The Comparison of SVM and ANN Classifier for COVID-19 Prediction

Avianty, Ditha Nurcahya; Wijaya, Prof. I Gede Pasek Suta; Bimantoro, Fitri

doi:10.24843/lkjiti.2022.v13.i02.p06

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…SVM is a classification technique for predicting classes using machine-learning training [18]. The training process is performed with input data known to be labeled to create the model.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Comparing Support Vector Machine and Naïve Bayes Methods with A Selection of Fast Correlation Based Filter Features in Detecting Parkinson's Disease

Farida,

Ulinnuha,

Sari

et al. 2023

LKJITI

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Dopamine levels fall due to brain nerve cell destruction, producing Parkinson's symptoms. Humans with this illness experience central nervous system damage, which lowers the quality of life. This disease is not deadly, but when people's quality of life decreases, they cannot perform daily activities as people do. Even in one case, this disease can cause death indirectly. Contrast support vector machines (SVM) and naive Bayesian approaches with and without fast correlation-based filter (FCBF) feature selection, this study attempts to determine the optimum model to detect Parkinson's disease categorization. In this study, datasets from the UCI Machine Learning Repository are used. The results showed that SVM with FCBF achieved the highest accuracy among all the models tested. SVM with FCBF provides an accuracy of 86.1538%, sensitivity of 93.8775%, and specificity of 62.5000%. Both methods, SVM and Naive Bayes, have improved in performance due to FCBF, with SVM showing a more significant increase in accuracy. This research contributed to helping paramedics determine if a patient has Parkinson's disease or not using characteristics obtained from data, such as movement, sound, or other pertinent factors.

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“…SVM is a classification technique for predicting classes using machine-learning training [18]. The training process is performed with input data known to be labeled to create the model.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Comparing Support Vector Machine and Naïve Bayes Methods with A Selection of Fast Correlation Based Filter Features in Detecting Parkinson's Disease

Farida,

Ulinnuha,

Sari

et al. 2023

LKJITI

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show abstract

“…Support vector machine (SVM) is one data mining method with a high accuracy level in classifying data. The previous Comparison of SVM and ANN Classifiers for the COVID-19 Prediction study proves that SVM results have slightly better accuracy than ANN [8]. Judging from the high accuracy, SVM research compared to KNN research shows that the SVM algorithm exhibits higher accuracy when tested with normalization, outperforming the KNN algorithm in normalized and non-normalized conditions.…”

Section: Introductionmentioning

confidence: 95%

Comparison of Gain Ratio and Chi-Square Feature Selection Methods in Improving SVM Performance on IDS

Diaz,

Putra,

Sudarma

et al. 2024

LKJITI

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An intrusion detection system (IDS) is a security technology designed to identify and monitor suspicious activity in a computer network or system and detect potential attacks or security breaches. The importance of accuracy in IDS must be addressed, given that the response to any alert or activity generated by the system must be precise and measurable. However, achieving high accuracy in IDS requires a process that takes work. The complex network environment and the diversity of attacks led to significant challenges in developing IDS. The application of algorithms and optimization techniques needs to be considered to improve the accuracy of IDS. Support vector machine (SVM) is one data mining method with a high accuracy level in classifying network data packet patterns. A feature selection stage is needed for an optimal classification process, which can also be applied to SVM. Feature selection is an essential step in the data preprocessing phase; optimization of data input can improve the performance of the SVM algorithm, so this study compares the performance between feature selection algorithms, namely Information Gain Ratio and Chi-Square, and then classifies IDS data using the SVM algorithm. This outcome implies the importance of selecting the right features to develop an effective IDS.

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A Comparison of Prediction Algorithms in Food Sales with Different K-Folds Cross-Validation

Roshinta,

Dinata,

Riatma

et al. 2023

2023 6th International Conference of Computer and Informatics Engineering (IC2IE)

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The Comparison of SVM and ANN Classifier for COVID-19 Prediction

Cited by 3 publications

References 14 publications

Comparing Support Vector Machine and Naïve Bayes Methods with A Selection of Fast Correlation Based Filter Features in Detecting Parkinson's Disease

Comparing Support Vector Machine and Naïve Bayes Methods with A Selection of Fast Correlation Based Filter Features in Detecting Parkinson's Disease

Comparison of Gain Ratio and Chi-Square Feature Selection Methods in Improving SVM Performance on IDS

A Comparison of Prediction Algorithms in Food Sales with Different K-Folds Cross-Validation

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