Using Regression to Predict Number of Tourism in Indonesia based of Global COVID-19 Cases

Brilliandy, Eduardo; Lucky, Henry; Hartanto, Adrian; Suhartono, Derwin; Nurzaki, Muhammad

doi:10.1109/aidas56890.2022.9918731

Cited by 3 publications

(3 citation statements)

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“…In this step, we predict the right rule on certain data based on an existing features and rules. This prediction process compare several algorithms which is possible to predict the rule , such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN) [14], Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) [15]. In this process, the rule set used as a feature.…”

Section: Rule Predictionmentioning

confidence: 99%

“…As we mentioned above, rule prediction process compare several algorithms, such as Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting Regression (XGBoost), Linear Regression, Support Vector Regression (SVR), and K-Nearest Neighbors Regression (KNN) [14] [15], with the consideration of using methods that can be used for regression tasks, and methods that can generate rules with a tree-based approach. Rule sets generated through constituent tree mapping are used as features in this process which include constituency labels, label positions, constituency sub-tree depth (height), and leaf node length.…”

Section: Rule Prediction Evaluationmentioning

confidence: 99%

“…These rule sets then used as learning data by the prediction model, in order to predict the right rule on certain data. We use several prediction algorithms such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN) [14], Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) [15], with the consideration of using methods that can be used for regression tasks, and methods that can generate rules with a tree-based approach.…”

Section: Introductionmentioning

confidence: 99%

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SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

Sopian,

Ilyas,

Kasyidi

et al. 2024

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Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets.

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Section: Rule Predictionmentioning

confidence: 99%

Section: Rule Prediction Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

Sopian,

Ilyas,

Kasyidi

et al. 2024

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An Electricity Consumption Monitoring and Prediction System Based on The Internet of Things

Angdresey

Sitanayah

Rumpesak

2022

2022 Seventh International Conference on Informatics and Computing (ICIC)

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An Efficient Crop Yield Prediction System Using Machine Learning

Swain,

Lakum,

Patel

et al. 2024

EAI Endorsed Trans IoT

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Farming is considered the biggest factor in strengthening the economy of any country. It also has significant effects on GDP growth. However, due to a lack of information and consultation, farmers suffer from significant crop losses every year. Typically, farmers consult agricultural officers for detecting crop diseases. However, the accuracy of predictions made by agricultural officers based on their experience is not always reliable. If the exact issues are not identified at right time then it results in a heavy crop loss. To address this issue, Computational Intelligence, also known as Machine Learning, can be applied based on historical data. In this study, an intelligent crop yield prediction algorithm is developed using various types of regression-based algorithms. The Crop Yield Prediction Dataset from the Kaggle repository is used for model training and evaluation. Among all different regression methods Random Forest has shown the better performance in terms of R2 score and other errors.

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Using Regression to Predict Number of Tourism in Indonesia based of Global COVID-19 Cases

Cited by 3 publications

References 0 publications

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

An Electricity Consumption Monitoring and Prediction System Based on The Internet of Things

An Efficient Crop Yield Prediction System Using Machine Learning

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