Analisis Risiko Pinjaman dengan Metode Support Vector Machine, Artificial Neural Network dan Naïve Bayes
Main Article Content
Abstract
Banking is an industrial institution that is influential in the economy of a country. Banks are engaged in finance that collect funds from the public in the form of deposits and distribute loans to the public. It is undeniable, that in making loans to the public, problems will inevitably arise, such as the borrower being late in making installment payments or misuse of funds for other purposes, the borrower failing to build his business, thereby hampering installment payments. In this study, we will predict loan risk using a machine learning approach using several methods such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naive Bayes. From the results of research that has tested the three methods using cross validation, confusion matrix, and ROC curves, the Support Vector Machine (SVM) method, which is the method with the best results, is 92.0% accuracy, then the second method is Artificial Neural Network. (ANN) of 91.2% and the lowest accuracy is the Naïve Bayes method with an accuracy of 81.2%.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to JTIK journal and Research Division, KITA Institute as the publisher of the journal. Copyright encompasses rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
JTIK journal and Research Division, KITA Institute and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in JTIK journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form JTIK]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Muhammad Wali (Editor-in-Chief)
Editorial Office of Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Research Division, KITA Institute
Teuku Nyak Arief Street Nomor : 7b, Lamnyong, Lamgugop, Kota Banda Aceh
Telp./Fax: 0651-8070141
Email: jtik@lembagakita.org - journal@lembagakita.org
References
Watopa, E.Y., Murni, S. and Saerang, I.S., 2017. Analisis Penerapan Pengelolaan Risiko Kredit dan Risiko Operasional Pada PT. Bank Sulut GO. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 5(2). DOI: https://doi.org/10.35794/emba.5.2.2017.15619.
Siamat, D., Kusumawardhani, P.N. and Agustin, F., 2005. Manajemen lembaga keuangan: kebijakan moneter dan perbankan: dilengkapi UU no. 10 tahun 1998, UU no. 23 tahun 1999, UU no. 03 tahun 2004. Lembaga Penerbit Fakultas Ekonomi Universitas Indonesia.
Bustami, B., 2013. Penerapan algoritma Naive Bayes untuk mengklasifikasi data nasabah asuransi. TECHSI-Jurnal Teknik Informatika, 5(2). DOI: https://doi.org/10.29103/techsi.v5i2.154.
Alhaq, Z., Mustopa, A., Mulyatun, S. and Santoso, J.D., 2021. PENERAPAN METODE SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN PENGGUNA TWITTER. Journal of Information System Management (JOISM), 3(1), pp.16-21. DOI: https://doi.org/10.24076/joism.2021v3i2.558.
Vapnik, V., 1995. Support-vector networks. Machine learning, 20, pp.273-297.
Gaspersz, V., 2002. Production Planning and Inventory Control Manufacturing 21. PT. Gramedia Pustaka Utama: Jakarta
Eska, J., 2016. Penerapan Data Mining Untuk Prediksi Penjualan Wallpaper Menggunakan Algoritma C4. 5 STMIK Royal, Ksiaran.
Gunawan, A. and Marwan, A., 2003. Anggaran Perusahaan. Yogyakarta: BPFE.
Cristianini, N. and Shawe-Taylor, J., 2000. An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
Bhakti, H.D., 2019. Aplikasi Artificial Neural Network (ANN) untuk Memprediksi Masa Studi Mahasiswa Program Studi Teknik Informatika Universitas Muhammadiyah Gresik. Jurnal Eksplora Informatika, 9(1), pp.88-95.
Syahrudin, A.N. and Kurniawan, T., 2018. Input dan output pada bahasa pemrograman python. Jurnal Dasar Pemrograman Python Stmik, January, pp.1-7.
Supribadi, K., Khakhim, N., 2014. Analisis metode support vector machine (SVM) untuk klasifikasi penggunaan lahan berbasis penutup lahan pada citra ALOS AVNIR-2 (Doctoral dissertation, Universitas Gadjah Mada).
Salim, A. and Pambudi, W.S., 2014. Implementasi Metode Hybrid Artificial Neural Network (Ann)–Pid Untuk Perbaikan Proses Berjalan Pada Prototype Robot Material Handling. Jurnal Mikrotek, 1(3), pp.155-164.
Muin, A.A., 2016. Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi). Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar, 2(1), pp.22-26.
Sparta, S., 2016. Risiko Kredit dan Efisiensi Perbankan di Indonesia. MIX: Jurnal Ilmiah Manajemen, 6(1), pp.28-44.
Suryati, I.S.N., Perancangan Sistem Informasi Prediksi Risiko Kredit Berbasis Web Menggunakan Metode Naïve Bayes Classifier. Perpustakaan Universitas Jember.
Ferry, N.I., 2008. Manajemen Risiko Perbankan. Pemahaman Pendekatan, 3.
Alvian, F.S., Carbini, S. and Komarudin, K., 2020. Prediksi Kelayakan Pemberian Fasilitas Kartu Kredit Kepada Nasabah Dengan Metode Klasifikasi Data Mining (Studi Kasus: Bank XYZ). Jurnal Computech & Bisnis, 14(2), pp.123-128.
Tengor, R., Murni, S. and Moniharapon, S., 2016. Penerapan Manajemen Risiko Untuk Meminimalisir Risiko Kredit Macet Pada PT. Bank Sulutgo. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 3(4). DOI: https://doi.org/10.35794/emba.3.4.2015.10892
Sembiring, F., 2014. Analisis Terhadap Penerapan Manajemen Resiko Kredit pada PT. Bank Sumut. Library. Polmed. ac. id.
Tamon, F.B.C., Tumbel, T.M. and Tatimu, V., 2016. Analisis Tingkat Risiko Kredit Pada PT. Bank Sulut, Tbk di Manado. JURNAL ADMINISTRASI BISNIS (JAB), 4(1). DOI: https://doi.org/10.35797/jab.v4.i1.%25p.
Pratiwi, Y.W., Dwiatmanto, D. and NP, M.G.W.E., Analisis Manajemen Risiko Kredit Untuk Meminimalisir Kredit Modal Kerja Bermasalah (Studi Pada PT. Bank Rakyat Indonesia (Persero), Tbk Cabang Ponorogo) (Doctoral dissertation, Brawijaya University).
Larose, D.T., 2005. An introduction to data mining. Traduction et adaptation de Thierry Vallaud.