Optimization Analysis Model Determining PNMP Mandiri Loan Status Based on Pearson Correlation

Teri Ade Putra; Pradani Ayu Widya Purnama; Riandana  Afira; Yesri Elva

doi:10.29207/resti.v6i6.4469

Teri Ade Putra Universitas Putra Indonesia YPTK Padang
Pradani Ayu Widya Purnama Universitas Putra Indonesia YPTK Padang
Riandana Afira Universitas Putra Indonesia YPTK Padang
Yesri Elva Universitas Putra Indonesia YPTK Padang

DOI: https://doi.org/10.29207/resti.v6i6.4469

Keywords: Classification Analysis, Loan Status, PNPM Mandiri, Artificial Neural Networks, Person Correlation

Abstract

PNPM Mandiri is an organization engaged in financing small and medium enterprises in the community. The problem that always occurs is an error in determining the loan status resulting in bad credit. This study aims to present a classification analysis model for determining loan status at PNPM Mandiri. The classification analysis model was built using the Perceptron algorithm artificial neural network. The analysis model will later be optimized using the Person Correlation (PC) method to measure the accuracy of the variables used. The research dataset is based on historical data from the last 2 years as many as 67 data samples. The analysis variables consist of Business Type (X1), Loan Amount (X2), Collateral (X3), Income (X4), and Expenses (X5). The results of the analysis show that the model built can provide optimal classification results. These results can be seen based on the results of variable measurements using the PC method indicating that variable X2 has no significant relationship. With the results of these measurements, the performance of the artificial neural network presents maximum results in determining loan status. Overall, the results of this study can provide an effective analytical model as well as an alternative solution for determining loan status.

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References

Murbeng, Siaga B. "Pelaksanaan Program Nasional Pemberdayaan Masyarakat Mandiri Perdesaan (PNPM MP) (Studi Pada Desa Bendungan Kecamatan Gondang Kabupaten Tulungagung)." Jurnal Administrasi Publik Mahasiswa Universitas Brawijaya, vol. 1, no. 6, 2013, pp. 1257-1265.

D. Kurnianto, B. Badaruddin, And H. Humaizi, “Keberlanjutan Dana Simpan Pinjam Perempuan Eks Program Nasional Pemberdayaan Masyarakat Mandiri Perdesaan Dalam Peningkatan Ekonomi Masyarakat Desa,” Perspektif, Vol. 10, No. 2, Pp. 383–390, 2021.

Alaslan, A. (2021). Artikel Implementasi Program Nasional Pemberdayaan Masyarakat Mandiri Pedesaan (PNPM-MP) Di Desa Harapan Kecamatan Malili Kabupaten Luwu Timur.

G. Gunawan, “Implementasi Program Pemberdayaan Masyarakat Mandiri Perdesaan Di Desa Ensaid Panjang Kecamatan Kelam Permai Kabupaten Sintang,” Publikauma J. Adm. Publik Univ. Medan Area, Vol. 9, No. 2, Pp. 33–43, 2021.

Bormasa, M. F. (2021). Implementasi Program Nasional Pemberdayaan Masyarakat Mandiri Pedesaan (Pnpm-Mp) Di Desa Harapan Kecamatan Malili Kabupaten Luwu Timur (No. xsu6b). Center for Open Science.

Baharuddin And Y. Kamis, “Dampak Program Pemberdayaan Masyarakat Miskin Melalui Bantuan Langsung Pemberdayaan Sosial Di Kelurahan Mareku Kecamatan Tidore Utara Kota Tidore Kepulauan,” Din. J. Ilm. Ilmu Adm. Negara, Vol. 8, No. 1, Pp. 15–24, 2021.

S. Arlis, D. S. Ekajaya, And M. Yanto, “Pola Penentuan Status Peminjaman Dengan Algoritma Perceptron,” Sebatik, Vol. 23, No. 2, Pp. 619–623, 2019.

