PREDICTION OF COOPERATIVE LOAN FEASIBILITY USING THE K-NEAREST NEIGHBOR ALGORITHM

  • Roviani Roviani (1) Universitas Bina Sarana Informatika
  • Deddy Supriadi (2) Universitas Bina Sarana Informatika
  • Iqbal Dzulfiqar Iskandar (3*) Universitas Bina Sarana Informatika Kampus Tasikmalaya

  • (*) Corresponding Author
Keywords: Accuracy, Creditworthiness, Data mining classification, K-Nearest Neighbor, ROC Curve Split Validation

Abstract

Approval of credit lending to cooperative members without proper feasibility analysis can cause credit problems, cooperatives such as late payment of installments, and an increase in bad credit which can threaten the survival of the cooperative as a provider of lending services. As a solution to minimize the creditworthiness assessment errors for loan funds, research is carried out to analyze the feasibility of loan funds from the data of cooperative members using the data mining method approach and the algorithm used using the K-Nearest Neighbor. The purpose of this research is to predict the feasibility of granting credit with the right decision and to find out the level of evaluation, accuracy, and validation of the effectiveness of the k-NN algorithm on processing creditworthiness application data classifications. After the prediction research was carried out, the data on the eligibility of credit lending applications were conducted at the Bakti Berkah Sukaraja Savings and Loan Cooperative, The data obtained from the accuracy value of the k-nearest neighbor algorithm before being validated has an accuracy of 87.78% with AUC 0.95, after validation with split validation the accuracy decreased slightly by 2% to be 85.71%, while the AUC value in the ROC Curve was 0.836%. Even though there was a decline, it can still be categorized as a good classification. The impact of this research is that besides the accuracy of the k-NN algorithm being validated, the Bakti Berkah Sukaraja Savings and Loan cooperative can predict the feasibility of applying for credit funds, as an effort to reduce the threat of bad credit risk

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Author Biographies

Deddy Supriadi, Universitas Bina Sarana Informatika

Program Studi Sistem Informasi
Fakultas Teknik dan Informatika, Universitas Bina Sarana Informatika Kampus Tasikmalaya, Indonesia

Iqbal Dzulfiqar Iskandar, Universitas Bina Sarana Informatika Kampus Tasikmalaya

Iqbal Dzulfiqar Iskandar, lahir di tasikmalaya 13 oktober 1991, s1 universitas siliwangi, dan melanjutkan jenjang pendidikan S2 di STMIK Magister ilmu Komputer Nusa Mandiri jakarta, bidang ke ahlian data mining. sekarang sebagai Dosen pengajar di BSI Tasikmalaya - Bidang keahlian Sistem Teknologi Informasi, Jaringan, Data mining - Jaringan Komputer - Familiar dengan Sistem Operasi clear OS, Ms. Windows 98, Windows 2000, Ms. Windows XP, Windows7 dan Windows 8. - Mampu mengoperasikan komputer, merakit/memperbaiki PC dan menginstall system operasi komputer. - Mampu installasi jaringan computer local area. - Mampu merancang kebutuhan system dan membuat perangkat lunak. - Mampu menggunakan aplikasi Microsoft Office, Software Pembangun Sistem : Turbo Pascal, C++, Visual Basic 6.0, Macromedia Dreamweaver, Adobe Dreamweaver, Java NetBeans, Flash, Packet tracer. - Software Desain Grafis : Macromedia Fireworks MX, Adobe Photoshop, Corel Draw. - Konfigurasi jaringan computer. - Manajemen Database : Microsoft Access dan MySQL - Mampu menggunakan Internet, Browsing, Email dll.

References

Ariadi, F. (2020). Analisa Perbandingan Algoritma DT C . 45 dan Naïve Bayes Dalam Prediksi Penerimaan Kredit Motor. Jurnal Riset Inovasi Bidang Informatika Dan Pendidikan Informatika (KERNEL), 1(1), 1–7.

