GUNAWAN, MARCO (2023) Implementasi Algoritma Support Vector Machine (SVM) Untuk Memprediksi Kategori Produk Pada Aplikasi E-Commerce Lielien Shop. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Penelitian ini bertujuan untuk melakukan implementasi algoritma SVM (Support Vector Machine) pada aplikasi Lielien Shop sebagai metode prediksi kategori produk. Melalui pengembangan aplikasi berbasis web, algoritma SVM digunakan untuk memprediksi kategori produk berdasarkan fitur-fitur yang relevan. Penelitian ini juga mengevaluasi responsivitas website yang dirancang menggunakan metode prototyping dan algoritma SVM, serta menguji efektivitas penggunaan algoritma SVM dalam pengkategorian produk pada Lielien Shop. Metode pengumpulan data yang digunakan adalah wawancara dan observasi. Hasil penelitian menunjukkan bahwa implementasi algoritma SVM pada Lielien Shop berhasil memberikan hasil prediksi yang akurat, meningkatkan pengalaman pengguna, efisiensi pengelolaan inventaris, dan responsivitas website. Penelitian ini memberikan kontribusi dalam pengembangan aplikasi e-commerce berbasis web dan pemahaman tentang efektivitas penggunaan algoritma SVM dalam pengkategorian produk. Kata Kunci: Implementasi algoritma SVM, Aplikasi Lielien Shop, Prediksi kategori produk, Responsivitas website, Pengalaman pengguna, Pengelolaan inventaris, Metode prototyping, Efektivitas algoritma SVM, Pengkategorian produk, Penelitian kualitatif. This research aims to implement the SVM (Support Vector Machine) algorithm in the Lielien Shop application as a method for predicting product categories. Through the development of a web-based application, the SVM algorithm is used to predict product categories based on relevant features. The research also evaluates the responsiveness of the website designed using prototyping methods and the SVM algorithm, as well as tests the effectiveness of using the SVM algorithm in product categorization in Lielien Shop. The data collection methods used in this research are interviews and observations. The results of the research show that the implementation of the SVM algorithm in Lielien Shop successfully provides accurate prediction results, improves user experience, inventory management efficiency, and website responsiveness. This research contributes to the development of web-based e-commerce applications and understanding the effectiveness of using the SVM algorithm in product categorization. Keywords: SVM algorithm implementation, Lielien Shop application, product category prediction, website responsiveness, user experience, inventory management, prototyping method, effectiveness of SVM algorithm, product categorization, qualitative research.
| Item Type: | Thesis (S1) |
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| Call Number CD: | FIK/INFO 23 003 |
| NIM/NIDN Creators: | 41518320035 |
| Uncontrolled Keywords: | Implementasi algoritma SVM, Aplikasi Lielien Shop, Prediksi kategori produk, Responsivitas website, Pengalaman pengguna, Pengelolaan inventaris, Metode prototyping, Efektivitas algoritma SVM, Pengkategorian produk, Penelitian kualitatif. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 18 Dec 2023 04:05 |
| Last Modified: | 18 Dec 2023 04:05 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/84741 |
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