Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results

• Published in: JISA (Jurnal Informatika dan Sains) Vol. 4; no. 1; pp. 22 - 27
• Main Authors: Saikin, Saikin, Fadli, Sofiansyah, Ashari, Maulana
• Format: Journal Article
• Language: English
• Published: Program Studi Teknik Informatika Universitas Trilogi 20-06-2021
• Subjects: classification; k-fold algorithm; machine learning; svm method
• ISSN: 2776-3234; 2614-8404
• DOI: 10.31326/jisa.v4i1.881

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.