OPTIMALISASI ALGORITMA C4.5 TERHADAP METODE DECISION TREE DALAM MENENTUKAN PLAFON KREDIT NASABAH
Decision Tree, Algorithm C4.5, Credit Ceiling, Banking
Abstract
The most basic banking activity is collecting money and buying money from the whole society. Then sell the collected money by directing it to the community through credit or credit. However, it is often found that customers are unable to pay their receivables based on the amount of receivables which often exceeds the specified payment period. Therefore, banking companies must know the ability to pay customers by providing credit limits to avoid losses. The purpose of this study was to analyze the data using the Decision Tree method with the C4.5 Algorithm on the report data of BPR Pijer Podi Kekelengen receivables in order to determine the customer's credit ceiling. From the data obtained from the accounts receivable report, the company produces 5 attributes, namely Payments, Receivables, Transactions, Recommendations, and Ceiling where the decision label is Ceiling. After testing the report data at BPR Pijer Podi Kekelengen using the Decision Tree method with the C4.5 Algorithm, it is concluded that if the ceiling is large, the payment is not good.
Downloads
References
Hermawati, F. A. (2013). Data Mining. CV. Andi Offset.
Junita, V., & Bachtiar, F. A. (2020). Klasifikasi Aktivitas Manusia menggunakan Algoritme Decision Tree C4.5 dan Information Gain untuk Seleksi Fitur. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(10), 9426–9433. http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/6446
Kamagi, D. H., & Hansun, S. (2014). Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa. Jurnal ULTIMATICS, 6(1), 15–20. https://doi.org/10.31937/ti.v6i1.327
Nurajijah, N., & Riana, D. (2019). Algoritma Naïve Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah. Jurnal Teknologi Dan Sistem Komputer, 7(2), 77–82. https://doi.org/10.14710/jtsiskom.7.2.2019.77-82
Pitono, P., & Susetiyo, W. (2019). Tinjauan Yuridis Penyelesaian Kredit Macet pada Bank Perkreditan Rakyat Berkah Pakto Kediri, Jawa Timur. Jurnal Supremasi, 9(2), 49–68. https://doi.org/10.35457/supremasi.v9i2.794
Pratama, A. Z., Kurniawati, L., Larbona, S., & Haryanti, T. (2019). Algoritma C4 . 5 Untuk Klasifikasi Nasabah Dalam Memprediksi Kredit Macet. Information System for Educators and Professionals, 3(2), 121–130.
Rani, L. N. (2016). Klasifikasi Nasabah Menggunakan Algoritma C4.5 Sebagai Dasar Pemberian Kredit. INOVTEK Polbeng - Seri Informatika, 1(2), 126. https://doi.org/10.35314/isi.v1i2.131
Romindo, R. (2021). Analisa Penentuan Saham Terbaik Menggunakan Metode Analytic Hierarchy Process ( AHP ). 5, 790–798. https://doi.org/10.30865/mib.v5i3.2978
Romindo, R., & Jamaludin, J. (2019). Implementasi Metode ANP Terhadap Sistem Pendukung Keputusan Memilih Toko Daring Terbaik. Jurnal Media Informatika Budidarma, 3(4), 254. https://doi.org/10.30865/mib.v3i4.1373
Widayu, H., Nasution, S. D., Silalahi, N., & Mesran, M. (2017). Data Mining Untuk Memprediksi Jenis Transaksi Nasabah Pada Koperasi Simpan Pinjam Dengan Algoritma C4.5. Media Informatika Budidarma, Vol 1, No(2), 37.
This work is licensed under a Creative Commons Attribution 4.0 International License.