The Prediction of COVID-19 Pandemic Situation in Indonesia Using SVR and SIR Algorithm

Yaqin, Ainul; Rahardi, Majid; Abdulloh, Ferian Fauzi; Kusnawi, Kusnawi; Budiprayitno, Slamet; Fatonah, Siti

doi:10.1109/icitisee57756.2022.10057813

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…The proposed approach demonstrates high efficiency and precision, making it a valuable tool to assist radiologists and healthcare professionals in identifying COVID-19 positivity through lung X-rays. Yaqin et al [2022] study started from a situation that began to emerge in Indonesia towards the end of February 2020, leading to a notable increase in new COVID-19 patients. Despite researchers conducting various models and projections for COVID-19 cases in Indonesia, the results remain somewhat unreliable due to the fluctuating patterns observed.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Models for Predicting Corticosteroid Therapy Necessity in COVID-19 Patients: A Comparative Study

Mujiba Shaima,

Norun Nabi,

Md Nasir Uddin Rana

et al. 2024

JCSTS

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This study analyzes machine learning algorithms to predict the need for corticosteroid (CS) therapy in COVID-19 patients based on initial assessments. Using data from 1861 COVID-19 patients, parameters like blood tests and pulmonary function tests were examined. Decision Tree and XGBoost emerged as top performers, achieving accuracy rates of 80.68% and 83.44% respectively. Multilayer Perceptron and AdaBoost also showed competitive performance. These findings highlight the potential of AI in guiding CS therapy decisions, with Decision Tree and XGBoost standing out as effective tools for patient identification. This research offers valuable insights for personalized medicine in infectious disease management.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Models for Predicting Corticosteroid Therapy Necessity in COVID-19 Patients: A Comparative Study

Mujiba Shaima,

Norun Nabi,

Md Nasir Uddin Rana

et al. 2024

JCSTS

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“…Much like XGBoost, the Support Vector Regression (SVR) algorithm requires parameter adjustments in this research to enhance model performance. The study sets the parameters, adjusting the C value to 1.0, epsilon to 0.1, and utilizing a kernel function (Yaqin et al, 2022;Yigit, Gunel, & Gunel, 2018;Zhan, Zhang, & Liu, 2021;H. Zhu & Hu, 2019).…”

Section: B Support Vector Regression (Svr) Algorithmmentioning

confidence: 99%

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

Hutagalung,

Rosalind,

Tuhu

et al. 2023

Brilliance

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This research aims to optimize wholesale inventory management at PT Primafood International Pasir Putih 2 by implementing leading algorithms, namely XGBoost, Support Vector Regression (SVR), and Random Forest. In the wholesale industry, effective inventory management plays a crucial role in maintaining smooth production processes and enhancing company profitability. Despite the acknowledged benefits of inventory management, there are aspects that remain not fully disclosed, particularly concerning demand uncertainty and market fluctuations. This study addresses these gaps by exploring the potential of these three algorithms. Experimental methods with a quantitative approach were employed to shape and prepare the dataset. The analysis and predictions' results using XGBoost, SVR, and Random Forest were evaluated using metrics such as Mean Squared Error (MSE), F1-Score, and Accuracy. The evaluation indicates that XGBoost and SVR exhibit optimal performance with low MSE values of 7714.446 and 119.315, high F1-Scores (0.92), and good accuracy levels (0.86 and 0.85), respectively. While Random Forest shows a higher MSE, it still delivers solid performance with an F1-Score of 0.89 and an accuracy rate of 0.81. These findings suggest that all three algorithms can be considered to enhance inventory management performance at PT Primafood International Pasir Putih 2, with significant potential benefits for overall industry development. This research provides valuable insights for decision-making at the business and industrial levels, highlighting the effectiveness of each algorithm in the context of predicting stock level.

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YouTube Sentiment Analysis on User Opinions Related to Metaverse Using RNN and LSTM

Abdulloh,

Rayhan,

Aminuddin

et al. 2023

2023 7th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)

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The Prediction of COVID-19 Pandemic Situation in Indonesia Using SVR and SIR Algorithm

Cited by 4 publications

References 15 publications

Machine Learning Models for Predicting Corticosteroid Therapy Necessity in COVID-19 Patients: A Comparative Study

Machine Learning Models for Predicting Corticosteroid Therapy Necessity in COVID-19 Patients: A Comparative Study

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

YouTube Sentiment Analysis on User Opinions Related to Metaverse Using RNN and LSTM

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