Bahar, H. (2020). Strategi Penyelesaian Kredit Macet Dan Dampak Terhadap Kinerja Keuangan Pada PT Bank Sulselbar Cabang Barru. DECISION Jurnal Ekonomi Dan Bisnis, 1(2), 178–186.

Bazan, J., Bazan-Socha, S., Ochab, M., Buregwa-Czuma, S., Nowakowski, T., & Woźniak, M. (2020). Effective construction of classifiers with the k-NN method supported by a concept ontology. Knowledge and Information Systems, 62(4), 1497–1510. https://doi.org/10.1007/s10115-019-01391-w

Bode, A. (2017). K-Nearest Neighbor Dengan Feature Selection Menggunakan Backward Elimination Untuk Prediksi Harga Komoditi Kopi Arabika. ILKOM Jurnal Ilmiah, 9(2), 188–195. http://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/139

Bramer, M. (2020). Principles of data mining fourth edition. In Drug Safety (Vol. 30, Issue 7). https://doi.org/10.1007/978-1-4471-7493-6

Deshpande, V. K. (2015). Predictive Analytics and Data Mining. Elsevier Inc.

Edy Nasri, A. S. A. (2020). PENJUALAN BARANG SECARA KREDIT DENGAN ALGORITMA K-NEAREST NEIGHBOR. Jurnal Sains & Teknologi, 4(1), 1–11.

Fatah, H., & Subekti, A. (2018). Prediksi Harga Cryptocurrency Dengan Metode K-Nearest Neighbours. Jurnal PILAR Nusa Mandiri, 14(2), 137–144. https://doi.org/10.33480/pilar.v14i2.30

Gani, A., & Fandorann, U. (2020). Analisis Tingkat Kredit Macet Bumdes Tunas Harapan Desa Simpang Campang Kecamatan Kisam Ilir Kabupaten Oku Selatan. JETAP Jurnal Akuntansi Dan Bisnis, 53(9), 1689–1699. http://journal.unbara.ac.id/index.php/etap/article/view/550

Gubernur Bank Indonesia. (2013). Peraturan Bank Indonesia Nomor 15/2/Pbi/2013 Tentang Penetapan Status Dan Tindak Lanjut Pengawasan Bank Umum Konvensional (pp. 1–41). BANK INDONESIA.

Hartini, S., & Kurahman, T. (2020). Sistem Pakar Pemilihan Calon Debitur Kredit Motor Dengan Algoritma C4 . 5 Pada PT . Federal International Finance. Information System For Educators And Professionals, 5(1), 51–60. http://journal.unbara.ac.id/index.php/etap/article/view/550

I D Iskandar, N Ch Basjaruddin, D Supriadi, Ratningsih, D S Purnia, and T. W. (2020). Popular Content Prediction Based on Web Visitor Data With Data Mining Approach Popular Content Prediction Based on Web Visitor Data With Data Mining Approach. Journal of Physics: Conference Series, 1–7. https://doi.org/10.1088/1742-6596/1641/1/012105

Khajenezhad, A., Bashiri, M. A., & Beigy, H. (2021). A distributed density estimation algorithm and its application to naive Bayes classification. Applied Soft Computing, 98, 106837. https://doi.org/10.1016/j.asoc.2020.106837

Kumar, A., Bhatnagar, R., & Srivastava, S. (2018). Analysis of Credit Risk Prediction Using ARSkNN. Advances in Intelligent Systems and Computing, 723(March 2016), 644–652. https://doi.org/10.1007/978-3-319-74690-6_63

Mardhiyah, P. A., Siregar, R. R. A., & Palupiningsih, P. (2020). Klasifikasi Untuk Memprediksi Pembayaran Kartu Kredit Macet Menggunakan Algoritma C4.5. Jurnal Teknologia, 3(1), 91–101. https://aperti.e-journal.id/teknologia/article/view/66

Miharja, J., Putra, J. L., & Hadianto, N. (2020). Comparison Of Machine Learning Classification Algorithm On Hotel Review Sentiment Analysis (Case Study : Luminor Hotel Pecenongan). Jurnal Pilar Nusa Mandiri, 16(1), 59–64. https://doi.org/10.33480/pilar.v16i1.1131

Nurhasan, F., Hikmah, N., & Utami, D. Y. (2018). Perbandingan Algoritma C4.5, KNN, Dan Naive Bayes Untuk Penentuan Model Klasifikasi Penanggung Jawab BSI Entrepreneur Center. Jurnal Pilar Nusa Mandiri, 14(2), 169–174.

