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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ilmiah Publika Kalbiscientia Jurnal Sains dan Teknologi Journal Indonesia Law and Policy Review (JILPR) The Journal of Business and Management Research Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah ANDIL Mulawarman Journal of Community Engagement Jurnal Penelitian Manajemen (JPEMA)
Richard
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : JITK (Jurnal Ilmu Pengetahuan dan Komputer)

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IMPLEMENTATION MEAN IMPUTATION AND OUTLIER DETECTION FOR LOAN PREDICTION USING THE RANDOM FOREST ALGORITHM Nimatul Mamuriyah; Richard; Haeruddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6437

Abstract

Loans and credit are among the most in-demand banking products, making accurate loan prediction systems essential for minimizing bank credit risks and boosting profitability. This study proposed a loan prediction model using the Random Forest algorithm, with mean imputation and 3 outlier detection (Boxplot, Z-score, and Interquartile Range (IQR)) as data pre-processing methods. Using Lending Club loan data from 2014-2021 (466,285 records, split 70/30 for training/testing), model performance was assessed using accuracy, recall, and F1 Score. The proposed approach achieved a 95% prediction accuracy, outperforming previous models at 83%. The best results were obtained using mean imputation with IQR-based outlier detection. However, the determination of the mean imputation mean can be a limitation of this study. This highlights the importance of thorough pre-processing in enhancing prediction accuracy. The study underscores the role of machine learning and financial technology (fintech) in informing credit decisions and support incorporating imputation and outlier handling as standard steps in financial modeling pipeline
Co-Authors Agustidora, Soviana Ahmad Jusmin Angga Dwi Setiawan, Angga Dwi bagus Mulyawan Carollin, Chintia Christiani, Susan Darnah Dina Haeruddin Hexana Sri Lastanti Jurinus, Bilung MARDIANA Mebang, Claudia Laura Milasari Mira Ziveria, Mira Nimatul Mamuriyah Putri, Sherly Meylinda Rendy Sarudin RR. Ella Evrita Hestiandari RUMASUKUN, M. RIDWAN Saling Sava, Benekdiktus Sudradjat, Agus Yendra