APLIKASI SELEKSI PENENTUAN NASABAH UNTUK PENJUALAN BARANG SECARA KREDIT DENGAN ALGORITMA K-NEAREST NEIGHBOR

  • Edy Nasri Universitas Banten Jaya
  • A. Selamet AW Universitas Banten Jaya
Keywords: RPL, SPK, K-Nearest Neighbor, debtor, creditor

Abstract

There is a lot of business competition in this case credit sales, where credit sales are made using the gradual payment method with requirements that must be agreed upon by the credit provider and credit recipient. In this XYZ company there are problems that often occur including when officers select and determine credit recipients (debtors) in accordance with the requirements given by the company, this company also still uses traditional methods in promotion and there is no decision-making system to be able to assist in determining the prospective debtor In addition, creditors (creditors) find it difficult to receive debtor data reports. In determining the debtor to get credit sales, in this study we will use the SPK K-Nearest Neighbor method where this method is to calculate the closeness between old cases and new cases in order to facilitate officers in determining prospective borrowers with a decision support system using the K-Nearest method Neighbor. In addition to facilitating and helping creditors to be able to find out the debtor data report in more detail and to increase sales. The results of this study, get results from calculating the closeness between new cases with old cases using the K-Nearest Neighbor method. And from some calculations with that method, the greatest value will be used to predict the next cases.

References

Asahar Johar T, dkk, 2016, Implementasi Metode K-Nearest Neighbor (Knn) Dan Simple Additive Weighting (SAW) Dalam Pengambilan Keputusan Seleksi Penerimaan Anggota Paskibraka, Jurnal Pseudocode, 09/2016, Vol 03, hal 98-112.
Dharma Gita, Virja, 2013, Solution Selling dari Rindu Order ke Banjir Order, Jakarta: PT Gramedia Prakasa Utama.
Fathansyah, 2012, Basis Data, Bandung : Informatika.
Helilintar, Risa, dkk., 2017, DATA MINING: K-Nearest Neighbor, Kediri: Fakultas Teknik Universitas Nusantara PGRI Kediri.
J., Han, and Kamber M., 2006, Data Mining: Concept and Techniques, New York : Morgan Kaufmann Publisher, h. 197.
Kusrini, 2008, Strategi Perancangan dan Pengelolaan Basis Data, Yogyakarta : CV. Andi Offset.
Mahiswan, Enggar, 2016, Untung Puluhan Juta dari Bisnis Anti Expired, Yogyakarta : FlashBooks.
Marbun, Murni, 2018, Buku Ajar Sistem Pendukung Keputusan Penilaian Hasil Belajar dengan Metode Topsis, Medan: CV. Rudang Mayang.
Mariana, Novita, dkk., 2015, Penerapan Algoritma K-NN (K-Nearest Neighbor) untuk Deteksi Penyakit (Kanker Serviks), Dinamika Informatika, 03/2015, Vol.7 No.1, h. 26-34.
Mustakim, dan Giantika Oktafiani F, 2016, Algoritma K-Nearest Neighbor Classification Sebagai Sistem Prediksi Predikat Prestasi Mahasiswa, Jurnal Sains, Teknologi dan Industri, 06/2016, Vol 13, hal 195-202.
Subakti, I., 2002, Sistem Pendukung Keputusan, Surabaya : Institut Teknologi Sepuluh November.
Subakti, Irfan, 2002, Sistem Pendukung Keputusan (Decision Support System), Yogyakarta: Graha Ilmu.
Sumarlin, 2015, Implementasi Algoritma K-Nearest Neighbor (K-NN) sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM, Jurnal Sistem Informasi Bisnis, 04/2015, Vol 01, hal 52-61.
Turban, E., 2005, Decision Support System and Intelligent System, Yogyakarta : Andi Publisher.
Wahyu Lukman Hakim, Pengertian Prototype, https://www.scribd.com/doc/58298607/ Pengertian-Prototype diakses pada tanggal 22 Nov 2019, 10:36 WIB.
Published
2020-02-26
How to Cite
Nasri, E., & AW, A. (2020). APLIKASI SELEKSI PENENTUAN NASABAH UNTUK PENJUALAN BARANG SECARA KREDIT DENGAN ALGORITMA K-NEAREST NEIGHBOR. Jurnal Ilmiah Sains Dan Teknologi, 4(1), 1-11. Retrieved from http://ejournal.lppm-unbaja.ac.id/index.php/saintek/article/view/817