Perancangan Data Mining Apriori pada RYN’Smart Mempawah

Adang Kaswara, Amar Pegirosa Natasuwarna

Abstract


Along with the rapid development of technology and can not be denied that technology as a secondary need for the users. In business competition, requires developers to determine a right strategy that can increase sales. To find out what items are purchased by consumers, can be done by using market basket analysis techniques, namely analysis of the habits purchased by consumers. Apriori is an algorithm that is widely used to determine the pattern of relationships between products that are often purchased in a store. The purpose of this research is how to apply Data Mining in the item sales transaction database of items at RYN 'Smart Mempawah, and want to know the application of the Apriori Algorithm on any items purchased together. Researchers use desktop-based applications with JAVA programming languages. The results of this research is Apriori Applications that can analyze any items purchased together, so that they can be used to determine effective item placement to increase a sales value at RYN's Smart Mempawah.

Keywords


RYN’Smart Mempawah; Data Mining; Java; Apriori

Full Text:

PDF

References


Yanto, Robi, Khoiriah, Riri 2015. “Implementasi Data Mining dengan Metode Algoritma Apriori dalam Menentukan Pola Pembelian Obat”.

Agung,Thoriq, M dan Nurhadiyono, Bowo., 2015, “Penerapan Data Mining pada Data Transaksi Penjualan Untuk Mengatur Penempatan Barang Menggunakan Algoritma Apriori”.

Anggraeni, Saputra dan Norita., 2013, “Implementasi Algoritma Apriori pada Sistem Persediaan Obat (Studi Kasus: Apotek Rumah Sakit Estomohi Medan”.

Rosa, A.S., Shalahuddin,M. 2013. Rekayasa Perangkat Lunak : Terstruktur dan Berorientasi Objek. Informatika. Bandung.

Nugroho, A., 2010, Rekayasa Perangkat Lunak Berbasis Objek dengan Metode USDP, Andi, Yogyakarta.

Pressman., 2012, Rekayasa Perangkat Lunak dan Pendekatan Praktisi (buku satu), Edisi 7. Andi, Jogyakarta.




DOI: http://dx.doi.org/10.30700/.v1i1.802

Article Metrics

Abstract view : 170 times
PDF - 191 times

Refbacks

  • There are currently no refbacks.