Improving Support Vector Machine Rainfall Classification Accuracy based on Kernel Parameters Optimization for Statistical Downscaling Approach

Sulaiman, Nurul AininaFilza

doi:10.30534/ijatcse/2020/9191.42020

Cited by 6 publications

(11 citation statements)

References 11 publications

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“…Because the observation data is x, then the multinomial logistic regression produces a class label 𝑦 as in equation ( 3): where 𝑥 𝑖 is the input vector, and 𝑦 𝑖 is the output. Then the formula for the binary classification problem is [5]:…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

“…Rainfall classification has been carried out by several researchers [4], [5], [6], and [7] using individual machine learning methods. In research [4] applying the C5.0 algorithm using k-fold cross-validation and obtaining the highest accuracy of 92% on imbalanced data, while applying the smote technique the accuracy increased by 99%.…”

Section: Introductionmentioning

confidence: 99%

“…In research [4] applying the C5.0 algorithm using k-fold cross-validation and obtaining the highest accuracy of 92% on imbalanced data, while applying the smote technique the accuracy increased by 99%. Research [5] uses the PCA algorithm Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol. 6 No.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Gumilar

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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This study proposes several methods to analyze the performance of the hybrid machine learning method using Voting and Stacking on rainfall classification. The two hybrid methods will combine five classification methods, namely Logistic Regression, Support Vector Machine, Random Forest, Artificial Neural Network, and eXtreme Gradient Boosting. The data used is Bandung City rainfall data for the years 2005 until 2021. The hybrid method is classified as an ensemble, which means combining several individual classification models to improve the performance of the built model. Voting algorithm has weaknesses in imbalanced data, while stacking does not. The results show that by combining five machine learning methods on an imbalanced dataset, the Stacking algorithm obtains an accuracy value of 99.60%. Meanwhile, with the addition of the SMOTE technique, the accuracy increases to 99.71%. This is supported by the performance of the Stacking method which is superior because it takes the best classification value for each individual model and can overcome the imbalance. Model evaluation does not only focus on accuracy, but also precision, recall, and f1-score. The contribution of this research is to provide information about the best Hybrid method between Voting and Stacking in obtaining model performance results on rainfall classification.

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Section: Logistic Regression (Lr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Gumilar

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…The use of these models is to predict the chosen factors by observing their patterns, discovering the trends and later, the outcome will be drawn. Some of the well-known predictive models are decision tree, singular spectrum analysis, support vector machine, and linear regression [9,10,11]. These models have a different style of an algorithm to produce an outcome.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics on Academic Performance in Higher Education Institution during COVID-19 using Regression Model

Zulfikri

Shaharudin

Rajak

et al. 2021

International Journal of Biology and Biomedical Engineering

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The coronavirus disease 2019 (COVID-19) outbreak in December 2019 had affected the way of living for people around the world including students in educational institutions. These students had to prepare for the continuity of their study mentally and physically by adapting to the online teaching and learning approach, which can significantly impact their academic performance. Hence, this study examines the students' academic performance on the online teaching and learning approach, thus predicting their academic performance for the upcoming semester. This study enables various actions to be taken in improving and maintaining the student's performance in their study activities. The study was conducted on undergraduate students from the Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris. This paper presents the prediction of the student's academic performance with a linear regression model. Evidently, the result shows that the student's academic performance continually improves while adapting to the online teaching and learning approach. It also shows that there were few respondents affected while adapting to this new norm approach. Hence, the future development of the regression model can be improved by having a more comprehensive range of Malaysian universities' data.

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“…Besides, [19] proposed classification of students based on quality of life and academic performing using SVM and [20] use the SVM as machine learning too due to its advantage to improve the accuracy of classification procedure especially in data mining. Moreover, the result from [21] shows that SVM is one the model that capable of predicting with scoring high accuracy not less than 92%. This study mainly focused on finding the pattern of students' academic performance before during online learning due to the COVID-19 pandemic outbreak by referring on their Grade Point Average (GPA).…”

Section: Introductionmentioning

confidence: 99%

Modeling Student’s Academic Performance During Covid-19 Based on Classification in Support Vector Machine

Samsudin¹

2021

TURCOMAT

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This study proposed a statistical investigate the pattern of students’ academic performance before and after online learning due to the Movement Control Order (MCO) during pandemic outbreak and a modelling students’ academic performance based on classification in Support Vector Machine (SVM). Data sample were taken from undergraduate students of Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI). Student’s Grade Point Average (GPA) were obtained to developed model of academic performances during Covid-19 outbreak. The prediction model was used to predict the academic performances of university students when online classes was conducted. The algorithm of Support Vector Machine (SVM) was used to develop a model of students’ academic performance in university. For the Support Vector Machine (SVM) algorithm, there are two important parameters which are C (misclassification tolerance parameter) and epsilon need to identify before proceed the further analysis. The parameters was applied to four different types of kernel which is linear kernel, radial basis function kernel, polynomial kernel and sigmoid kernel and the result was found that the best accuracy achieved by SVM are 73.68% by using linear kernel and the worst accuracy obtained from a sigmoid kernel which is 67.99% with parameter of misclassification tolerance C is 128 and epsilon is 0.6.

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Improving Support Vector Machine Rainfall Classification Accuracy based on Kernel Parameters Optimization for Statistical Downscaling Approach

Cited by 6 publications

References 11 publications

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Predictive Analytics on Academic Performance in Higher Education Institution during COVID-19 using Regression Model

Modeling Student’s Academic Performance During Covid-19 Based on Classification in Support Vector Machine

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