Analisis Kelayakan Pemberian Kredit Nasabah Koperasi Menggunakan Algoritma C4.5

Setiawan, Rudy

doi:10.36805/technoxplore.v5i2.1175

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…Hasil klasifikasi menggunakan Algoritma C4.5 menunjukan bahwa diperoleh akurasi 97,5%, berdasarkan hasil yang diperoleh menunjukan bahwa algoritma C4.5 cocok digunakan untuk menentukan kelayakan pemberian kredit nasabah pada KOPERIA (Santoso and Sekardiana, 2019). Hasil analisis menunjukkan Area Under Curve yang optimis sebesar 0.971 ini menunjukkan hasil klasifikasi berada pada kategori sangat baik (Setiawan, 2020).…”

Section: Pendahuluanunclassified

Application of C4.5 Algorithm for Late Payment Classification of Insurance Premiums

Karlia¹,

Nurmansyah²

2021

tekinfo

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The problem that often arises in insurance companies is the number of customers who do not smoothly pay premiums. The procedure that applies to the insurance during the grace period is 30 days. The insured customer must follow the premium payment procedure, if the customer does not pay the premium, the insurance policy will be canceled, this is part of the company's loss. An insurance company has a lot of data and this data can be processed to produce information on how to find out potential customer delays from a pattern formed using the C4.5 method. This research was conducted by applying the C4.5 algorithm using insurance customer data. The results of this study are a classification system for late payment of insurance premiums that can classify insurance customer premium payment status as a consideration for accepting insurance customers. The system test results show that the system can classify the status of insurance customer premium payments with a classification accuracy of 88%. Keywords: Algorithm C 4.5, Insurance, Classification, Premium

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Section: Pendahuluanunclassified

Application of C4.5 Algorithm for Late Payment Classification of Insurance Premiums

Karlia¹,

Nurmansyah²

2021

tekinfo

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“…Researchers [2], [18] used decision tree techniques and Support Vector Machine to process the German credit dataset without oversampling. While researchers [4], [19] used a decision tree algorithm to evaluate credit using primary data, with fewer attributes and records compared to the German Credit Dataset. The results of these studies show significant differences.…”

Section: Introductionmentioning

confidence: 99%

“…The results of these studies show significant differences. Researchers [2], [18] produced fair models, while researchers [4], [19] produced very good models. The use of other primary data for credit evaluation was also carried out by researcher [3], who used Tsukamoto fuzzy to evaluate credit in a company.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is still possible to conduct research using the same method with different data sets. Significant differences in credit scoring model results among researchers [2], [4], [18], [19] also motivated researchers to conduct a study on the application of SMOTE to the German credit dataset. The aim of this study is to develop an optimal model for performing credit scoring on the German Credit Dataset using SMOTE, KNN, and decision trees.…”

Section: Introductionmentioning

confidence: 99%

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Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation

Priyanto,

Sela,

Latumakulita

et al. 2023

Ilk. J. Ilm.

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Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset. Another challenge is developing an optimal model for evaluating debtor eligibility. Based on these challenges, this study aims to develop an optimal model for evaluating debtor eligibility on the German Credit Dataset, using the decision trees, k-Nearest Neighbor (k-NN) and Synthetic Minority Oversampling Technique (SMOTE). SMOTE and k-NN is used to overcome challenges regarding imbalanced datasets. While the decision tree are applied to produce a debtor classification model. In general, the steps taken are preparing datasets, pre-processing data, dividing datasets, oversampling with SMOTE, and classification models using decision trees, and testing. Model performance evaluation is represented by accuracy values obtained from the confusion matrix and area under curve (AUC) values generated by the Receiver Operating Characteristic (ROC). Based on the tests that have been carried out, the best accuracy value in the test is obtained at 73.00% and the AUC value is 0.708, in parameters k = 3 and Max-Depth = 25. Based on the analysis produced, the proposed model can improve performance compared to if the dataset is not applied SMOTE.

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“…Untuk membantu pengelola dalam mengantisipasi kredit macet, pada penelitian ini akan dilakukan prediksi dengan melakukan simulasi terkait kelancaran anggota koperasi dalam membayar iuran pinjaman. Atribut yang digunakan dalam penelitian ini yaitu pendapatan, tanggungan, jenis usaha, jangka waktu, kepemilikan rumah dan status pembayara dan hasil akurasi dari ujicoba yang dilakukan pada penelitian ini sebesar 73.42% [5].…”

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Penggunaan Algoritma C4.5 Untuk Penentuan Pemberian Pinjaman Pada Anggota Koperasi

Arianto¹,

Jananto²

2022

JATISI

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Koperasi adalah lembaga keuangan dengan kegiatan usaha menerima simpanan dan memberikan pinjaman uang kepada anggotanya. Koperasi Simpan Pinjam Bhina Raharja Purbalingga merupakan koperasi yang bergerak dibidang usaha simpan pinjam yang berdiri sejak tahun 1994 dan telah memperoleh persetujuan Badan Hukum Nasional, dengan nomor badan hukum 12198/BH/Vi/1994. Kegiatan terhadap permohonan pemberian pinjaman di koperasi merupakan hal yang paling penting dilakukan agar tidak terjadi penunggakan pembayaran angsuran dikemudian hari oleh para anggota, hasil analisa kelayakan pemberian pinjaman menjadi penentu atas permohonan pinjaman yang diajukan. Pada penelitian ini menggunakan algoritma c4.5 untuk analisa kelayakan pemberian pinjaman pada anggota. Hasil analisa menunjukkan atribut penghasilan merupakan atribut dengan nilai tertinggi dengan tingkat akurasi Confusion Matrix sebesar 85.34%.

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Analisis Kelayakan Pemberian Kredit Nasabah Koperasi Menggunakan Algoritma C4.5

Cited by 3 publications

References 7 publications

Application of C4.5 Algorithm for Late Payment Classification of Insurance Premiums

Application of C4.5 Algorithm for Late Payment Classification of Insurance Premiums

Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation

Penggunaan Algoritma C4.5 Untuk Penentuan Pemberian Pinjaman Pada Anggota Koperasi

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