DATA MINING UNTUK MEMPREDIKSI JENIS TRANSAKSI NASABAH PADA KOPERASI SIMPAN PINJAM DENGAN ALGORITMA C4.5
DOI:
https://doi.org/10.30865/mib.v1i2.323Abstract
In the field of business, data mining techniques are used to support a wide range of business intelligence applications such as customer profiling, targeted marketing, workflow management, store layout and fraud detection. Lack of predicting the type of Customer Transactions that exist in this cooperative makes the manager or leader of this cooperative difficult in terms of providing loans and also in accepting new cooperative members. Leaders also have difficulties in knowing the profession of customers who borrow the most at this cooperative. As for the process of predicting the type of Customer Transaction during this time, there is a saving and loan operation is still based on the direct view of the type of Customer Transaction and see the existing ledger notes of the cooperative.References
F. A. Hermawati, Data Mining, Yogyakarta: Andi, 2013.
K. and E. T. Luthfi, Algoritma Data Mining, Yogyakarta: Andi, 2009.
P. R. Indonesia, Pasal 3 UU No. 12, Jakarta: Republik Indonesia, 1967.
F. Sulianta and D. Juju, Data Mining Meramalkan Bisnis Perusahaan, Jakarta: Elex Media Komputindo, 2010.
P. P. Widodo, R. T. Handayanto and H. , Penerapan Data Mining Dengan Matlab, Bandung: REKAYASA SAINS, 2013.
M. Pemodelan Visual dengan UML, Yogyakarta: Graha Ilmu, 2005. [7] G. Gunadi and D. I. Sensuse, "PENERAPAN METODE DATA MINING MARKET BASKET ANALYSIS TERHADAP DATA PENJUALAN PRODUK BUKU DENGAN MENGGUNAKAN ALGORITMA APRIORI DAN FREQUENT PATTERN GROWTH (FP-GROWTH) : STUDI KASUS PERCETAKAN PT. GRAMEDIA," TELEMATIKA, pp. 118-132, 2012.
E. Buulolo, “ALGORITMA APRIORI PADA DATA PENJUALAN DI SUPERMARKET,†in Seminar Nasional Inovasi dan Teknologi Informasi 2015 (SNITI), 2015, no. September 2015, pp. 4–7.
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