Comparison multi-layer perceptron and linear regression for time series prediction of novel coronavirus covid-19 data in West Java

Yulita, Intan Nurma; Abdullah, Atje Setiawan; Helen, Afrida; Hadi, Sinan Jasim; Sholahuddin, Asep; Rejito, Juli

doi:10.1088/1742-6596/1722/1/012021

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…According to the present data trend, West Java had zero instances on several occasions in May but reported an increase in July 2020 [8]. If the government intends to loosen limits, it must consider the possibility of future instances.…”

Section: Introductionmentioning

confidence: 72%

Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

Yulita,

Afrida Helen,

Mira Suryani

2023

j. nas. pendidik. teknik. inform.

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In 2019, the COVID-19 pandemic appeared. There have been several efforts to curb the spread of this virus. West Java, Indonesia, employs social restrictions to prevent the spread of this disease. However, this method destroyed the economy of the people. If no instances were detected in the region, the World Health Organization (WHO) authorized the social restrictions to be relaxed. If the government lifts the social limitation, the decision must also consider the potential of future confirmed instances. By utilizing machine learning, it is possible to forecast future data. This work utilized the following algorithms: linear regression (LR), locally weighted learning (LWL), multi-layer perceptron (MLP), radial basis function regression (RBF), and support vector machine (SVM). The study investigated daily new instances of COVID-19 in West Java, Indonesia, from March 2, 2020, to October 15, 2020. The RBF algorithm was the best in this investigation. Mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and relative absolute error (RAE) were 48.85, 89.73, 88.67, 62.99, and 60.88, respectively. The RBF prediction model may be proposed to the government of West Java for assessing data on COVID-19 instances, particularly in social restriction management. It is anticipated that West Java would have a minimum of 275 new cases every day for the following 30 days beginning on October 16, 2020. Consequently, the easing of societal limitations requires careful consideration.

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Section: Introductionmentioning

confidence: 72%

Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

Yulita,

Afrida Helen,

Mira Suryani

2023

j. nas. pendidik. teknik. inform.

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“…Research that specifically addresses the prediction of new cases of COVID-19 can be found in a number of machine-learning studies. Yulita et al studied it for a province in Indonesia with traditional machine learning [11]. The use of deep learning has also been applied to this prediction [12][13].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for New COVID-19 Cases Using Recurrent Neural Networks and Long-Short Term Memory

Yulita,

Manurung,

Suryana

2023

Iraqi Journal of Science

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This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being 0.66975075, 0.47075, 0.29616625, and 0.7644 on the test data.

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“…Hopefully, this chatbot can help residents obtain comprehensive, complete, and targeted vaccine information. So that residents have sufficient knowledge about vaccines and are confident to vaccinate (Yang et al, 2021;Yulita et al, 2021;Nuanmeesri et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

The Implementation of Deep Learning Techniques in Developing Conversational Chatbot as The Source of Vaccination Information

Yuliska

Najwa

Syaliman

2022

JAETS

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The Covid-19 pandemic has hit Indonesia for more than 2 years. To overcome Covid-19, Indonesian government implemented a vaccination program with a target of 70% of the population being vaccinated. However, the recorded population that has been vaccinated to reduce the risk of being exposed to Covid-19 is still low. Several studies have stated that information and invitations to vaccines through mass media are considered insufficient to convince the population to vaccinate. Residents who are still unsure and do not even want to vaccinate need really comprehensive information from experts. To answer this problem, a chatbot that can replace experts in explaining everything related to vaccines can be one solution. This is evidenced by a study which states that the interaction between people who have not been vaccinated with a chatbot that explains about vaccination can reduce the level of doubt of the population about the vaccine by up to 20%. The purpose of this research is to build a chatbot using deep learning technique. Meanwhile, the deep learning technique used to build a conversational chatbot is the Multilayer Perceptron Network (MLP). Based on the result of our study, our chatbot can answer 83% questions correctly out of 30 questions.

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Comparison multi-layer perceptron and linear regression for time series prediction of novel coronavirus covid-19 data in West Java

Cited by 12 publications

References 14 publications

Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

Machine Learning Approach for New COVID-19 Cases Using Recurrent Neural Networks and Long-Short Term Memory

The Implementation of Deep Learning Techniques in Developing Conversational Chatbot as The Source of Vaccination Information

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