A., Shofiyah, Q, “PREDIKSI KASUS COVID-19 DI INDONESIA MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS),” Prosiding Seminar Nasional Terapan Riset Inovatif (SENTRINOV), 6(1), pp.974-981, 2020.
[9] Syafa’ah, L., Lestandy, M, “Penerapan Deep Learning untuk Prediksi Kasus Aktif Covid-19,” J-SAKTI (Jurnal Sains Komputer dan Informatika), 5(1), pp.453-457, 2021.
[10] Li Deng and Dong Yu, "Deep Learning: Methods and Applications," Foundations and Trends® in Signal Processing, vol.7, no. 3–4, pp 197-38, 2014.J.
[11] Patterson and A. Gibson, in Deep Learning: A Practitioner's Approach, USA: O’Reilly Media, 2017.
[12] A. Hassan, I. Shahin and M. B. Alsabek, "COVID-19 Detection System using Recurrent Neural Networks," 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), 2020, pp. 1-5, doi: 10.1109/CCCI49893.2020.9256562.
[13] M. Villegas et al., “Predicting the Evolution of COVID-19 Mortality Risk: a Recurrent Neural Network Approach”, medRxiv, 2021.
Published
2022-04-15
How to Cite
GHOZI, Akhdan Aziz et al. Analisis Prediksi Data Kasus Covid-19 di Provinsi Lampung Menggunakan Recurrent Neural Network (RNN). Indonesian Journal of Applied Mathematics, [S.l.], v. 2, n. 1, p. 25-32, apr. 2022. ISSN 2774-2016. Available at: <https://journal.itera.ac.id/index.php/indojam/article/view/763>. Date accessed: 16 may 2022. doi: https://doi.org/10.35472/indojam.v2i1.763.