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All Journal Jurnal Penelitian Komunikasi dan Opini Publik MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA
Shinta Palupi
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Other

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CLUSTER ANALYSIS FOR LEARNING STYLE OF VOCATIONAL HIGH SCHOOL STUDENT USING K-MEANS AND FUZZY C-MEANS (FCM) Shinta Palupi; Reza Andrea; Siti Qomariah
Jurnal Penelitian Komunikasi dan Opini Publik Vol 21, No 2 (2017): Jurnal Penelitian Komunikasi dan Opini Publik - Desember 2017
Publisher : BPSDMP Kominfo Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (487.564 KB) | DOI: 10.33299/jpkop.21.2.1243

Abstract

Predicting financial default risks: A machine learning approach using smartphone data Shinta Palupi; Gunawan; Ririn Kusdyawati; Richki Hardi; Rana Zabrina
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 14 No. 3 (2024): Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v14i3.107-118

Abstract

This study leverages machine learning (ML) techniques to predict financial default risks using smartphone data, providing a novel approach to financial risk assessment. Data were collected from 1,000 individuals who had taken personal loans, focusing on key behavioral parameters such as app usage frequency, GPS location data, and communication patterns over a six-month period prior to loan application. The analysis employed Logistic Regression, Decision Trees, and Random Forest models to determine correlations between these parameters and default risks. The Random Forest model demonstrated superior performance, achieving 85% accuracy. Key findings show that high usage of financial apps was associated with lower default risks, while irregular communication patterns and erratic mobility were significant indicators of higher risk. These results suggest that smartphone-derived behavioral data can significantly enhance traditional credit scoring methods. The study not only contributes to predictive analytics in financial risk management but also raises ethical considerations around privacy and data security.
Co-Authors Gunawan Rana Zabrina Reza Andrea Richki Hardi Ririn Kusdyawati Siti Qomariah