L. Farokhah And R. D. Indahsari, “Implementasi Decision Tree C4. 5 Dalam Penentuan Pinjaman Uang Di Koperasi Xyz Di Banjarmasin,” Klik-Kumpul. J. Ilmu Komput, Vol. 6, No. 3, P. 293, 2019.

D. Sartika And I. Saluza, “Penerapan Metode Principal Component Analysis (Pca) Pada Klasifikasi Status Kredit Nasabah Bank Sumsel Babel Cabang Km 12 Palembang Menggunakan Metode Decision Tree,” Generic, Vol. 14, No. 2, Pp. 45–49, 2022.

R. Adhitama, M. Kurniawan, And M. Hakimah, “Klasifikasi Status Pinjaman Calon Nasabah Koperasi Simpan Pinjam Menggunakan Metode Bayesian Network (Studi Kasus: Koperasi Simpan Pinjam Btm Nasyiah 1 Bojonegoro),” In Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, Dan Teknik Informatika (Snestik), 2022, Vol. 1, No. 1, Pp. 409–414.

Hasugian, H., Mursyidin, I., & Handayani, M. (2018). Sistem Penunjang Keputusan Pemberian Kredit Dengan Metode Simple Additive Weighting (Saw) Studi Kasus: Koperasi Karyawan Gatera Pt Pln (Persero) Area Kebayoran. Sintak, 2. Retrieved From Https://Www.Unisbank.Ac.Id/Ojs/ Index.Php/Sintak/Article/View/6657.

M. I. Tegar And W. H. Haji, “Penggunaan Algoritma C4. 5 Untuk Rekomendasi Peminjaman Uang Pada Koperasi Sejahtera Mandiri,” Sisfo Vol 9 No 1, Vol. 9, 2019.

L. Y. Sipayung, “Sistem Penentuan Pemberian Pinjaman Dana Pada Nasabah Koperasi Simpan Pinjam Menggunakan Metode Simple Additive Weighting,” J. Sains Dan Teknol. Istp, Vol. 15, No. 2, Pp. 144–153, 2021.

L. S. Pangestu, Y. Ardian, And W. Kuswinardi, “Sistem Pendukung Keputusan Kelayakan Penerimaan Bantuan Pinjaman Modal Usaha Kecil Menengah Dengan Metode Smart,” Rainstek J. Terap. Sains Teknol., Vol. 1, No. 1, Pp. 55–62, 2019.

Nurfortuna, N. H., & Sarwoko, E. A. (2018). Aplikasi Penentuan Nominal Pemberian Kredit Modal Usaha Dengan Metode Naive Bayes Classifier (Studi Kasus Spkp Pnpm Mandiri Kecamatan Wirosari) (Doctoral Dissertation, Universitas Diponegoro).

F. Hidayah, “Sistem Pendukung Keputusan Kelayakan Pemberian Pinjaman Menggunakan Metode Topsis Studi Kasus: Upk Bina Artha Kecamatan Kedung.” Unisnu Jepara, 2018.

R. B. Asha And S. K. Kr, “Credit Card Fraud Detection Using Artificial Neural Network,” Glob. Transitions Proc., Vol. 2, No. 1, Pp. 35–41, 2021.

M. M. Samy, R. E. Almamlook, H. I. Elkhouly, And S. Barakat, “Decision-Making And Optimal Design Of Green Energy System Based On Statistical Methods And Artificial Neural Network Approaches,” Sustain. Cities Soc., Vol. 84, P. 104015, 2022.

W. Ye, X. Wang, Y. Liu, And J. Chen, “Analysis And Prediction Of The Performance Of Free-Piston Stirling Engine Using Response Surface Methodology And Artificial Neural Network,” Appl. Therm. Eng., Vol. 188, P. 116557, 2021.

