Hybrid Data Mining For Member Determination And Financing Prediction  In Syariah Financing Saving And Loan Cooperatives

Ondra Eka Putra; Randy Permana

doi:10.29207/resti.v8i2.5683

Ondra Eka Putra Universitas Putra Indonesia YPTK Padang
Randy Permana Universitas Putra Indonesia YPTK Padang

DOI: https://doi.org/10.29207/resti.v8i2.5683

Keywords: KPPS, hybrid data mining, k-medoids, naïve bayes, k-nearest neighbor (k-NN)

Abstract

Syariah Financing Saving And Loan Cooperatives (KSPPS) is an Islamic financial institution aimed at people who are on the lower middle scale to lift the economy of small communities through microfinancing programs. Problems that often occur in member recommendations to get KSPPS financing are often not on target. In addition, The amount of member financing is often problematic due to a lack of analysis, resulting in poor financing instalments. This research aims to present an analysis model for clustering and classification using hybrid data mining algorithms. This research method is using hybrid data mining Algorithms, namely K-Medoids, Naïve Bayes, and k-Nearest Neighbors (k-NN). This study uses the historical dataset of the last two years on KSPPS BMT Dadok Tunggul Hitam as a total of 70 data samples. The analysis parameters consist of income, business, residence Status, financing application, billing history, and balance amount. The best analysis Model will be obtained by comparing the results between Naïve Bayes with K-Medoids, and K-Nearest Neighbor (k-NN) with K-Medoids. The results of this research showed the best performance is using the hybrid Naïve Bayes data mining model with K-Medoids which has an accuracy of 90.91% for data split 70:30, while performance with K-fold cross-validation shows an accuracy of 93.49% using this algorithm. Overall, the results of this study can provide an effective analysis model to determine the status of the loan.

Downloads

Download data is not yet available.

References

A. Riyani, G. Pratama, and S. Surahman, “Analisis Sistem Pengelolaan Keuangan Pembiayaan Syariah Dengan Akad Murabahah,” Ecobankers J. Econ. Bank., vol. 3, no. 1, p. 1, 2022, doi: 10.47453/ecobankers.v3i1.672.

R. Muhamad, “Kegiatan Usaha Bank Perkreditan Rakyat Ditinjau Dari Undang-Undang Nomor 10 Tahun 1998 Tentang Perbankan,” Lex Priv., vol. 8, no. 1, pp. 66–77, 2020.

I. Nurlaeli, “Analisis Akad Qardhul Hasan ( Studi Kasus di KSPPS BMT Mentari Bumi Purbalingga ),” Islam. J. Pemikir. Islam, vol. 23, no. 2, pp. 239–253, 2022.

A. Studies, “Implementasi Sistem Bagi Hasil dan Perlakuan Akuntansi pada Pembiayaan Mudharabah Di KSPPS BMT An-Nuur Jombang,” JFAS J. Financ. Account. Stud., vol. 3, pp. 55–71, 2021.

A. Nugroho, D. I. Astanti, and D. Septiandani, “PENYELESAIAN PEMBIAYAAN MACET DENGAN JAMINAN HAK TANGGUNGAN DI KOPERASI SIMPAN PINJAM DAN PEMBIAYAAN SYARIAH (KSPPS) HUDATAMA CABANG SEMARANG BARAT,” Semarang Law Rev., vol. 1, no. 1, pp. 46–58, 2020.

K. F. Hana and E. A. Chodlir, “ELABORASI ANALISIS PEMBIAYAAN DALAM MEMINIMALISIR NON PERFORMING FINANCE ( NPF ) PADA LEMBAGA KEUANGAN SYARIAH,” MALIA J. Islam. Bank. Financ., vol. 5, no. 2, pp. 121–132, 2021.

S. Arlis et al., “POLA PENENTUAN STATUS PEMINJAMAN DENGAN ALGORITMA PERCEPTRON,” SEBATIK, pp. 619–623, 2018.

A. D. Mining and I. Pendahuluan, “Penerapan Metode Principal Component Analysis ( PCA ) Pada Klasifikasi Status Kredit Nasabah Bank Sumsel Babel Cabang KM 12 Palembang Menggunakan Metode Decision Tree,” Generic, 2022.

T. A. Putra, P. Ayu, W. Purnama, R. Afira, and Y. Elva, “Optimization Analysis Model Determining PNMP Mandiri Loan Status Based Based on Pearson Correlation,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 158, pp. 5–12, 2022.

S. Nasional, T. Elektro, S. Informasi, and T. Informatika, “Klasifikasi Status Pinjaman Calon Nasabah Koperasi Simpan Pinjam Menggunakan Metode Bayesian Network (Studi Kasus: Koperasi Simpan Pinjam BTM Nasyiah 1 Bojonegoro),” SNESTIK -Seminar Nas. Tek. Elektro, Sist. Informasi, dan Tek. Inform., pp. 409–414, 2022.

