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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Hasmawati, H
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Covid-19 Prediction Using Lightgbm and LSTM Dharayani, Ramanti; Hasmawati, H; Khotijah, Siti
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.495

Abstract

Covid-19 has become a global health problem during this pandemic. Every country is struggling to fight this problem as well as Indonesia. Indonesia has a high number of new cases and this has an impact on the high demand for bed occupancy rates. To overcome this situation, we recommend the prediction of covid-19 using LGBM and LSTM. We implement two pre-processing, namely one-hot data encode and Normalization. The results of the pre-processing will be used as input for the prediction of new cases of COVID-19 using the LGBM and LSTM algorithms. The experimental results show that LSTM has better results than LGBM. We evaluated that the number of epochs we used in the LSTM had a large influence on the RMSE, MAE, and R2 measurements
Covid-19 Prediction Using Lightgbm and LSTM Dharayani, Ramanti; Hasmawati, H; Khotijah, Siti
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.495

Abstract

Covid-19 has become a global health problem during this pandemic. Every country is struggling to fight this problem as well as Indonesia. Indonesia has a high number of new cases and this has an impact on the high demand for bed occupancy rates. To overcome this situation, we recommend the prediction of covid-19 using LGBM and LSTM. We implement two pre-processing, namely one-hot data encode and Normalization. The results of the pre-processing will be used as input for the prediction of new cases of COVID-19 using the LGBM and LSTM algorithms. The experimental results show that LSTM has better results than LGBM. We evaluated that the number of epochs we used in the LSTM had a large influence on the RMSE, MAE, and R2 measurements
Co-Authors Ramanti Dharayani RR. Ella Evrita Hestiandari