scholarly journals Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan K-Nearest Neighbor

2020 ◽  
Vol 22 (1) ◽  
pp. 73-82
Author(s):  
Yogiek Indra Kurniawan ◽  
Tiyssa Indah Barokah
Keyword(s):  
Credit Card ◽  
Nearest Neighbor ◽  
Credit Cards ◽  
K Nearest Neighbor ◽  
The Status ◽  
Using Data

A credit card is a device payment issued by the bank certain made of plastic and useful as a tool payment on credit carried out by the owner of the card or in accordance with the name of listed in a credit card is on when making purchases goods or services. The problems facing in giving a credit cards to customers bank that have signed up is difficult to determine the category of a credit cards in accordance with the customer bank. By doing this research is expected to facilitate the bank or the analysis to determine the category of a credit card to customers bank right. The research used is by applying methods K-Nearest Neighbor to classify prospective customers in the making a credit card in accordance with the category of  customers by using data customers at the Bank BNI Syariah Surabaya. A method K-Nearest Neighbor used to seek patterns on the data customers so established variable as factors supporters in the form of gender, the status of the house, the status, the number of dependants (children), a profession and revenue annually. The results of this research shows that an average of the value of precision of 92%, the value of recall of 83%, and the value of accuracy of 93%. Thus, this application is effective to help analyst credit cards in classifying customers to get credit cards that appropriate criteria.

scholarly journals Detect Frauds in Credit Card using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 4891-4895
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Hidden Markov ◽  
K Nearest Neighbor ◽  
Detection Systems ◽  
Important Concern ◽  
On Line ◽  
Using Data ◽  
The Individual

In today era credit card are extensively used for day to day business as well as other transactions. Ascent within the variety of transactions through master card has junction rectifier to rise in the dishonest activities. In trendy day's fraud is one in every of the most important concern within the monetary loses not solely to the merchants however additionally to the individual purchasers. Data processing had competed a commanding role within the detection of credit card in on-line group action. Our aim is to first of all establish the categories of the fraud secondly, the techniques like K-nearest neighbor, Hidden Markov model, SVM, logistic regression, decision tree and neural network. So fraud detection systems became essential for the banks to attenuate their loses. In this paper we have research about the various detecting techniques to identify and detect the fraud through varied techniques of data mining

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

scholarly journals Credit Card Rates and Consumer Switch: New Evidence

2016 ◽  
Vol 8 (12) ◽  
pp. 95
Author(s):  
Omar A. Abdelrahman
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Credit Card ◽  
Percent Level ◽  
Empirical Finding ◽  
Credit Cards ◽  
Credit Card Market ◽  
The Ohio State University ◽  
Using Data ◽  
Level Of Significance

This paper investigates the underlying determinants of consumer’s choices regarding switching credit-card balances. To estimate the likelihood that consumers switch credit cards, two logit models are estimated. Using data from the Consumer Finance Monthly (CFM) of The Ohio State University, the author finds that at the conventional 5 percent level of significance, the following variables have significance: old interest rate, new interest rate, duration of the introductory rate, balances, number of credit cards, homeownership, and age. As expected, interest rates, balances, the duration of new introductory offer rates, and homeownership have the greatest influence on why or why not people switch credit cards. The findings are consistent with the view that consumers make rational decisions in the credit card market, challenging Ausubel’s (1991) argument of credit card consumer irrationality and Calem and Mester’s (1995) empirical finding that credit card rates are sticky because consumers are irresponsive to rate cuts.

scholarly journals Credit Card Usage Among College Students in China

2018 ◽  
Vol 29 (2) ◽  
pp. 304-315 ◽  
Author(s):  
Rui Yao ◽  
Xiangyi Meng
Keyword(s):  
College Students ◽  
Credit Card ◽  
Credit Cards ◽  
Common Method ◽  
Financial Knowledge ◽  
Chinese College Students ◽  
Credit History ◽  
Using Data ◽  
High Level ◽  
Credit Card Usage

Credit cards have become a common method of payment for college students in China. It is important that they form good credit card usage behaviors and build a good credit history early in their financial life. Using data collected from 10 universities in China, results of this study found that being financially dependent on their parents is negatively associated with Chinese college students’ ability to pay their credit card bills. The study also found that students with a high level of financial knowledge were less likely to take cash advances on their credit card. Implications for financial educators and parents as well as policymakers were provided.

scholarly journals Fraudulent credit card transaction detection using soft computing techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 1634
Author(s):  
Aishwarya Priyadarshini ◽  
Sanhita Mishra ◽  
Debani Prasad Mishra ◽  
Surender Reddy Salkuti ◽  
Ramakanta Mohanty
Keyword(s):  
Soft Computing ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Detection System ◽  
Fraud Detection ◽  
K Nearest Neighbor ◽  
Computationally Efficient ◽  
Detection Techniques ◽  
Government Organizations ◽  
Soft Computing Techniques

