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Journal : TEKNIK INFORMATIKA

SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal Masdar Desiawan; Achmad Solichin
JURNAL TEKNIK INFORMATIKA Vol 17, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.37422

Abstract

The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.
SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal Desiawan, Masdar; Solichin, Achmad
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.37422

Abstract

The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.
Co-Authors Abdullah 'Alim Abdurrohim Musthofa Achmad Maulana Agus Harjoko Agus Santoso Ahmad Ihsanudin Ahmad Zainul Mafakhir Akbar, Kafi Kurnia Alfredo Pasaribu Alhafiz, Muhammad Ihza Ananda Surya, Archie Andi Hakim Arif Anggi Ayu Ningtyas Anindya Putri Pradiptha Arif, Andi Hakim Asmoro, Phaksi Bangun Bayu Raditya Nasution Chaerullah, Dhiesky Chalid, Iqbal Chandra, Joko Christian Dasril Aldo Dedy Mirwansyah Desena, Wahyu Desiawan, Masdar Dewantara, Erno Kurniawan Dwi Kristanto Dwi Kristanto Emil Salim Fadlan Amrullah Fahrullah Fahrullah Galih Gumilar Widhasmara Goenawan Brotosaputro Hanafi, Mohammad Afif Hari Soetanto Irennada Ismail Adi Susanto Khaeri Diniari Khansa Khairunnisa Kurnianta, Kristana Lia Amellia Putri Lutfi Nukman Majid, Muhammad Farras Masdar Desiawan Mochammad Andika Putra Mohammad Syafrullah Muhamad Refaldi Muhammad Agus Arianto Muhammad Agus Arianto Muhammad Ali Akbar Muhammad Arif Kurniawan Muhammad Fahrizal Muhammad Hamdi Sukriyandi Muhammad Verdiansyah Muharam, Asep Budiyana Nanda Arista Rizki Nariza Wanti Wulan Sari Nazori AZ Nita, Yulia Noor Ferdyansyah Nugroho, Ludi Nurwijayanti Obby Oktafianto Painem, Painem Pradana, Rizky Pradiptha, Anindya Putri Pramudita, Bagas Prayogi, Muhamad Nur Rahmat Kurniawan Rasyid, Annisa Ratna Kusumawardani Reka Dwi Syaputra Restu Maulunida Reva Ragam Santika Richki Hardi Riki Wijaya Rizki Darmawan, Dika Robby Suganda Rusdah Rusdah Saddam, M Amiruddin Setiyadi, Prambudi Sister, Maya Gian Suherman Achmad Syahrul, Ahmad Tan Wee Chang Tetlageni, Muhamad Ridho Triyono, Gandung Tulodo, Bernadeta Asri Rejeki Ummu Habibah Romlah Utomo Budiyanto Wati, Lisna Wirasno, Wirasno Zainal A. Hasibuan Zulfikar Rosadi