Implementasi Data Mining Untuk Menentukan Kelayakan Pemberian Kredit Dengan Menggunakan Algoritma  K-Nearest Neighbors (K-Nn)

Muryono, Tupan Tri; Irwansyah, Irwansyah

doi:10.37365/jti.v6i1.78

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Penelitian lainnya yang dilakukan oleh Dita Noviana, dkk pada tahun 2019 didapatkan hasil bahwa algoritma K-NN menunjukkan akurasi dan eror algoritma k-NN sebesar 90.7% dan 9.3% dimana hasil tersebut merupakan akurasi terbesar dibandingkan dengan algoritma lainnya [8]. Dan pada tahun 2020 dilakukan penelitian oleh Tupan Tri Muryono dan Irwansyah dengan hasil penelitian dari hasil evaluasi dan validasi menggunakan k-5 fold yang telah dilakukan menggunakan tools RapidMiner diperoleh hasil akurasi tertinggi dari Metode K-Nearest Neighbor (K-NN) sebesar 93.33% [9].…”

Section: Pendahuluanunclassified

Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN

Whendasmoro

Joseph

2022

Jur. Ris. Kom.

View full text Add to dashboard Cite

The large volume of information in the data causes a lot of data to be stored in the dataset. The dataset consists of various attributes and attribute values which contain information stored in the dataset. Data mining is a process that can be used to search for information on datasets. However, the problems encountered in the dataset are often found to have abnormal data such as the range of values that are too far and different between dataset attributes. The value range that is too far causes the results of the information obtained to be not optimal, in data mining itself the process or results are good based on the quality of the data stored in the dataset. Data normalization is a preprocessing stage, where data normalization is scaled back to the range of values in the attribute. Z-Score Normalization is a statistical technique that can be used in data mining to preprocess data by performing data transformations. Z-Score Normalization can be combined with data mining classification techniques, where the role of Z-Score Normalization is to normalize data which is useful for improving the performance of data mining classification algorithms, especially the K-NN algorithm in this study. The results of the study show that Z-Score Normalization is useful for improving performance than the K-NN algorithm. This can be seen from the increase in the accuracy value obtained from the K-NN process before normalizing the dataset and after normalizing the dataset. The accuracy values respectively before normalizing the dataset were 95.13%, 95.83%, 96.11%, 95.77% and 95.81% after normalizing the dataset there was an increase in the accuracy value, namely 97.87%, 98, 57%, 98.77%, 97.23% and 98.11%.

show abstract

Section: Pendahuluanunclassified

Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN

Whendasmoro

Joseph

2022

Jur. Ris. Kom.

View full text Add to dashboard Cite

show abstract

“…There are several important elements in the provision of a credit facility, namely Trust, Agreement, Term, Risk and Rewards [18]. To find out how to provide credit to customers based on good credit quality, it is necessary to accurately predict the capability of customer credit payments as a reference for management in making decisions to improve credit quality and collectability [19].…”

Section: Collectibility Of Creditmentioning

confidence: 99%

Application of Information Gain to Select Attributes in Improving Naïve Bayes Accuracy in Predicting Customer's Payment Capability

Herfandi¹,

Zaen²,

Yuliadi³

et al. 2021

JISA

View full text Add to dashboard Cite

The customer is the main factor in the running of PT. XYZ. A good understanding of customers is very important for predicting the capability of customers to pay. The implementation of credit collectibility is used to determine the quality of customer credit, one of which is the customer's capability to pay interest and principal on time. While manually, it is very difficult to accurately predict the capability of customer credit payments. Data mining techniques with the Naïve Bayes algorithm were chosen to classify customers to be able to find patterns, analyze and predict, because they have good performance, are efficient, and simple. The Naïve Bayes algorithm has a weakness in terms of sensitivity to many attributes, so the accuracy is low. Based on the problem stated, his study will apply the Information Gain method to select the most influential attribute on the label in order to increase the accuracy of the Naïve Bayes algorithm. This research produces a new dataset with seven attributes: TENOR, SALARY, DOWN PAYMENT, INSTALLMENT, APPROVAL, OTR CLASS, AGE with Labels: Status and Id: Id number based on the Information Gain method. The dataset comparison process with 995 data records showed an increase in accuracy, precision, and AUC using the new dataset compared to the old dataset, but in the t-Test test with an alpha value = 0.05 there is a difference but not significant. In the evaluation process, performance experienced a significant increase in the use of new datasets with the following percentages of performance improvement: accuracy = 8%, precision = 18.42%, recall = 17.65% and AUC= 0.057%. The results of this study obtained AUC of 0.876, accuracy of 87.88%, precision of 61.90%, and recall of 76.47%, and classified into good classification.

show abstract

Implementasi Data Mining Untuk Menentukan Kelayakan Pemberian Kredit Dengan Menggunakan Algoritma K-Nearest Neighbors (K-Nn)

Cited by 2 publications

References 0 publications

Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN

Analisis Penerapan Normalisasi Data Dengan Menggunakan Z-Score Pada Kinerja Algoritma K-NN

Application of Information Gain to Select Attributes in Improving Naïve Bayes Accuracy in Predicting Customer's Payment Capability

Contact Info

Product

Resources

About