P. A. Amahan, M. V Villarica, And A. A. Vinluan, “Technical Analysis Of Twitter Data In Preparation Of Prediction Using Multilayer Perceptron Algorithm,” In 2021 4th International Conference On Data Science And Information Technology, 2021, Pp. 109–113.

K. L. Kohsasih, M. D. A. Rizky, T. Fahriyani, V. Wijaya, And R. Rosnelly, “Analisis Perbandingan Algoritma Convolutional Neural Network Dan Algoritma Multi-Layer Perceptron Neural Dalam Klasifikasi Citra Sampah,” J. Times, Vol. 10, No. 2, Pp. 22–28, 2021.

N. Kahar And W. Aritonang, “Implementasi Jaringan Syaraf Tiruan Dengan Algoritma Perceptron Dalam Penentuan Program Studi Mahasiswa Baru,” J. Akad., Vol. 14, No. 2, Pp. 74–80, 2022.

S. Zahara And Sugianto, “Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning,” J. Resti (Rekayasa Sist. Dan Teknol. Informasi), Vol. 5, No. 1, Pp. 24–30, 2021, Doi: 10.29207/Resti.V5i1.2562.

Kurniawan, L. S. Silaban, And D. Munandar, “Implementation Of Convolutional Neural Network And Multilayer Perceptron In Predicting Air Temperature In Padang,” J. Resti (Rekayasa Sist. Dan Teknol. Informasi), Vol. 4, No. 6, Pp. 1165–1170, 2020.

F. Asydiq, “Model Prediksi Penentuan Kelayakan Nasabah Pinjaman Kur Pada Bank Mandiri Mikro Serbelawan Menggunakan Algortima Jst,” Vol. 2, No. 4, Pp. 1–1, 2022.

M. Sihombing, E. D. Sitanggang, M. Pasaribu, And M. Sembiring, “Credit Risk Prediction Using Neural Network Backpropagation Algorithm,” Infokum, Vol. 10, No. 1, Pp. 1–10, 2021.

N. Wijaya, “Model Jaringan Saraf Tiruan Untuk Evaluasi Resiko Kredit,” Comput. J. Comput. Sci. Inf. Syst., Vol. 2, No. 1, Pp. 76–90, 2018.

K. Dwiantoro, “Penerapan Jaringan Saraf Tiruan Untuk Memprediksi Kandungan Oksigen Di Dalam Flue Gas Pada Boiler Pt. Pertamina Ru V Balikpapan.” Universitas Gadjah Mada, 2020.

Arisandi, C. D. (2021). Analisis Metode Fuzzy C-Means dan Pearson Correlation untuk Reduksi Data pada Algoritma KNN.

F. Daru, M. B. Hanif, And E. Widodo, “Improving Neural Network Performance With Feature Selection Using Pearson Correlation Method For Diabetes Disease Detection,” Juita J. Inform., Vol. 9, No. 1, Pp. 123–130, 2021.

P. Dody Suarnatha, “Perbandingan Metode Profile Matching, Topsis, Dan Profile Matching-Topsis Dalam Sistem Pendukung Keputusan Penilaian Kinerja Dosen Studi Kasus: Universitas Tabanan.” Universitas Pendidikan Ganesha, 2021.

M. Yanto, S. Sanjaya, Y. Yulasmi, D. Guswandi, and S. Arlis, “Implementation multiple linear regresion in neural network predict gold price,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 3, p. 1635, Jun. 2021, DOI: http://doi.org/10.11591/ijeecs.v22.i3.pp1635-1642

Thakkar, D. Patel, and P. Shah, “Pearson Correlation Coefficient-based performance enhancement of Vanilla Neural Network for stock trend prediction,” Neural Comput. Appl., vol. 33, no. 24, pp. 16985–17000, 2021

Sun, “The correlation between green finance and carbon emissions based on improved neural network,” Neural Comput. Appl., vol. 34, no. 15, pp. 12399–12413, 2022

Jebli, “Prediction of solar energy guided by pearson correlation using machine learning,” Energy, vol. 224, 2021, doi: 10.1016/j.energy.2021.120109.

