Penerapan Data Mining Untuk Klasifikasi Penerima Kredit Dengan Perbandingan Algoritma Naïve Bayes dan Algoritma C4.5

 Dison Librado (Universitas Teknologi Digital Indonsia, Yogyakarta, Indonesia)
 (*)Asyahri Hadi Nasyuha Mail (Universitas Teknologi Digital Indonsia, Yogyakarta, Indonesia)

(*) Corresponding Author

Submitted: October 12, 2023; Published: October 25, 2023

Abstract

Credit is the process of borrowing money from customers to be paid over a certain period of time and with a payment agreement. In general, credit is provided by companies operating in the financial sector such as banks, cooperatives, business credit and finance. In the implementation process, providing credit to customers must be appropriate. In reality, the process of granting credit is still given to the wrong people. The problems faced must be resolved immediately and well, if the problems continue and giving credit not to the right customers will be very detrimental to the company. The settlement process can be done by looking at customer data that has previously received credit. Data mining is a technique that can be used to help solve these problems. In the process of resolving credit granting problems, data mining can be used to process previous credit customer data to obtain a pattern of which customers are eligible for credit. Classification is a method used in data mining to solve various kinds of problems. In this research, research will be carried out using the Naïve Bayes algorithm and the C4.5 algorithm. The method comparison process carried out in the research was carried out to obtain more definite results. This is based on the importance of giving credit to the right person so that there are no problems in the process of completing credit bill payments. Completion of data mining by applying the Naïve Bayes and C4.5 algorithms has been successfully carried out and classification can be carried out for decision making, both algorithms have the same decision making result, namely "Accepted". However, there are differences in the level of accuracy obtained. In the Naïve Bayes algorithm the accuracy level is 86.67%, while in the C4.5 algorithm the accuracy level is 100%.

Keywords


Data Mining; Classification; Credit; Naïve Bayes Algorithm; Algorithm C4.5

Full Text:

PDF


Article Metrics

Abstract view : 158 times
PDF - 46 times

References

I. Ubaedi and Y. M. Djaksana, ‘Optimasi Algoritma C4.5 Menggunakan Metode Forward Selection Dan Stratified Sampling Untuk Prediksi Kelayakan Kredit’, JSiI (Jurnal Sist. Informasi), vol. 9, no. 1, pp. 17–26, 2022, doi: 10.30656/jsii.v9i1.3505.

Tukino, ‘Penerapan Metode Algoritma C4 . 5 dalam Penilaian Kelayakan Pemberian Kredit Kepada Mitra Usaha PT Arita Prima Sukses’, in Prosiding Seminar Nasional Ilmu Sosial & Teknologi (SNISTEK), 2023, no. September, pp. 306–314.

S. D. Fernando, S. D. Purnamasari, H. Syaputra, N. Adha, and O. Saputri, ‘Prediksi Kredit Sepeda Motor pada Showroom Astra Motor Sako Palembang dengan Metode Naive Bayes’, DoubleClick J. Comput. Inf. Technol., vol. 7, no. 1, pp. 17–23, 2023.

O. Pahlevi, A. Amrin, and Y. Handrianto, ‘Implementasi Algoritma Klasifikasi Random Forest Untuk Penilaian Kelayakan Kredit’, J. Infortech, vol. 5, no. 1, pp. 71–76, 2023, doi: 10.31294/infortech.v5i1.15829.

I. Nurjanah, J. Karaman, I. Widaningrum, D. Mustikasari, and S. Sucipto, ‘Penggunaan Algoritma Naïve Bayes Untuk Menentukan Pemberian Kredit Pada Koperasi Desa’, Explorer (Hayward)., vol. 3, no. 2, pp. 77–87, 2023.

J. Jasmir, X. Sika, M. Mulyadi, and R. Amelia, ‘Klasifikasi Kelayakan Pemberian Kredit Pada Calon Debitur Menggunakan Naïve Bayes’, JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 6, p. 1833, 2022, doi: 10.30865/jurikom.v9i6.5131.

N. K. A. Suarpurningsih, N. W. Utami, and N. M. Estiyanti, ‘Klasifikasi Penentuan Kelayakan Pemberian Kredit Menggunakan Metode Naive Bayes Classifier (Kasus: Koperasi Simpan Pinjam Artha Segara)’, J. Sains Komput. Inform., vol. 6, no. 1, pp. 391–404, 2022.

R. Hasibuan Budiansyah, H. Hafizah, and R. Mahyuni, ‘Penerapan Data Mining Clustering Dengan Menggunakan Algoritma K-Means Pada Data Nasabah Kredit Bermasalah PT. BPR Milala’, J-SISKO TECH (Jurnal Teknol. Sist. Inf. dan Sist. Komput. TGD), vol. 5, no. 1, p. 7, 2022, doi: 10.53513/jsk.v5i1.4767.

M. Rianto, R. Rusdiah, and H. Ichwan, ‘Penerapan Data Mining Dengan Metode Naïve Bayes Dan Learning Vector Quantization Credit Rating Dalam Memprediksi Kelayakan Pemberian Kredit Oleh PT. BPR Lebak Sejahtera’, Respati, vol. 17, no. 1, p. 69, 2022, doi: 10.35842/jtir.v17i1.443.

A. Triayudi and Sumiati, ‘Implementasi Klasifikasi Data Mining Untuk Penentuan Kelayakan Pemberian Kredit dengan Menggunakan Algoritma Naïve Bayes’, J. Sist. Komput. dan Inform. Hal 240−, vol. 244, no. 1, pp. 240–244, 2022, doi: 10.30865/json.v4i1.4653.

