IMPLEMENTASI DATA MINING DENGAN MENGGUNAKAN ALGORITMA APRIORI UNTUK MENGOPTIMALKAN POLA PENJUALAN PRODUK ELEKTRONIK

RAHAYU KUSNITA DEWI, NPM 2009100106 (2025) IMPLEMENTASI DATA MINING DENGAN MENGGUNAKAN ALGORITMA APRIORI UNTUK MENGOPTIMALKAN POLA PENJUALAN PRODUK ELEKTRONIK. Tugas_Akhir(Artikel) Building of Informatics, Technology and Science (BITS), 7 (1). pp. 513-525. ISSN 2684-8910(p-ISSN) 2685-3310(e-ISSN)

	Text COVER.pdf Download (4MB)
	Text ARTIKEL.pdf Restricted to Registered users only Download (746kB)

Abstract

Penelitian ini membahas penerapan algoritma Apriori dalam analisis data penjualan produk elektronik. Hasil penelitian menunjukkan bahwa algoritma Apriori efektif dalam menemukan pola pembelian konsumen melalui analisis asosiasi, yang memungkinkan identifikasi kombinasi produk yang sering dibeli bersama. Kombinasi produk dengan hubungan pembelian kuat, seperti AAA Batteries (4-pack) dan USB-C Charging Cable (confidence 0,9), serta Wired Headphones dan USB-C Charging Cable (confidence 0,7), dapat dimanfaatkan untuk strategi bundling dan peningkatan penjualan. Dari 18 jenis produk elektronik yang dianalisis, tujuh produk memenuhi syarat minimum support, menunjukkan potensi tinggi untuk analisis lanjutan. Algoritma Apriori juga terbukti cocok untuk dataset skala menengah karena kesederhanaannya, meskipun kurang efisien dibandingkan FP-Growth pada data besar. Penelitian ini menyimpulkan bahwa penerapan algoritma Apriori mendukung pengambilan keputusan bisnis berbasis data, terutama dalam memahami perilaku konsumen, efisiensi pengelolaan stok, dan pengembangan strategi pemasaran. Kata kunci : Data Mining; Algoritma Apriori; Pola Penjualan; Elektronik =========================================== This study discusses the application of the Apriori algorithm in analyzing electronic product sales data. The results show that the Apriori algorithm is effective in finding consumer purchasing patterns through association analysis, which allows the identification of product combinations that are often purchased together. Combinations of products with strong purchasing relationships, such as AAA Batteries (4-pack) and USB-C Charging Cable (confidence 0.9), and Wired Headphones and USB-C Charging Cable (confidence 0.7), can be utilized for bundling strategies and increasing sales. Of the 18 types of electronic products analyzed, seven products met the minimum support requirements, indicating high potential for further analysis. The Apriori algorithm also proved suitable for medium-scale datasets due to its simplicity, although it is less efficient than FP-Growth on big data. This study concludes that the application of the Apriori algorithm supports data-based business decision making, especially in understanding consumer behavior, stock management efficiency, and marketing strategy development. Keywords: Data Mining; Apriori Algorithm; Sales Pattern; Electronics

Item Type:	Article
Uncontrolled Keywords:	Data Mining; Algoritma Apriori; Pola Penjualan; Elektronik===============Data Mining; Apriori Algorithm; Sales Pattern; Electronics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	07 Jul 2026 03:24
Last Modified:	07 Jul 2026 03:24
URI:	http://repository.ulb.ac.id/id/eprint/2584

Actions (login required)

View Item