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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika JIKO (Jurnal Informatika dan Komputer) Bina Insani ICT Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System Compiler Technologia: Jurnal Ilmiah CERMIN: Jurnal Penelitian Jurnal Sistem Informasi Simpatik: Jurnal sistem Informasi dan Informatika Profitabilitas Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi Academic Journal of Computer Science Research (AJCSR) SAINSTECH: Jurnal Penelitian dan Pengkajian Sains dan Teknologi INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System SmartComp
Abdussomad, Abdussomad
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Other

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Journal : Compiler

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Implementation of the Decission Tree Algorithm to Determine Credit Worthiness Abdussomad Abdussomad; Ilham Kurniawan; Agung Wibowo
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1911

Abstract

Credit is a loan from a bank that needs to be repaid with interest. In practice, problematic credit or bad credit often occurs due to less thorough credit analysis in the credit granting process, or from bad customers. This research aims to predict creditworthiness using the Decision Tree Classification Algorithm and find a solution for determining it. This research uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method. This research method tests the effects of using the decision tree, Support Vector Machine, and Naïve Bayes model with the Decision Tree Classification Algorithm. The decision tree classification algorithm accurately analyzed problem loans and non-problem debtors at 93.49%. The decision tree algorithm test results are better than the support vector machine by 3.45%, and naïve bayes by 13.03%. The results of our study were also 4.16% better than the previous study. This research has also implemented the selected model in the form of website application deployment.
Co-Authors Agung Wibowo Agung Wibowo Akmaludin Akmaludin Akmaludin Akmaludin, Akmaludin Andi Taufik Asep Asep Burhanudin Zali Cep Adiwiharja Dede Firmansyah Saefudin Dewi Yuliandari Diah Wijayanti Dila Delia Fadilah Duwi Cahya Putri Buani Duwi Cahya Putri Buani Eka Fitriani Eko Haryadi Eko Haryadi Eriska Sinurat Fikri Maulana, Fikri Gata, Windu Grace Gata, Grace Hamzah, Zainul Hardiyanto Hardiyanto Hasan Basri Ilham Kurniawan Ilham Kurniawan Mike Amelia Miranti Miranti Muhammad Tabrani Nia Kusuma Wardhani Nopitasari Nopitasari Prasetyo, Basuki Hari Priska Agrelia Ratih Kurnia Rizal Amegia Saputra Robi Robi robi sopandi Royadi - Siswanto, Siswanto Suharyanto Suharyanto Syafrianto Syafrianto Tabrani, Muhamad Taufik Baidawi, Taufik Wardhani, Nia Kusuma Widya Apriliah Widya Apriliah, Widya Windu Gata Yoseph Tajul Arifin