Penerapan Teknik Data Mining dengan Metode Support Vector Machine (SVM) untuk Memprediksi Siswa yang Berpeluang Drop Out (Studi Kasus di SMKN 1 Sutera)

2017 ◽  
Vol 3 (1) ◽  
pp. 15
Author(s):  
Ryci Rahmatil Fiska
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Drop Out ◽  
Support Vector ◽  
Kernel Trick

Drop out adalah suatu keadaan di mana siswa diberhentikan dari sekolah karena beberapa alasan yang mengharuskan siswa untuk di drop out . faktor drop out seperti absen yang telah melebihi batas maksimal, nilai yang kurang dari batas bawah yang telah ditentukan pihak sekolah dan prilaku siswa yang sudah melanggar nilai attitude yang telah diterapkan oleh pihak sekolah. Dalam penelitian ini penulis ingin memprediksi siswa yang berpeluang drop out dengan menggunakan metode SVM dan diharapkan mampu memprediksi masalah drop out apakah siswa akan lanjut ke semester selanjutnya atau di drop out karena beberapa alasan. Dan hasil yang disimpulkan adalah metode SVM mampu memprediksi siswa yang berpeluang drop out.Kata kunci : Support Vector Machine, Kernel Trick, Drop Out.

scholarly journals Detection of Anxiety Expression From EEG Analysis Using Support Vector Machine

2019 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Kou Yamada ◽  
Wan Junaidee bin Wan Hamat ◽  
Harris Majdi bin Ishak ◽  
Kotaro Hashikura ◽  
Takaaki Suzuki
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Learning Communities ◽  
Ranking Function ◽  
Support Vector ◽  
Eeg Analysis ◽  
Nonlinear Functions ◽  
Kernel Trick ◽  
Efficient Learning

Support Vector Machine (SVMs) have been extensively researched in data mining and machine learning communities for the last decade and actively applied to application in various domains. SVMs are typically used for learning classification, regression and ranking function. Two specials properties of SVMs are that SVMs achieve high generalization by maximizing the margin and support an efficient learning of nonlinear functions by kernel trick. In this paper, we present how to clarify when we feel anxiety by using SVM technique to estimate the condition of user.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

2013 ◽  
Vol 295-298 ◽  
pp. 644-647 ◽  
Author(s):  
Yu Kai Yao ◽  
Hong Mei Cui ◽  
Ming Wei Len ◽  
Xiao Yun Chen
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Mining Algorithm ◽  
Grid Search ◽  
Mining Algorithm ◽  
Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

scholarly journals Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining

2019 ◽  
Vol 1255 ◽  
pp. 012067
Author(s):  
Natalina Br Sitepu ◽  
Sawaluddin ◽  
M Zarlis ◽  
Syahril Efendi ◽  
Hanna Willa Dhany
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Support Vector ◽  
Smooth Support Vector Machine

scholarly journals An Improved Hybrid Model for Early Prediction of Autism

2020 ◽  
Vol 8 (5) ◽  
pp. 4358-4361
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Intellectual Disabilities ◽  
Hybrid Model ◽  
Support Vector ◽  
Early Prediction ◽  
Creative Mind ◽  
Social Collaboration

Autism is described by extreme, unavoidable intellectual disabilities which are adverse on perspectives related with social collaboration, correspondence, creative mind and conduct. Treating Autism has secured an exceptional spot, as a few heuristic and measurable models are proposed by scientists working around there. Henceforth kids influenced with such issue should be upheld with recognition of an early, well-planned and singular scholarly endeavours created in adjusted settings bringing about early location and accurately diagnose the issues of Autism. Requirements of Data mining and soft computational methodologies are thought as a characteristic qualified for finding confounded examples. The paper defines a definite investigation and proposes the hybrid improved methodologies of Bee Hive Optimization with Support Vector Machine for the requirement of versatile and early prediction of Autism among developing youngsters with more Accuracy and with the less error and time.

scholarly journals Integration of synthetic minority oversampling technique for imbalanced class

2019 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Noviyanti Santoso ◽  
Wahyu Wibowo ◽  
Hilda Hikmawati
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Class Imbalance ◽  
Original Data ◽  
Support Vector ◽  
Classification Methods ◽  
Problematic Issue ◽  
Imbalanced Class ◽  
F Measure

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.

scholarly journals Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Seyed Reza Kamel ◽  
Reyhaneh YaghoubZadeh ◽  
Maryam Kheirabadi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Support Vector Machine ◽  
Feature Selection Method ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Gray Wolf ◽  
Diagnose Breast Cancer ◽  
Computer Methods ◽  
A New Technique

Abstract One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods such as computer science is extremely important for early detection of the condition. Today, the main emphasis is on the science of data mining as one of the computer methods in the field of diagnosis. In the present study, we used data mining as a combination of feature selection method by Gray Wolf Optimization (GWO) and support vector machine (SVM), which is a new technique with high accuracy compared to other methods in this classification, to increase the accuracy of breast cancer diagnosis. The UCI dataset and functional parameters and various statistical criteria were applied to evaluate the proposed method and assess the validity of the results in MATLAB, respectively. Application of the proposed method increased the improvement of the evaluated criteria, which increased the accuracy of diagnosis by 27.68%, compared to former works in the field. As such, it could be concluded that the proposed method had a higher ability to diagnose breast cancer, compared to previous techniques.

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