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All Journal Multitek Indonesia : Jurnal Ilmiah JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JSAI (Journal Scientific and Applied Informatics) Jurnal ABDINUS : Jurnal Pengabdian Nusantara Academia Open Indonesian Journal of Cultural and Community Development Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) International Journal of Engineering, Science and Information Technology Bulletin of Information Technology (BIT) Indonesian Journal of Innovation Studies PELS (Procedia of Engineering and Life Science) Procedia of Social Sciences and Humanities MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Prosiding University Research Colloquium Physical Sciences, Life Science and Engineering Indonesian Journal of Applied Technology Journal of Technology and System Information Journal for Technology and Science Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani
Setiawan, Hamzah
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Journal : Academia Open

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Application of Data Mining Using the Support Vector Machine (SVM) Method to Analyze Fashion Retail Products to Determine Trends Setiawan, Hamzah
Academia Open Vol 9 No 1 (2024): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.9.2024.8581

Abstract

This study addresses the escalating volume of research by proposing an efficient research storage system through data mining-based categorization. Employing the Support Vector Machine (SVM) method on a dataset comprising 541,910 retail product purchases, the research achieves a significant 96.2% accuracy in categorization using the cross-entropy loss function. The SVM method proves instrumental in systematically organizing research based on fields, methods, and outcomes, showcasing its efficacy in large-scale research storage and organization. This study highlights the SVM's potential as a vital tool for governments and private organizations to enhance access and utilization of research information. The results underscore the positive impact of SVM in overcoming the complexity of research storage on a broader scale, contributing to the advancement of efficient research management systems. Highlights: Efficient SVM Data Management: Proposes SVM-based data mining for effective research information storage. 96.2% Accuracy in Categorization: SVM with cross entropy achieves high accuracy in classifying research data. Organized Access for Better Utilization: SVM organizes research systematically, enhancing accessibility and utilization for government and private sectors. Keywords: Support Vector Machine, Data Mining, Dataset, Retail.
Co-Authors Abidin, Husnul Ade Eviyanti Adi Putra, Lutfi Alshaf Pebrianggara Amelia Kusumawardani, Paramitha Amelia, Paramitha Arief Wicaksono, Arief Arif Senja Fitrani Arisandi, Ricky Renaldo Asiddiq, Afnizar Maulana Assara, Enggi Sabrilla Astutik, Ika Ratna Indra Aulia Aliffiandi, Rizca Aziziyah, Ismi Anisa Duwi Rahayu Grahita Albarika, Ayu Hasan, Jamal Hindarto Imanda, Almyra Gitta Intan Nuraini Mochamad Alfan Rosid Muhammad Fikri Muhammad, Fajar Muhammad, Khithoh Sabda Nur Maslikhatun Nisak Nuril Lutvi Azizah Paramitha Amelia Kusumawardani Pralaya, Gilang Pratiwi, Rosa Machmuda Ramdansyah, Adiffanani Ribangun Bamban Jakaria Rohman Dijaya RR. Ella Evrita Hestiandari sandy, Mochamad septa Senja Fitrani, Arief Sinta Nuriyah, Rizky SITI CHOLIFAH Sumarno . Suprianto1, Suprianto Taurusta, Cindy Triwahono, Handi Uce Indahyanti Usqi Salsabila, Firdausi Wirabumi Putra, Cakra Wiwik Sumarmi Yansah, Muhammad Kahfi Yunianita Rahmawati, Yunianita