Penerapan Komputer dalam Identifikasi Barang di E-Commerce Berbasis AI

This research explores the implementation of computer vision technology in AI-based e-commerce platforms to enhance product identification and improve user experience. The study specifically examines the use of deep learning algorithms, particularly Convolutional Neural Networks (CNN), to automate product recognition and classification. The results indicate that AI-driven image search features significantly increase the speed and accuracy of product search, leading to greater customer engagement. However, challenges such as the need for high-quality datasets, varying image quality, and high initial investment costs were identified as barriers to effective implementation. The findings suggest that overcoming these obstacles can lead to improved operational efficiency and customer satisfaction. The success of AI in e-commerce depends on robust infrastructure, data quality, and skilled workforce training.

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Publication Details

Journal
Jurnal Sistem Informasi dan Ilmu Komputer
Published
2024-12-18
DOI
https://doi.org/10.59581/jusiik-widyakarya.v3i1.4455
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Penerapan Komputer dalam Identifikasi Barang di E-Commerce Berbasis AI

Sri Ratna Dewi, Salsabila Yusra, Trisatin Panggabean
Jurnal Sistem Informasi dan Ilmu Komputer
Data Mining and Machine Learning Applications
article

Penerapan Komputer dalam Identifikasi Barang di E-Commerce Berbasis AI

Sri Ratna Dewi, Salsabila Yusra, Trisatin Panggabean
article en

Abstract

This research explores the implementation of computer vision technology in AI-based e-commerce platforms to enhance product identification and improve user experience. The study specifically examines the use of deep learning algorithms, particularly Convolutional Neural Networks (CNN), to automate product recognition and classification. The results indicate that AI-driven image search features significantly increase the speed and accuracy of product search, leading to greater customer engagement. However, challenges such as the need for high-quality datasets, varying image quality, and high initial investment costs were identified as barriers to effective implementation. The findings suggest that overcoming these obstacles can lead to improved operational efficiency and customer satisfaction. The success of AI in e-commerce depends on robust infrastructure, data quality, and skilled workforce training.

Jurnal Sistem Informasi dan Ilmu KomputerVol. 3(1)
Universitas Islam Negeri Sumatera Utara (ID)
Openalex Percentile: Top 52%
Data Mining and Machine Learning Applications
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