Prediksi Kelancaran Pembayaran Cicilan  Calon Debitur Dengan Metode K-Nearest Neighbor

Rizky, Sri Ayu; Yesputra, Rolly; Santoso, Santoso

doi:10.33330/jurteksi.v7i2.1078

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The cooperative, as the lender, will conduct a survey of prospective credit recipients through the 5C analysis (character, capacity, capital, economic conditions, and collateral) to minimize the risk of non-performing loans happening [2]. Data mining can assist cooperatives in analyzing the credit recipients' potential for non-performing loans by comparing the previous credit granting data with the survey data of prospective credit recipients and classifying them in the form of bad credit or non-bad credit classifications [3]. With the selection, exploration, and modeling of previous data, data mining can find knowledge in the form of relationships between one feature and another previously unknown feature [4].…”

Section: Imentioning

confidence: 99%

The Factors that Affect Customer Ratings in E-Commerce Company

Magdalena,

Raharjo,

Sensuse

2023

ICoSTEC

View full text Add to dashboard Cite

In developing the e-commerce business in Indonesia, e-commerce companies must maintain customer satisfaction. In the e-commerce company, customer satisfaction can see from the ratings given by consumers. This research aims to find factors influencing consumers in providing ratings to improve services. The company’s current condition does not know the indicators when consumers give ratings. The current situation is decreasing order because the number of bad rating is higher and impact to profit company. This research uses several methods, such as the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology Acceptance Model (TAM), and product quality that influences ratings. The target respondents are consumers who have given bad ratings and have never been given a rating, with 326 respondents. The statistical analysis processing used was Partial Least Square (PLS) with SmartPLS v.4 tools. The results of the research concluded that the factors that influence bad rating respondents in giving ratings with significant positive results are the factors of perceived ease of use, social influence, and customer satisfaction, with an r-square rating value of 0.354 (moderate). From moderation, the variable with the most significant positive effect is the female gender. Meanwhile, respondents who have never been given a rating that has a significant positive impact are performance expectancy, social influence, and customer satisfaction, with an r-square value for ratings of 0.823 (strong). The results of this research concluded that there are several improvements to improve service, namely improving application algorithms, developing operational teams, and several strategies to overcome the impact of the covid pandemic.

show abstract

Section: Imentioning

confidence: 99%

The Factors that Affect Customer Ratings in E-Commerce Company

Magdalena,

Raharjo,

Sensuse

2023

ICoSTEC

View full text Add to dashboard Cite

show abstract

“…Penelitian yang juga dilakukan oleh Sri dan Rolly, dkk untuk memprediksi kelancaran pembayaran cicilan calon debitur pada koperasi simpan pinjam dengan 6 kriteria yaitu nomor pinjaman, jenis kelamin, status pernikahan, jenis usaha, jumlah pinjaman dan jenis pinjaman. Tetapi pada penelitian ini metode yang digunakan ialah metode K-NN dan menghasilkan nilai accuracy sebesar 73,37% [11]. Dengan menerapkan metode naive bayes pada penelitian ini diharapkan mampu menghasilkan nilai akurasi yang lebih baik lagi dibandingkan penelitian sebelumnya.…”

Section: A Pendahuluanunclassified

Prediksi Kelancaran Pembayaran Angsuran Pada Koperasi Dengan Metode Naive Bayes Classifier

Suwati

Yesputra

Sapta

2022

ijcs

View full text Add to dashboard Cite

This study aims to predict the smoothness of installment payments in cooperatives, making it easier for staff to analyze credit lending. Lack of prudence in analyzing credit results in customers who are in arrears in paying installments, resulting in bad credit. To minimize errors that exist, it is necessary to evaluate the provision of loans to prospective debtors. By utilizing past member criteria data in the past that will be used to predict smooth payments using data mining. The data mining technique used is the Naive Bayes classifier method. The prediction process uses the naive Bayes method, namely by determining the probability or opportunity based on the previous member's data, and the results are used to help make a decision. The criteria used are member data: employment, income, house status, number of credits, and type of credit. Based on the naive Bayes method, the results obtained are 90.00% accuracy, 0.880% AUC, 83,33% recall, and 100% precision.

show abstract

“…Data mining merupakan salah satu bidang ilmu komputer yang dapat digunakan untuk memecahkan masalah prediksi, seperti klasifikasi dan regresi [2], [3]. Data mining dapat digunakan untuk menganalisis kredit macet calon penerima kredit dengan cara membandingkan data lama (data pemberian kredit sebelumnya) dengan data baru (data hasil survei calon penerima kredit) dan mengelompokkan nya dalam bentuk klasifikasi kredit macet atau kredit tidak macet [4].…”

Section: Pendahuluanunclassified

Comparison of Adam's Optimization Function and Stochastic Gradient Descent on Bad Credit Classification of Savings and Loan Cooperatives Using Multilayer Perceptron

Samudra¹,

Hayadi²

2022

Com, Engine, Sys, Sci

View full text Add to dashboard Cite

Dengan pesatnya pertumbuhan koperasi di Indonesia, maka pemberian kredit bagi anggota koperasi semakin diperketat dengan melakukan survey terhadap calon penerima kredit melalui analisis 5C yaitu character, capacity, capital, condition of economic dan economic condition. agunan, sehingga risiko kredit macet dapat diminimalkan. Data mining dapat membantu koperasi dalam menganalisis kredit macet calon penerima kredit dengan cara membandingkan data lama (data pemberian kredit sebelumnya) dengan data baru (data survei calon penerima kredit) dan mengelompokkannya dalam bentuk klasifikasi kredit macet atau non-kredit. melakukan pinjaman. Penelitian ini membangun model klasifikasi kredit macet berdasarkan hasil analisis calon penerima kredit menggunakan algoritma multi layer perceptron. Berbagai fungsi stochastic gradient descent (SGD) dan optimasi Adam digunakan yang dievaluasi menggunakan validasi silang 5 kali lipat, 10 kali lipat, dan 20 kali lipat. Hasil yang diperoleh adalah optimasi Adam merupakan fungsi optimasi terbaik untuk mengklasifikasikan dataset kredit macet, hal ini dapat dilihat dari nilai akurasi sebesar 95,6%, nilai F1 sebesar 95,6%, nilai presisi sebesar 95,7%, dan nilai recall sebesar 95,6%.

show abstract

Prediksi Kelancaran Pembayaran Cicilan Calon Debitur Dengan Metode K-Nearest Neighbor

Cited by 3 publications

References 0 publications

The Factors that Affect Customer Ratings in E-Commerce Company

The Factors that Affect Customer Ratings in E-Commerce Company

Prediksi Kelancaran Pembayaran Angsuran Pada Koperasi Dengan Metode Naive Bayes Classifier

Comparison of Adam's Optimization Function and Stochastic Gradient Descent on Bad Credit Classification of Savings and Loan Cooperatives Using Multilayer Perceptron

Contact Info

Product

Resources

About