Accuracy of Malaysia Public Response to Economic Factors During the Covid-19 Pandemic Using Vader and Random Forest

Jumanto, Jumanto; Muslim, Much Aziz; Dasril, Yosza; Mustaqim, Tanzilal

doi:10.52465/joiser.v1i1.104

Cited by 15 publications

(15 citation statements)

References 34 publications

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“…The dataset obtained will consist of 8 classes, namely Valid, Satire/Parody, Misleading Content, Fabricated Content, False Context, Manipulated Content, Imposter Content, and False Connection. The data that has been obtained is then cleaned in several steps, starting from lowercasing, cleaning non-ascii characters, removing links, removing account names and hashtags, removing foreign language articles, normalizing non-standard words, removing stopwords to reduce noise on data [23], removing duplicate articles, and removing rows with empty narration due to the data cleaning process. After the data is clean, then the dataset is exported in (.csv) format.…”

Section: Data Collection and Cleaningmentioning

confidence: 99%

Hoax Classification in Indonesian Language with Bidirectional Temporal Convolutional Network Architecture

Maulana

Asih

2023

J. Soft Comput. Explor.

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The increasingly massive rate of information dissemination in cyberspace has had several negative impacts, one of which is the increased vulnerability to the spread of hoaxes. Hoax has seven classifications. Classification problems such as hoax classification can be automated using the application of the Deep Learning model. Bidirectional Temporal Convolutional Network (Bi-TCN) is a type of Deep Learning architectural model that is very suitable for text classification cases. Because the architecture uses dilation factors in its feature extraction so it can generate exceptionally large receptive fields and is supported by Bidirectional aggregation to ensure that the model can learn long-term dependencies without storing duplicate context information. The purpose of this study is to evaluate the performance of Bi-TCN architecture combined with pre-trained FastText embedding model for hoax classification in Indonesian and implement the resulting model on website. Based on the research that has been done, the model with Bi-TCN architecture has satisfactory performance with an accuracy score of 92.98% and a loss value that can be reduced to 0.191. Out of a total of 13,673 data tested with this model, only 414 data or in other words around 3% of the total data were incorrect predictions.

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Section: Data Collection and Cleaningmentioning

confidence: 99%

Hoax Classification in Indonesian Language with Bidirectional Temporal Convolutional Network Architecture

Maulana

Asih

2023

J. Soft Comput. Explor.

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“…Unlike traditional statistical methods, in which errors may be more predictable and understandable, deep learning models can sometimes produce unexpected and inexplicable errors. Such errors can have life-threatening consequences [ 31 , 53 , 54 ]. To mitigate these risks, robust procedures should be performed to test and validate the models, monitor their performance in clinical settings, and update them as required to ensure that they continue to perform reliably [ 55 , 56 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

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“…Its surface, which is covered in spikes that resemble crowns, is referred to as the corona. The Covid-19 pandemic has reached all regions of the world [3]. SARS (severe acute respiratory syndrome), MERS (Middle East Respiratory Syndrome), and other illnesses are examples of this illness that affects people.…”

Section: Introductionmentioning

confidence: 99%

Developed an expert system for analysis of Covid-19 affected

Mishra

Yadav

Aggarwal

et al. 2023

J. Soft Comput. Explor.

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The expert system solves problems within a specific area of the knowledge base. Prolog is a logical programming language which works on its knowledge base and effectively can be used to develop an expert system. Covid 19 is a pandemic deices and an expert system can be developed to diagnose this disease with the help of its symptoms that can be used as a knowledge base in Prolog. This expert system can make a fast diagnosis process for the covid 19 which is important to prevent the spread of the virus. Here we developed an expert system using prolog for diagnosis purposes. Like humans, these systems can get better with time as they gain more experience. Expert systems combine their experiences and expertise into a knowledge base that is then used by an inference or rules engine, a set of rules that the software employs, to apply to certain scenarios. Prolog is ideal for use with intelligent systems for a few reasons. Prolog can be viewed as a straightforward theorem prover or inference engine that derives from predefined rules. With the help of Prolog's built-in search and backtracking techniques, simple expert systems can be created.

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Accuracy of Malaysia Public Response to Economic Factors During the Covid-19 Pandemic Using Vader and Random Forest

Cited by 15 publications

References 34 publications

Hoax Classification in Indonesian Language with Bidirectional Temporal Convolutional Network Architecture

Hoax Classification in Indonesian Language with Bidirectional Temporal Convolutional Network Architecture

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Developed an expert system for analysis of Covid-19 affected

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