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All Journal Proceedings of KNASTIK Jurnal Penelitian Pendidikan IPA (JPPIPA) BAREKENG: Jurnal Ilmu Matematika dan Terapan JUTEI (Jurnal Terapan Teknologi Informasi) Journal of Physics and Its Applications IAIC Transactions on Sustainable Digital Innovation (ITSDI) Journal of Fundamental Mathematics and Applications (JFMA) SERVIRISMA: Jurnal Pengabdian kepada Masyarakat Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Jurnal Tadris Matematika (JTMT)
Joko Purwadi
Universitas Kristen Duta Wacana
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation Other

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Journal : Jurnal Penelitian Pendidikan IPA (JPPIPA)

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Support Vector Machine for Classification: A Mathematical and Scientific Approach in Data Analysis Restiani, Yulia; Purwadi, Joko
Jurnal Penelitian Pendidikan IPA Vol 10 No 11 (2024): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i11.8122

Abstract

In this research, SVM will be used to differentiate between plain nail art designs (class 0), 3D nail art designs (class 1), and hand painting nail art designs (class 2). The dataset used consists of images of nail designs that have been collected and analyzed previously. First, the dataset is divided into three different classes based on the type of nail design. The first class (class 0) includes plain nail art designs, then the second class (class 1) is 3D nail art designs, and the third class (class 2) is hand painting nail art designs. This process is carried out to allow SVM to learn the feature differences between the two types of designs. The data used will be divided into training and testing data and divided into three data division schemes, namely 60/40, 70/30, and 80/20. Based on the results of the research discussed, it can be concluded that classification using the Linear SVM model on three data sharing schemes provides the best level of accuracy on the 80/20 scheme, namely 81.25%. Meanwhile, classification using the non-linear SVM model achieved the highest level of accuracy of 95% in the 80/20 scheme with the RBF Kernel. Thus, the SVM model that is suitable for classifying nail art designs is a non-linear SVM model with the 80/20 scheme. The accuracy results obtained from this research also show that SVM provides good performance in classifying nail art designs.
Co-Authors Adam, Muhammad Kevin Albert Briliakta G S. Antonius Rachmat Chrismanto Argo Wibowo Balisa, Delfia Budi Susanto Bueraheng, Surairee Denna Prestianti Gani Indriyanta Gloria Virginia Gumelar, Bagus Gunawan Santosa Halim Budi Santoso Hilmi, Isom I Nyoman Marcel Mahardika Jong, Jek Siang Kadek Ayu Yanti Pawitri (Prog. Studi Teknik Informatika Fakultas Teknik Universitas Kristen Duta Wac Kusuma, Damar Yoga Liandaru, Putu Gde Qwat Bayu Minarni, Nani Ningsih, Zhilvia Noviana Nugroho Agus H. Nugroho Agus Haryono Nur Muallifah, Azizah Restiani, Yulia Rosa Delima Sevina, Grisselia Rizky Soesanto, Qidir Maulana Binu Soffi Widyanesti Priwantoro, Soffi Widyanesti Syariful Fahmi, Syariful Theresa Mariana Kurniasih Triyoga, Arilia Widiantoro, Tri