Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia

Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Kalbuana, Nawang; Meiriki, Andri; Sutarno, .

doi:10.1088/1742-6596/1933/1/012050

Cited by 20 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The higher the resulting RMSE value, the lower the level of accuracy, and the lower the resulting RMSE value, the higher the level of accuracy [5]. The second measure is the Mean Absolute Percentage Error (MAPE) calculation, to determine LSTM performance evaluation in forecasting (forecasting), which can be seen in the following equation (2).…”

Section: G Evaluation Of Test Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Mukhtar¹,

Taufiq²,

Herwinanda³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Confirmed statistical data of Covid-19 cases that have accumulated sourced from (https://corona.riau.go.id/datastatistik/) in Riau Province on June 7, 2021, there were 63441 cases, on June 14, 2021, it increased to 65883 cases, on June 21, 2021, it increased to 67910, and on June 28, 2021, it increased to 69830 cases. Since the beginning of this pandemic outbreak, it has been observed that the case data continues to increase every week until this July. This study predicts cases of Covid-19 time series data in Riau Province using the LSTM algorithm, with a dataset of 64 lines. Long-Short Term Memory has the ability to store memory information for patterns in the data for a long time at the same time. Tests predicting historical data for Covid-19 cases in Riau Province resulted in the lowest RMSE value in the training data, which was 8.87, and the test data, which was 13.00, in the death column. The evaluation of the best MAPE value in the training data, which is 0.23%, is in the recovered column, and the evaluation of the best MAPE value in the test data, which is 0.27%, in the positive_number column. In the test to predict the next 30 days using the LSTM model that has been trained, it was found that the performance evaluation of the prediction results for the positive_number column and the death column was very good, the recovery column was categorized as good, the independent_isolation column and the care_rs column were categorized as poor.

show abstract

Section: G Evaluation Of Test Resultsmentioning

confidence: 99%

“…Time series data is a series of observed data based on a certain time interval. The time-series approach explains that the model is influenced by data that occurred in the past [5]. This study makes predictions using the Long Short-Term Memory (LSTM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Mukhtar¹,

Taufiq²,

Herwinanda³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…LSTM plays a significant role in predicting pandemic diseases. In [16], authors introduced studies of how to predict data of COVID-19. The prediction of data is based on LSTM method and GRU by using python.…”

Section: And DL Are Playing a Vital Role In The Early Diagnosis As It...mentioning

confidence: 99%

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Aly¹,

Hussein²,

Rahouma³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

show abstract

“…The appointment of Nadiem Makarim as Minister of Cultural Education in October 2019, which initially reaped many pros and cons in the world of Education, began to be trusted because of this pandemic (Bhwana, 2019;Kadi, Wati, & Sari, 2020). In the world of education that was initially conducted offline in March 2020 (Hastomo, Karno, Kalbuana, Meiriki, & Sutarno, 2021) was changed into an online form; this is supported by the issue of several policies from the Ministry of Education and Culture in accordance with the Circular Letter (Covid-19) allowing educational institutions to impose online learning as per Education Ministry Circular Letter No. 2 of 2020 and No.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Zoom Application as An Online Learning Media During the Covid-19 Pandemic at Politeknik Penerbangan Indonesia Curug

Kurniawati¹,

Kurnianto²,

Abdusshomad³

et al. 2023

jiip

View full text Add to dashboard Cite

The spread of covid-19 in Indonesia requires all Cadets on campus under the Ministry of Transportation in Indonesia to be conducted online. Politeknik Penerbangan Indonesia Curug is one of the schools under the Ministry of Transportation that applies in the Covid-19 epidemic to online learning through the Zoom application. Zoom.us provides a version of Zoom Education as a form of CSR provided to schools affected by the Covid-19 Pandemic. Online learning's effectiveness is very important, so cadets understand the lessons during the Teaching and Learning Activities (KBM). This study aims to understand how effective online learning is conducted at Politeknik Penerbangan Indonesia Curug using the Zoom application. This research uses a qualitative research method and data collection techniques by conducting interview techniques. This research resulted in online learning conducted with a zoom application at Politeknik Penerbangan Indonesia Curug. Online learning got an excellent response from cadets because it offered greater flexibility in their studies. Online learning makes cadets more independent but also encourages cadets more active when learning. In addition, acceptance and the understanding delivered by lecturers can be felt by the Cadets at Politeknik Penerbangan Indonesia Curug. But this online learning system also has weaknesses: limited cadets quota, which causes not all cadets to go online by activating the video so that lecturers cannot see all activities carried out by cadets.

show abstract

Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia

Cited by 20 publications

References 8 publications

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Utilization of Zoom Application as An Online Learning Media During the Covid-19 Pandemic at Politeknik Penerbangan Indonesia Curug

Contact Info

Product

Resources

About