C. Fadlan, S. Ningsih2, and A. P. Windarto, “PENERAPAN METODE NAÏVE BAYES DALAM KLASIFIKASI KELAYAKAN KELUARGA PENERIMA BERAS RASTRA,” JUTIM, vol. 3, no. 1, pp. 1–8, 2018.

A. Swasono, “Analisis Faktor - Faktor Penentu Penerimaan Data Kredit Pada Nasabah Koperasi KSPPS BMT Lampung Tengah Menggunakan Gradient Descent,” J. Ilmu Data, vol. 2, no. 10, pp. 1–9, 2022.

A. Gumilar, S. S. Prasetiyowati, and Y. Sibaroni, “Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 158, pp. 481–490, 2022.

S. Simsek and A. Dag, “A Hybrid Data Mining Approach for Identifying the Temporal Effects of Variables Associated with Breast Cancer Survival,” Dep. Inf. Manag. Bus. Anal. Fac. Scholarsh. Creat. Work., 2020.

M. T. Sattari, A. Avram, and H. Apaydin, “Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models,” Mathematics, 2020.

K. Khosravi, Z. Sheikh, and J. R. Cooper, “Predicting stable gravel-bed river hydraulic geometry : A test of novel , advanced , hybrid data mining algorithms,” Environ. Model. Softw., vol. 144, no. August, p. 105165, 2021, doi: 10.1016/j.envsoft.2021.105165.

T. S. Kumar, “Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm,” J. Artif. Intell. Capsul. Networks, vol. 02, no. 03, pp. 185–193, 2020.

K. Penjualan, D. E. Putri, E. Praja, and W. Mandala, “Hybrid Data Mining berdasarkan Klasterisasi Produk untuk,” J. KomtekInfo, vol. 9, pp. 68–73, 2022, doi: 10.35134/komtekinfo.v9i2.279.

I. Riadi, A. Yudhana, and M. R. Djou, “Optimization of Population Document Services in Villages using Naive Bayes and k-NN Method,” Int. J. Comput. Digit. Syst., vol. 1, no. 1, 2024.

D. Marlina, N. F. Putri, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” J. CoreIT, vol. 4, no. 2, pp. 64–71, 2018.

R. Sistem, A. Wibowo, M. Makruf, I. Virdyna, and F. C. Venna, “Penentuan Klaster Koridor TransJakarta dengan Metode Majority Voting,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 565–575, 2021.

S. Harlina, “DATA MINING ON CREDIT FEASIBILITY DETERMINATION USING K-NN ALGORITHM BASED ON FORWARD SELECTION,” Raharja Open J. Syst., vol. 11, no. 2, pp. 236–244, 2018.

S. Harlina and M. O. Kadang, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi kelayakan Calon Nasabah Kredit Berbasis Web,” Pros. Semin. Nas. Tek. Elektro, Inform. Sist. Inf., 2022.

R. F. Putra and I. D. Ratih, “Comparison of K-Nearest Neighbor , Naive Bayes Classifier , Decision Tree , and Logistic Regression in Classification of Non- Performing Financing,” Int. J. Adv. Sci. Comput. Appl., vol. 2, pp. 69–76, 2023, doi: 10.47679/ijasca.v2i2.35.

N. Nurajijah and D. Riana, “Algoritma Naïve Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah,” J. Teknol. dan Sist. Komput., vol. 7, no. 2, pp. 77–82, 2019, doi: 10.14710/jtsiskom.7.2.2019.77-82.

Nurajijah, F. Amsury, I. Saputra, Frieyadie, D. N. Sulistyowati, and B. Rifai, “Approval of Sharia Cooperative Customer Financing Using PSO-Based SVM Classification Algorithm,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012047.

D. Priyanto, A. Robbiul, and D. Jollyta, “Naïve Bayes and K-Nearest Neighbor Approaches in Data Mining Classification of Drugs Addictive Diseases,” Ilk. J. Ilm., vol. 15, no. 2, pp. 262–270, 2023.

A. Martono and G. Maulani, “The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 patients in Indonesia,” Ilk. JurnalIlmiah, vol. 15, no. 2, pp. 240–249, 2023.

C. Fu, M. Quintana, Z. Nagy, and C. Miller, “Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation,” Appl. Therm. Eng., 2023.

E. Setiawati, U. D. Fernanda, and S. Agesti, “Implementation of K-Means , K-Medoid and DBSCAN Algorithms In Obesity Data Clustering,” IJATIS Indones. J. Appl. Technol. Innov. Sci., vol. 1, no. February, pp. 23–29, 2024.

R. Mutia and J. A. Ariani, “Performance Comparison K-Nearest Neighbor , Naive Bayes , and Decision Tree Algorithms for Netflix Rating Classification,” IJATIS Indones. J. Appl. Technol. Innov. Sci., vol. 1, no. February, pp. 16–22, 2024.

G. Varone, W. Boulila, M. Driss, S. Kumari, and M. Khurram, “Finger pinching and imagination classification : A fusion of CNN architectures for IoMT-enabled BCI applications,” Inf. Fusion J., vol. 101, no. May 2023, 2024.