eprintid: 3867
rev_number: 9
eprint_status: archive
userid: 46
dir: disk0/00/00/38/67
datestamp: 2022-09-01 02:25:25
lastmod: 2022-09-03 17:14:28
status_changed: 2022-09-01 02:25:25
type: thesis
metadata_visibility: show
creators_name: Nur Alya, Nisrina
creators_name: Harsani, Prihastuti
creators_name: Qur’ania, Arie
creators_NPM: 065116004
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_name: Harsani, Prihastuti
contributors_name: Qur’ania, Arie
contributors_NIDN: 0427017501
contributors_NIDN: 0427047601
corp_creators: Universitas Pakuan
corp_creators: FMIPA
corp_creators: Ilmu Komputer
title: IMPLEMENTASI METODE LOGIKA FUZZY DAN JARINGAN SYARAF TIRUAN PERCEPTRON PADA KLASIFIKASI KELAYAKAN PEMBERIAN KREDIT BERBASIS WEB
ispublished: pub
subjects: QK
divisions: sch_ecs
full_text_status: none
abstract: Abstract
Bank is a financial institution that has many activities, one of which is serving credit activities. Before giving or extending credit to debtors. Previously, the bank conducted a careful and thorough assessment and analysis of potential debtors in accordance with the procedures for granting credit that had been applied. Therefore, analysis in granting credit is needed so that credit is channeled to be of good quality and as desired.To solve this problem, it is necessary to have an application system, one of which is a decision support system application. This application can provide a classification of creditworthiness so that it can quickly and accurately select prospective debtors who are feasible and unworthy.There are several methods used in the system including using the Perceptron ANN method. Perceptron is used to classify the separation pattern linearly while to determine the weighting using fuzzy logic method.The results of this study are in the form of an application system that can assist the bank in the classification of creditworthiness. The level of accuracy of the system using the Perceptron ANN method and Fuzzy Logic was obtained by 93% on training data as much as 700 data and 83% on testing data as much as 300 data.
date: 2021-06
date_type: published
institution: Universitas Pakuan
department: Ilmu Komputer
thesis_type: Skripsi
thesis_name: Sarjana
citation:   Nur Alya, Nisrina and Harsani, Prihastuti and Qur’ania, Arie  (2021) IMPLEMENTASI METODE LOGIKA FUZZY DAN JARINGAN SYARAF TIRUAN PERCEPTRON PADA KLASIFIKASI KELAYAKAN PEMBERIAN KREDIT BERBASIS WEB.  Skripsi thesis, Universitas Pakuan.