Nuryaman, Y. (2018). Komparasi Klasifikasi Penentuan Customer Kredit Menggunakan Algoritma C4.5 dan KNN Pada PT. Citra Semesta Energy. Jurnal Pilar Nusa Mandiri, 14(2), 233–238.

Pahlevi, O. (2020). Data Mining Model for Designing Diagnostic Applications Inflammatory Liver Disease. Jurnal Dan Penelitian Teknik Informatika, 5(1), 51–57. https://doi.org/https://doi.org/10.33395/sinkron.v5i1.10589 e-ISSN

Pérez-Martín, A., Pérez-Torregrosa, A., & Vaca, M. (2018). Big Data techniques to measure credit banking risk in home equity loans. Journal of Business Research, 89(February), 448–454. https://doi.org/10.1016/j.jbusres.2018.02.008

Permana, T., Siregar, A. M., Masruriyah, A. F. N., & Juwita, A. R. (2020). Perbandingan Hasil Prediksi Kredit Macet Pada Koperasi. Conference on Innovation and Application of Science and Technology, 3(1), 737–746. http://publishing-widyagama.ac.id/ejournal-v2/index.php/ciastech/article/view/1970

Pham, B. T., Jaafari, A., Avand, M., Al-Ansari, N., Du, T. D., Hai Yen, H. P., Phong, T. Van, Nguyen, D. H., Van Le, H., Mafi-Gholami, D., Prakash, I., Thuy, H. T., & Tuyen, T. T. (2020). Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry, 12(6), 1–21. https://doi.org/10.3390/SYM12061022

Pratama, A. A. S., & Purwanto, I. W. N. (2018). Upaya Restrukturisasi Kredit Bermasalah di PT. Bank Pembangunan Daerah Cabang Gianyar. Kertha Semaya: Journal Ilmu Hukum, 6(4), 1–15.

Roviani, R., Supriadi, D., & Iskandar, I. D. (2021). Laporan Akhir Penelitian Mandiri: Prediction Of Feasibility Of Cooperative Loans Using K-Nearest Neighbor Algorithm.

Simanjuntak, Daulat Freddy, Keri Boru Hotang, A. R. (2021). Pelatihan penyusunan laporan keuangan koperasi. Jurnal Kewirausahaan, Akuntansi, Dan Manajemen TRI BISNIS, 3(1), 66–75.

Wang, L. (2019). Research and Implementation of Machine Learning Classifier Based on Research and Implementation of Machine Learning Classifier Based on KNN. IOP Conference Series: Materials Science and Engineering PAPER, 1–5. https://doi.org/10.1088/1757-899X/677/5/052038

Yogiek Indra Kurniawan, T. I. B. (2020). Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan. Jurnal Ilmiah Matrik Universitas Bina Darma, 22(1), 73–82. https://doi.org/https://doi.org/10.33557/jurnalmatrik.v22i1.843

Zahra, N. L. (2020). Penilaian Tingkat Kesehatan Koperasi Pada Koperasi Simpan Pinjam Mitra Sukses Lestari Malang. Jurnal Ilmiah Mahasiswa Fakultas Ekonomi Dan Bisnis, 9(2), 1–20.

Published
2021-03-02
How to Cite
Roviani, R., Supriadi, D., & Iskandar, I. (2021). PREDICTION OF COOPERATIVE LOAN FEASIBILITY USING THE K-NEAREST NEIGHBOR ALGORITHM. Jurnal Pilar Nusa Mandiri, 17(1), 39-46. https://doi.org/10.33480/pilar.v17i1.2183
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