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All Journal Jurnal Dedikasi Journal of Information Technology and Computer Science (JOINTECS) Jurnal Sains dan Informatika Jurnal Teknoinfo Jurnal Tekinkom (Teknik Informasi dan Komputer) Abdimas Singkerru Proceedings Series on Physical & Formal Sciences Aptekmas : Jurnal Pengabdian Kepada Masyarakat Prosiding University Research Colloquium Journal of Electrical Engineering Academia Open Jurnal Komunikasi Bisnis dan Teknologi Digital
Hamzah Setiawan
Universitas Muhammadiyah Sidoarjo
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Application of Data Mining Using the Support Vector Machine (SVM) Method to Analyze Fashion Retail Products to Determine Trends: Penerapan Data Mining Dengan Menggunakan Metode Support Vector Machine (SVM) Untuk Menganalisa Produk Fashion Retail Untuk Menentukan Tren Hamzah Setiawan
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.
Sistem Prediksi Kelulusan Mahasiswa Fakultas Saintek Universitas Muhammadiyah Sidoarjo Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Moch Ridwan Alwi; Hindarto; Hamzah Setiawan
Jurnal Komunikasi Bisnis dan Teknologi Digital Vol. 1 No. 1 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jkbtd.v1i1.264

Abstract

Perguruan tinggi swasta maupun negeri mewajibkan mahasiswanya untuk lulus. Begitu pula dengan perguruan tinggi swasta di Sidoarjo, seperti Universitas Muhammadiyah Sidoarjo (UMSIDA), tingkat kelulusan mahasiswanya dapat berdampak pada akreditasi program studi. Pentingnya menggunakan berbagai metode untuk menentukan jumlah mahasiswa yang akan mendaftar dan lulus, mengingat pentingnya nilai akreditasi dalam kelulusan mahasiswa. Memprediksi kelulusan siswa memungkinkan persiapan dan dukungan yang memadai bagi siswa untuk berhasil menyelesaikan studinya. Memiliki sistem yang dapat meramalkan seberapa cepat atau lambat seorang mahasiswa akan lulus akan memperlancar pengembangan sistem kampus bagi mahasiswa. Penelitian ini memanfaatkan Artificial Neural Network (JST) dengan pendekatan backpropagation untuk meramalkan kelulusan siswa. Data masukan untuk pelatihan JST ini bersumber dari Fakultas Sains dan Teknologi Universitas Muhammadiyah Sidoarjo (UMSIDA) tentang tingkat kelulusan mahasiswa tahun 2015 sampai dengan tahun 2019. Hasil pengujian menunjukkan bahwa Mean Square Error (MSE) pada keluaran JST sebesar 0,000141295, pada pengujian akurasi didapatkan nilai akurasi 93.428901%. Hal ini menunjukkan bahwa metode backpropagation dengan ANN dapat dimanfaatkan secara efektif untuk memprediksi kelulusan mahasiswa.
Co-Authors Arif Senja Fitriani Hindarto Ika Ratna Indra Astutik Irwan Alnarus Kautsar Jefry Fernando Moch Ridwan Alwi Mochamad Alfan Rosid Mochammad Septa Sandy Mohamad Haris Muzadi Nur Maslikhatun Nisak Nuril Lutvi Azizah Paramitha Amelia Kusumawardani Rachmat Firdaus Rayhanantha Akbar Putra Prasetyo Ribangun Bamban Jakaria Siti Cholifah Siti Cholifah Uce Indahyanti Wiwik Sumarmi Yunianita Rahmawati