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All Journal Pixel : Jurnal Ilmiah Komputer Grafis JURTEKSI Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Manajemen Informatika dan Sistem Informasi Jambura Journal of Electrical and Electronics Engineering JATI (Jurnal Mahasiswa Teknik Informatika) JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal IPTEK Bagi Masyarakat Jurnal ABDIMAS Budi Darma Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Ilmu Komputer untuk Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) SainsTech Innovation Journal Internet of Things and Artificial Intelligence Journal Journal of Artificial Intelligence and Digital Business ETIK (Jurnal Elektronika Terapan dan Ilmu Komputer) Jurnal Pengabdian Bersama Masyarakat Indonesia Pekayunan
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Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results Saikin, Saikin; Fadli, Sofiansyah; Ashari, Maulana
JISA(Jurnal Informatika dan Sains) Vol 4, No 1 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i1.881

Abstract

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.
Co-Authors Ahmad S. Pardiansyah Akbar, Jihadul Amrina Rosada, Amrina Apriati, Yati Ayu Lestari Cahyanti, Riska Indah Didik Sudyana Didik Sudyana Fadli, Sofiansyah Fahmi , Hairul Fahmi, Hairul Fauzi Zulkarnain, Mohammad Febrian, Melyana Firda Widia Gilang Restu Imam Wahyudi Haekal Fawaid Hairul Fahmi Handayani, Yudiana Tutik Hasyim Asyari Husnul Khotimah Ichwan Purwata ilahi, faidlul Indah Cahyanti, Riska Kartini, Ajeng Mastuti Kusrini Kusrini Lalu Mutawalli Lesteri, Widia Maemun Saleh Maulana Ashari Mohammad Taufan Asri Zaen Muliana, Muliana Musofa, Aolia Pirmansyah Pirmansyah Qubro, Baiq Ainurrahmi Imanda Riska Indah Cahyanti Saeful Hamdi Sari, Kadek Dwijayanti Komala Septiani, Ria Siti Halimatun Jannah Sofiansyah Fadli Suhaini Suhaini Tantoni, Ahmad Wafiah Murniati Wahyu Hadi, Wahyu Wire Bagye Yuan Sa'adati