Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Jurnal Akuntansi, Manajemen dan Bisnis Digital Indonesian Journal of Social Economics and Agricultural Policy
Deki Fujiansyah
Universitas Serelo Lahat
Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Analisis Default Kartu Kredit Dengan Deep Learning Untuk Mendukung Keputusan Manajemen Keuangan Digital Dini Pratiwi; Deki Fujiansyah
Jurnal Akuntansi, Manajemen dan Bisnis Digital Vol 5 No 2 (2026): April
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jambd.v5i2.10484

Abstract

The development of the digital economy requires financial institutions to optimize risk management through data-driven analysis. This study aims to analyze the factors influencing credit card default and to develop a predictive model using a Deep Learning algorithm based on an Artificial Neural Network (ANN) to support digital financial management decision-making. The data were obtained from the public “Default of Credit Card Clients” dataset (UCI/Kaggle), consisting of 30,000 observations and 23 financial variables. The results show that the model achieved an accuracy of 81.6% and an AUC value of 0.771, with high specificity but relatively low recall. These findings indicate that deep learning is effective in capturing non-linear patterns in customer payment behavior and can serve as a decision support tool for digital financial institutions in identifying credit risk and designing more adaptive default mitigation strategies.
Segmentation of Inclusive Economic Growth Profiles Across Provinces in Indonesia Using a Clustering Approach Deki Fujiansyah
Indonesian Journal of Social Economics and Agricultural Policy Vol. 1 No. 1 (2025): (July) Indonesian Journal of Social Economics and Agricultural Policy
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/ijseap.v1i1.64

Abstract

This study aims to address the issue of economic inequality across provinces in Indonesia despite national economic growth. Inclusive economic growth, which ensures that economic progress benefits all layers of society particularly those in lower socio-economic groups remains unevenly distributed. Using Gross Regional Domestic Product (GRDP) per employed person as a key indicator of inclusive growth, this research investigates the contributing factors and patterns of disparity among 34 Indonesian provinces from 2015 to 2021. A quantitative approach using K-Means clustering was applied to segment provinces based on determinants such as education, health, investment, formal sector involvement, and infrastructure. The study employed secondary data sourced from Statistics Indonesia (BPS) and the Directorate General of Fiscal Balance (DJPK), processed with machine learning techniques such as standardization, PCA dimensionality reduction, and cluster evaluation using Silhouette Score. The optimal number of clusters was determined using the Elbow Method, which identified three distinct groups of provinces. These clusters were analyzed to highlight the unique characteristics and disparities among them, supporting the development of more targeted, evidence-based policy recommendations. The findings not only deepen the understanding of interprovincial economic disparities but also emphasize the potential of data science in shaping inclusive and sustainable development strategies.
Co-Authors Dini Pratiwi