Prediksi Pengajuan Kredit Usaha Pada Koperasi Menggunakan Algoritma K-Nearest Neighbor
Cooperative activities have become the activities most needed by many people because they are related to money, cooperatives are places that provide loans to housewives and also workers in a certain area or environment, the lack of interest offered by this cooperative is considered very easy. and very helpful for many parties in facilitating financial affairs, especially in financial matters, because the convenience offered by the cooperative makes many interested people ask for the same thing resulting in vulnerability to fraud, the importance of making predictions on prospective new business loan applications can help reduce the worst risks from various risks that occur in the future, in this study the k-nearest neighbor algorithm will be used as a prediction algorithm for prospective business credit applications at cooperatives, the value obtained is the value of training data or data records of several previous customers so as to easier to know new data as test data in a study. The results found in this study for prospective business credit applications are "Not Eligible" seen to the closest value based on the smallest value with (closest distance) between one another as many as 3 distances, namely numbers 1, 2 and 3 where number 1 states "Not feasible ”, at the second closest distance stating “Eligible” and number 3 stating “Not Eligible”, the most results stated Not Eligible so that the decision value on new customers had to be rejected “Not eligible” to be accepted
H. Widayu, S. Darma, N. Silalahi, and Mesran, “Data Mining Untuk Memprediksi Jenis Transaksi Nasabah Pada Koperasi Simpan Pinjam Dengan Algoritma C4.5,” Issn 2548-8368, vol. Vol 1, No, no. June, p. 7, 2019, [Online]. Available: https://ejurnal.stmik-budidarma.ac.id/index.php/mib/article/view/323.
H. Widayu, S. Darma, N. Silalahi, and Mesran, “Data Mining Untuk Memprediksi Jenis Transaksi Nasabah Pada Koperasi Simpan Pinjam Dengan Algoritma C4.5,” Issn 2548-8368, vol. Vol 1, No, no. June, p. 7, 2017, doi: 10.30865/mib.v1i2.323.
M. Rizki and G. Ginting, “Penerapan Metode Preference Selection Index Dalam Pemilihan Teller Terbaik,” Build. Informatics, Technol. Sci., vol. 2, no. 2, pp. 127–134, 2020, doi: 10.47065/bits.v2i2.136.
T. C. Pratama, “Penerapan Metode K-Nearest Neighbour Dalam Menentukan Kelayakan Calon pengajuan kredit usaha Yang Layak Untuk Kredit Mobil ( Studi Kasus : Pt . Astra International , Tbk-Toyota ),” JURIKOM (Jurnal Ris. Komputer), vol. 5, no. 4, pp. 402–408, 2018.
D. Evanko, “Optical imaging of the native brain,” Nat. Methods, vol. 7, no. 1, p. 34, 2010, doi: 10.1038/nmeth.f.284.
S. Juanita, “Analisa Strategi Bisnis Penjualan Online,” Konf. Nas. ICT-M Politek. Telkom, pp. 254–260, 2017, [Online]. Available: http://journals.telkomuniversity.ac.id/knip/article/view/557.
G. Abdurrahman, “Clustering Data Kredit Bank Menggunakan Algoritma Agglomerative Hierarchical Clustering Average Linkage,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 4, no. 1, p. 13, 2019, doi: 10.32528/justindo.v4i1.2418.
M. Laia, R. K. Hondro, and T. Zebua, “Implementasi Pengolahan Citra dengan Menggunakan Metode K-Nearest Neighbor Untuk Mengetahui Daging Ayam Busuk dan Daging Ayam Segar,” J. Ris. Komputer), vol. 8, no. 2, pp. 2407–389, 2021, doi: 10.30865/jurikom.v8i2.2818.
A. E. Woerner et al., “Forensic human identification with targeted microbiome markers using nearest neighbor classification,” Forensic Sci. Int. Genet., vol. 38, pp. 130–139, 2019, doi: 10.1016/j.fsigen.2018.10.003.
P. D. Putra, S. Sukemi, and D. P. Rini, “Peningkatan Akurasi Klasifikasi Backpropagation Menggunakan Artificial Bee Colony dan K-NN Pada Penyakit Jantung,” J. Media Inform. Budidarma, vol. 5, no. 1, p. 208, 2021, doi: 10.30865/mib.v5i1.2634.
Y. Zhang, G. Cao, B. Wang, and X. Li, “A novel ensemble method for k-nearest neighbor,” Pattern Recognit., vol. 85, pp. 13–25, 2019, doi: 10.1016/j.patcog.2018.08.003.
Amal S Menon, “Early Stage Prediction of Type Two Diabetes in Females,” Int. J. Eng. Res., vol. V9, no. 06, pp. 750–757, 2020, doi: 10.17577/ijertv9is060535.
E. W. Winarni, Teori dan Praktik Penelitian Kualitatif dan Kuantitatif PTK dan R&D. Jakarta: Bumi Aksara, 2018.
D. Prasada, “KREATIF Jurnal Ilmiah Prodi Manajemen Universitas Pamulang, Volume 7, No 1 Juni 2019,” Kreat. J. Ilm. Prodi Manaj. Univ. Pamulang, vol. 7, no. 1, pp. 55–65, 2019, doi: 2406-8616.
Z. Lv and L. Qiao, “Analysis of healthcare big data,” Futur. Gener. Comput. Syst., vol. 109, pp. 103–110, 2020, doi: 10.1016/j.future.2020.03.039.
Albi Anggito and Johan Setiawan, Metodologi Penelitian Kuantitatif. Jawa Barat: CV Jejak, 2018.
S. Aren and H. Nayman Hamamci, “Relationship between risk aversion, risky investment intention, investment choices: Impact of personality traits and emotion,” Kybernetes, vol. 49, no. 11, pp. 2651–2682, 2020, doi: 10.1108/K-07-2019-0455.
J. T. Informatika and U. Sriwijaya, “Prediksi Cuaca di Kota Palembang Berbasis,” pp. 9–18.
E. Buulolo, Buku Data Mining Untuk Perguruan Tinggi, I. 2020.
D. A. Anggoro and N. D. Kurnia, “Comparison of accuracy level of support vector machine (SVM) and K-nearest neighbors (KNN) algorithms in predicting heart disease,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 5, pp. 1689–1694, 2020, doi: 10.30534/ijeter/2020/32852020.
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