Analisis Support Vector Machine Untuk Pemberian Rekomendasi Penundaan Biaya Kuliah Mahasiswa

Widayani, Wiwi; Harliana, Harliana

doi:10.34128/jsi.v7i1.268

Cited by 4 publications

(5 citation statements)

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“…For the parameter of K-Nearest Neighbor, this research uses the same parameters used in research [8]. While the kernel of Support Vector Machine using a kernel that produces a high level of accuracy, according to the kernel used in research [19].…”

Section: Feature Selection Resultsmentioning

confidence: 99%

Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method

Katili

Soeleman

Pramunendar

2023

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

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Indonesia is a country rich in a variety of regional cultures. Regional airspace needs to be preserved so as not to become extinct. One of them is the local culture of Central Java Province, namely Javanese Character. In this modern era, globalization is growing in every country. The impact of globalization is increasingly widespread and developing in society. One effect of globalization is local people prefer foreign language skills to learn local languages. This study, applies the method of character recognition using a new combination workflow that contains Local Binary Pattern (LBP) and Information Gain. Then compare Support Vector Machine (SVM), k-Nearest Neighbor and Naïve Bayes. The LBP method is used to obtain an image's texture or shape characteristics. Information Gain is used for the feature selection algorithm, whereas SVM, k-Nearest Neighbor and Naïve ayes is used for the classification method. From previous research, the information gain method succeeded in increasing the accuracy by 2%. This research compares the SVM classification with another classification method, and the result shows that our proposed can improve classification performance. The best accuracy result using SVM classification gets 87,86%, at ten folds and cell size 64x64.

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Section: Feature Selection Resultsmentioning

confidence: 99%

Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method

Katili

Soeleman

Pramunendar

2023

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

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“…In this study, the polynomial kernel obtained the highest accuracy compared to linear and RBF kernel, 85.56% at γ = 2, C = 2, and d =1. Meanwhile, research conducted by [31,37,43] states that the use of the RBF kernel has higher accuracy than other kernels. The kernel function influences this evidence in SVM; in other words, the effectiveness of SVM depends on the selection of kernel functions and parameters of each kernel.…”

Section: Discussionmentioning

confidence: 99%

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

2022

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Sex classification is part of forensic anthropological identification aimed at determining whether the skeleton belongs to a male or a female. This paper exhibits the performance of the Support Vector Machine (SVM) in classifying the sex of the sacrum in forensic anthropology. Bone data was measured by the metric method based on six variables, namely superior breadth, anterior length, mid ventral breadth, real height, diameter the base, and max-transverse diameter of the base. This study shows performance analysis of SVM using the library libSVM with linear, polynomial, and RBF kernel to observe the results of the comparison of the accuracy of the kernel used. According to the results of the trials, the best accuracy was attained in each kernel function, i.e., the RBF kernel is 83.33% with g = 1 and C = 1, the polynomial is 85.56% at γ = 2, C = 2 and d =1, and the linear kernel obtained best accuracy is 84.44 % with C = 2 and C = 3. In conformity with the experimental result, polynomial attained the highest accuracy of 85.56% at γ = 2, C = 2, and d =1.

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“…Research related to CFS [10]- [13] Which basically explains The dataset used was feature selected using the correlation-based method where the correlation test was carried out by calculating and comparing the level of correlation between features and their classes as well as features with other features. Correlation-based feature selection (CFS) is a feature selection method that uses the correlation between features and target classes to determine which features should be retained in a model.…”

Section: Methodsmentioning

confidence: 99%

“…Research related to C5.0 [8], [9], [14], [15] and related to Support Vektor Machine [13], [16], [17] that use algorithms for classification after the data has been selected and divided, the data is then processed using the C5.0 algorithm and Support Vector Machine accompanied by feature selection. For research on support vector machines using the RBF Kernel and Sigmoid Kernel, where the related research is [18]- [21], The kernel is the choice used in many cases because of its ability to handle complex non-linear fragmentation.…”

Section: Methodsmentioning

confidence: 99%

Implementation Of C5.0 Classification And Support Vector Machine Algorithm With Correlation-Based Feature Selection In Application Liver Disease

Rachman,

Ramdani,

Insani

2024

JAISI

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Liver disease is a general term that refers to a number of disorders or problems that affect the liver. The liver is an important organ in the human body and has many diverse functions, including food processing, protein production, toxin removal, and energy storage. Therefore, when the liver experiences disorders or disease, it can have a serious impact on the overall health and function of the body. Liver disease is a significant global health problem. Early detection as well as classification of liver disease can provide valuable guidance for effective treatment. Based on the problems above, the aim of this research is to create a liver disease classification model using C5.0 and Support Vector Machine with Radial Basis Function (RBF) and Sigmoid kernels. With data obtained from the liver disease dataset. The two methods will be compared and we will find out which one produces the best results. The method used is also optimized with CFS (Correlation Based Feature Selection) feature selection. The results of the classification process, namely the C5.0 Model and Support Vector Machine (RBF) with CFS have a similar accuracy of 76%, while the Support Vector Machine (Sigmoid) has an accuracy of 70%, without feature selection the C5.0 algorithm has an accuracy of 66% , Support Vector Machine between RBF and sigmoid kernels has an accuracy of 69% and 55%.

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Analisis Support Vector Machine Untuk Pemberian Rekomendasi Penundaan Biaya Kuliah Mahasiswa

Cited by 4 publications

References 0 publications

Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method

Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

Implementation Of C5.0 Classification And Support Vector Machine Algorithm With Correlation-Based Feature Selection In Application Liver Disease

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