<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>

Crop Disease Detection Using Data Science Techniques

2021 ◽  
pp. 80-97
Author(s):  
Shakti Kumar
Keyword(s):  
Data Science ◽  
Nearest Neighbor ◽  
Plant Diseases ◽  
K Nearest Neighbor ◽  
Classification Feature ◽  
Crop Disease ◽  
Using Data ◽  
Agricultural Yields ◽  
Day By Day

Plant disease is a mutilation of the normal state of a plant that changes its essential quality and prevents a plant from performing to its actual potential. Due to drastic environment changes, plant diseases are growing day by day, which results the higher losses in quantity of agricultural yields. To prevent the loss in the crop yield, the timely disease identification is necessary. Monitoring the plant diseases without any digital mean makes it difficult to identify the disease correctly and timely. It requires more amounts of work, time, and great experience in the plant diseases. Automatic approach of image processing and applying the different data science techniques to classify the disease correctly is a good idea for this which includes acquisition, classification, feature extraction, pre-processing, and segmentation all are performed on the leaf images. This chapter will briefly discuss the data science techniques used for the classification of the images like SVM, k-nearest neighbor, decision tree, ANN, and convolutional neural network (CNN).

Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards

2003 ◽  
Vol 93 (5) ◽  
pp. 1703-1729 ◽  
Author(s):  
Christopher R Knittel ◽  
Victor Stango
Keyword(s):  
Credit Card ◽  
Focal Point ◽  
Credit Cards ◽  
State Level ◽  
Tacit Collusion ◽  
Focal Points ◽  
National Integration ◽  
Credit Card Market ◽  
Using Data ◽  
Perverse Effect

We test whether a nonbinding price ceiling may serve as a focal point for tacit collusion, using data from the credit card market during the 1980’s. Our empirical model can distinguish instances when firms match a binding ceiling from instances when firms tacitly collude at a nonbinding ceiling. The results suggest that tacit collusion at nonbinding state-level ceilings was prevalent during the early 1980’s, but that national integration of the market reduced the sustainability of tacit collusion by the end of the decade. The results highlight a perverse effect of price regulation.

scholarly journals Data Mining Classification Of Filing Credit Customers Without Collateral With K-Nearest Neighbor Algorithm (Case study: PT. BPR Diori Double)

2020 ◽  
Vol 2 (2) ◽  
pp. 204-210
Author(s):  
Jeprianto Sinaga ◽  
Bosker Sinaga
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Savings And Loans ◽  
Long Time ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
The Many

Unsecured loans are the community's choice for lending to banks that provide Reviews These services. PT. RB Diori Ganda is a regional private banking company that serves savings and loans and loans without collateral for the community. Submission of unsecured loans must go through an assessor team to process the analysis of the attributes that Affect the customer's classification so that credit can be approved, the which is then submitted to the commissioner for credit approval. But what if Reviews those who apply for credit on the same day in large amounts, of course this will the make the process of credit analysis and approval will take a long time. If it is seen from the many needs of the community to apply for loans without collateral, a classification application is needed, in order to Facilitate the work of the assessor team in the process of analyzing the attributes that Affect customer classification. To find out the classification of customers who apply for unsecured loans for using data mining with the K-Nearest Neighbor algorithm. The result of this research is the classification of problematic or non-performing customers for credit applications without collateral.

scholarly journals Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

2020 ◽  
Vol 18 (1) ◽  
pp. 75
Author(s):  
M. Jupri ◽  
Riyanarto Sarno
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Fuzzy Ahp ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Time Required ◽  
Mining Algorithms

The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

Diffusion and Adoption of Credit Cards in Indian Banking Sector

2018 ◽  
Vol 1 (2) ◽  
pp. 1-20
Author(s):  
Dr. Mandeep Kaur ◽  
Dr.Kamalpreet Kaur
Keyword(s):  
Credit Card ◽  
Banking Sector ◽  
Credit Cards ◽  
Annual Reports ◽  
Banking Services ◽  
Time Period ◽  
Dichotomous Variable ◽  
Reserve Bank ◽  
The Status ◽  
Reserve Bank Of India

The study emphasizes on the identification of factors, which may have influenced the banks to adopt credit cards along with their traditional banking services. Bank specific variables were investigated to deepen the understanding on the diffusion and adoption of credit cards. The data relating to sampled banks’ characteristics have been collected from database of Reserve Bank of India. To know about the status of the bank regarding its adoption of credit card, the websites and annual reports of the banks were explored during different intervals of time period of the study. The study considers the dependent variable i.e. adoption of credit cards as dichotomous variable, whether or not a bank renders the credit card services, denoting 1 if the bank has adopted credit card otherwise 0. The logistic regression has thus been applied to get the valid and reliable results. The empirical findings reveal that, size, non-interest income, non performing assets, profitability, age and market share of the bank are the variables which have contributed significantly in the diffusion and adoption of credit cards.

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