R. D. Larasati And Y. Sambharakreshna, “Analisis Pengelolaan Dana Bergulir Kelompok Simpan Pinjam Perempuan (Spp) Untuk Meminimalkan Kredit Macet Pada Pnpm Mandiri Perdesaan (Studi Kasus Pada Upk Kecamatan Binangun Kabupaten Blitar),” J. Kompil. Ilmu Ekon., Vol. 8, No. 1, Pp. 35–49, 2016.

N. Gammoudi, K. Nagaz, And A. Ferchichi, “Establishment Of Optimized In Vitro Disinfection Protocol Of Pistacia Vera L. Explants Mediated A Computational Approach: Multilayer Perceptron–Multi− Objective Genetic Algorithm,” Bmc Plant Biol., Vol. 22, No. 1, Pp. 1–13, 2022.

E. N. Fierro, C. A. Faúndez, A. S. Muñoz, And P. I. Cerda, “Application Of A Single Multilayer Perceptron Model To Predict The Solubility Of Co2 In Different Ionic Liquids For Gas Removal Processes,” Processes, Vol. 10, No. 9, P. 1686, 2022.

N. Bacanin, K. Alhazmi, M. Zivkovic, K. Venkatachalam, T. Bezdan, And J. Nebhen, “Training Multi-Layer Perceptron With Enhanced Brain Storm Optimization Metaheuristics,” Comput. Mater. Contin, Vol. 70, Pp. 4199–4215, 2022.

M. Benedetti, E. Ventura, E. Marinari, G. Ruocco, And F. Zamponi, “Supervised Perceptron Learning Vs Unsupervised Hebbian Unlearning: Approaching Optimal Memory Retrieval In Hopfield-Like Networks,” J. Chem. Phys., Vol. 156, No. 10, P. 104107, 2022.

E. Abbe, S. Li, And A. Sly, “Binary Perceptron: Efficient Algorithms Can Find Solutions In A Rare Well-Connected Cluster,” In Proceedings Of The 54th Annual Acm Sigact Symposium On Theory Of Computing, 2022, Pp. 860–873.

C. B. Pronin, O. I. Maksimychev, A. V Ostroukh, A. V Volosova, And E. N. Matukhina, “Creating Quantum Circuits For Training Perceptron Neural Networks On The Principles Of Grover’s Algorithm,” In 2022 Systems Of Signals Generating And Processing In The Field Of On Board Communications, 2022, Pp. 1–5.

S. E. Karimboyevich And A. O. Nematullayevich, “Single Layer Artificial Neural Network: Perceptron,” Eur. Multidiscip. J. Mod. Sci., Vol. 5, Pp. 230–238, 2022.

H. Pan, X. You, S. Liu, And D. Zhang, “Pearson Correlation Coefficient-Based Pheromone Refactoring Mechanism For Multi-Colony Ant Colony Optimization,” Appl. Intell., Vol. 51, No. 2, Pp. 752–774, Feb. 2021, Doi: 10.1007/S10489-020-01841-X.

P. Waldmann, “On The Use Of The Pearson Correlation Coefficient For Model Evaluation In Genome-Wide Prediction,” Front. Genet., Vol. 10, 2019, Doi: 10.3389/Fgene.2019.00899.

J. Cai, M.-H. Zhang, Y.-T. Zhu, And Y.-H. Liu, “Model Of Freight Vehicle Energy Consumption Based On Pearson Correlation Coefficient,” Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal Transp. Syst. Eng. Inf. Technol., Vol. 18, No. 5, Pp. 241–246, 2018.

H. Zhu, X. You, And S. Liu, “Multiple Ant Colony Optimization Based On Pearson Correlation Coefficient,” Ieee Access, Vol. 7, Pp. 61628–61638, 2019, Doi: 10.1109/Access.2019.2915673.