MULTINOMIAL NAIVE BAYES UNTUK KLASIFIKASI STATUS KREDIT MITRA BINAAN DI PT. ANGKASA PURA I PROGRAM KEMITRAAN

Main Article Content

Meilani T Bunga
Bertha S Djahi
Yelly Y Nabuasa

Abstract

Status classification of partner acordiing to sector parimeter, loan disbursement, loan reimbursment, loan arrears, remaining loan and grace period is very important in anticipating the case in PT. Angkasa Pura I. Problematic credit is very unbeneficial for the PT. Angkasa Pura I because it will disturb the economy condition of a company and will affect the next small and medium enerprises (SME). To solve this, the reserch uses Multinominal Naive Bayes to method to classify the partners status in the PT. Angkasa Pura I according to the parimeter that is divided into 4 clases namely smooth class, less smooth class, doubted and jammed class. The process used was classification process where it calculated probability value and the atribute of the partner. The data used in this research is consisted of 148 that taken from 2012-2015. The final result, after the classification is done, the class probability value that was taken randomly is gained, with the resuld to system test with 5 times of testing data division that is taken randomly, it is gained the accuracy as big as 86,56%, precision is as big as 73%, recall is as big as 73% and F-1 Measure is as big as 73%.

Downloads

Download data is not yet available.

Article Details

How to Cite
Bunga, M., Djahi, B., & Nabuasa, Y. (2018). MULTINOMIAL NAIVE BAYES UNTUK KLASIFIKASI STATUS KREDIT MITRA BINAAN DI PT. ANGKASA PURA I PROGRAM KEMITRAAN. Jurnal Komputer Dan Informatika (JICON), 6(2), 30-34. Retrieved from https://ejurnal.undana.ac.id/jicon/article/view/512
Section
Articles

References

[1] Bustami, 2013, Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Data Nasabah Asuransi, TECHSI : Jurnal Penelitian Teknik Informatika, Vol. 3, No.2, Hal. 127-146.
[2] Jiang, L., Wang, S., Li, C., Zhang, L., 2014, Structure extended multinomial naive bayes, doi:10.1007/s10115-014-0746-y, diakses 11 mei 2017.
[3] Raymond, McLeod, J., 2001, Sistem Informasi Edisi 7 Jilid 2, Prenhallindo, Jakarta.
[4] Saleh, A., 2014, Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga, Yogyakarta.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.