A. Sentimen et al., ‘Analisis Sentimen Objek Wisata Bali Di Google Maps Menggunakan Algoritma Naive Bayes’, J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 1, pp. 418–427, 2022.

A. C. Fauzan and K. Hikmah, ‘Implementasi Algoritma Naive Bayes Dalam Analisis Polarisasi Opini Masyarakat Terkait Vaksin Covid-19’, Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 7, no. 2, pp. 122–128, 2022, doi: 10.36341/rabit.v7i2.2403.

I. Verawati and B. S. Audit, ‘Algoritma Naïve Bayes Classifier Untuk Analisis Sentiment Pengguna Twitter Terhadap Provider By.u’, J. Media Inform. Budidarma, vol. 6, no. 3, p. 1411, 2022, doi: 10.30865/mib.v6i3.4132.

A. S. Rahayu, A. Fauzi, and R. Rahmat, ‘Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify’, J. Sist. Komput. dan Inform., vol. 4, no. 2, p. 349, 2022, doi: 10.30865/json.v4i2.5398.

B. H. Hayadi and A. R. Damanik, ‘Pendekatan Machine Learning Menggunakan Algoritma C4 . 5 Berbasis Pso Dalam Analisa’, J. Inform. dan Tek. Elektro Terap., vol. 10, no. 3, 2022.

R. Girsang, E. F. Ginting, and M. Hutasuhut, ‘Penerapan Algoritma C4.5 Pada Penentuan Penerima Program Bantuan Pemerintah Daerah’, J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 1, no. 4, p. 449, 2022, doi: 10.53513/jursi.v1i4.5727.

T. Widiastuti, K. Karsa, and C. Juliane, ‘Evaluasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Metode Klasifikasi Algoritma C4.5’, Technomedia J., vol. 7, no. 3, pp. 364–380, 2022, doi: 10.33050/tmj.v7i3.1932.

D. Darwis, N. Siskawati, and Z. Abidin, ‘Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional Dedi’, J. Tekno Kompak, vol. 15, no. 1, p. 131, 2021, doi: 10.33365/jtk.v15i1.744.

A. Damuri, U. Riyanto, H. Rusdianto, and M. Aminudin, ‘Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako’, J. Ris. Komput., vol. 8, no. 6, pp. 219–225, 2021, doi: 10.30865/jurikom.v8i6.3655.

I. W. Saputro and B. W. Sari, ‘Uji Performa Algoritma Naïve Bayes untuk Prediksi Masa Studi Mahasiswa’, Creat. Inf. Technol. J., vol. 6, no. 1, p. 1, 2020, doi: 10.24076/citec.2019v6i1.178.

D. Marlina and M. Bakri, ‘Penerapan Data Mining Untuk Memprediksi Transaksi Nasabah Dengan Algoritma C4.5’, J. Teknol. dan Sist. Inf., vol. 2, no. 1, pp. 23–28, 2021.

S. Febriani and H. Sulistiani, ‘Analisis Data Hasil Diagnosa Untuk Klasifikasi Gangguan Kepribadian Menggunakan Algoritma C4.5’, 89Jurnal Teknol. dan Sist. Inf., vol. 2, no. 4, pp. 89–95, 2021.

S. Ucha Putri, E. Irawan, and F. Rizky, ‘Implementasi Data Mining Untuk Prediksi Penyakit Diabetes Dengan Algoritma C4.5’, Januari, vol. 2, no. 1, pp. 39–46, 2021.

A. H. Nasyuha, Z. Zulham, and I. Rusydi, ‘Implementation of K-means algorithm in data analysis’, TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 20, no. 2, p. 307, Apr. 2022, doi: 10.12928/telkomnika.v20i2.21986.

A. H. Nasyuha, ‘Implementasi Teorema Bayes Dalam Diagnosa Penyakit Ayam Broiler’, vol. 4, pp. 1062–1068, 2020, doi: 10.30865/mib.v4i4.2366.

E. Febriyani and H. Februariyanti, ‘Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Di Twitter’, J. TEKNO KOMPAK, vol. 17, no. 2, pp. 25–38, 2022.

N. Agustina and M. Hermawati, ‘Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media’, Fakt. Exacta, vol. 14, no. 4, pp. 1979–276, 2021, doi: 10.30998/faktorexacta.v14i4.11259.

A. I. Tanggraeni and M. N. N. Sitokdana, ‘Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes’, JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785–795, 2022, doi: 10.35957/jatisi.v9i2.1835.

R. R. Andarista and A. Jananto, ‘Penerapan Data Mining Algoritma C4 . 5 Untuk Klasifikasi Hasil Pengujian Kendaraan Bermotor’, vol. 16, no. 2, pp. 29–43.

I. Romli and A. T. Zy, ‘Penentuan Jadwal Overtime Dengan Klasifikasi Data Karyawan Menggunakan Algoritma C4.5’, J. Sains Komput. Inform. (J-SAKTI, vol. 4, no. 2, pp. 694–702, 2020.

N. Azwanti and E. Elisa, ‘Analisa Kepuasan Konsumen Menggunakan Algoritma C4.5’, Pros. Semin. Nas. Ilmu Sos. dan Teknol., no. 3, pp. 126–131, 2020.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Penerapan Data Mining Untuk Klasifikasi Penerima Kredit Dengan Perbandingan Algoritma Naïve Bayes dan Algoritma C4.5

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.