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All Journal Journal of Informatics Management and Information Technology Jurnal Ilmiah Informatika dan Ilmu Komputer
Sanjaya, Ival
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Peran Big Data dalam Inovasi Bisnis Digital: Pendekatan Tinjauan Literatur Sistematis Fadli, Muhammad; Prasetio, Mugi; Sanjaya, Ival; Surono, Muhammad; Dewantoro, Mahendra; Suryono, Ryan Randy
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 4 No. 1 (2025): Volume 4 Nomor 1 March 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v4i1.48

Abstract

Penelitian ini meninjau bagaimana Big Data berperan dalam inovasi bisnis digital, terutama dalam membantu pengambilan keputusan strategis, memperbaiki efisiensi operasional, serta menciptakan Produk dan layanan digital yang benar-benar pas dengan kebutuhan pengguna. Dengan menggunakan pendekatan tinjauan literatur sistematis, penelitian ini mengidentifikasi manfaat signifikan Big Data, termasuk kemampuannya untuk menyediakan analisis mendalam, memprediksi tren pasar, dan personalisasi layanan pelanggan. Namun, penelitian ini juga mengungkap berbagai tantangan dan kendala dalam implementasi Big Data, seperti keterbatasan infrastruktur teknologi, kualitas data yang rendah, serta isu privasi dan keamanan data. Hasil penelitian menunjukkan bahwa pemanfaatan Big Data yang optimal dapat meningkatkan daya saing bisnis digital, tetapi membutuhkan dukungan infrastruktur yang memadai dan kepatuhan terhadap regulasi yang berlaku. Studi ini berkontribusi pada pengembangan pemahaman tentang bagaimana Big Data dapat diintegrasikan ke dalam strategi inovasi bisnis digital untuk mendorong pertumbuhan dan keberlanjutan bisnis di era digital.
Segmentasi Produk Fashion Berdasarkan Harga, Ukuran, dan Merek Menggunakan K-Means di Rapidminer Sanjaya, Ival; Nitami Evita Inonu; Muhammad Fahmi Fudholi; Adelia Pratiwi; Heni Sulistiani
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.651

Abstract

Tight competition and product diversity are the hallmarks of the fashion industry, especially in terms of price variation, size, and brand. To help the process of making more accurate business decisions, product segmentation is needed to identify the characteristics of each group. This study utilizes the K-Means Clustering algorithm to group fashion products based on these attributes. The implementation is carried out using the RapidMiner platform, starting with the data normalization stage and the transformation of categorical attributes into numeric form. The optimal number of clusters is determined through the elbow method approach, which shows a significant decrease in the average distance between data in the cluster. The clustering results show the formation of product groups with different characteristics, which can be utilized in stock planning and marketing strategies. This study confirms that the K-Means algorithm is effective in analyzing the distribution of fashion products based on the main attributes they have.
Co-Authors Adelia Pratiwi Dewantoro, Mahendra Fadli, Muhammad Heni Sulistiani Muhammad Fahmi Fudholi Nitami Evita Inonu Prasetio, Mugi Ryan Randy Suryono Surono, Muhammad