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All Journal Journal of Management and Bussines (JOMB) Jurnal Akuntansi, Manajemen dan Bisnis Digital
Dini Pratiwi
Universitas Serelo Lahat
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Pengaruh Modal Kerja, Kas dan Piutang Terhadap Profitabilitas pada Perusahaan Industri Barang Konsumsi di Bursa Efek Indonesia Dini Pratiwi; Melia Andayani; Nidyawati Nidyawati; Iskandar Malian; Pahlan Pahlan
Journal of Management and Bussines (JOMB) Vol 5 No 2 (2023): Journal of Management and Bussines (JOMB)
Publisher : IPM2KPE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/jomb.v5i2.7982

Abstract

This research aims to determine the influence of working capital, cash and receivables on the probability of consumer and industrial goods companies on the Indonesian Stock Exchange. This research uses the cross section method. The results of the t test research show that working capital (X1) against probability (Y) can be processed partially using a calculated t of 1.971≤ -6.5253. Therefore, there is a partially significant influence of working capital on probability. For cash (X2) the probability (Y) t count is -6.64449, while the t count is 1.9721, so it can be concluded that t > t table means that there is a partial significant difference in cash to the probability, at the t count probability of 4.123983 t table is 1.9721. In conclusion, working capital, cash and receivables simultaneously influence probability. Keywords: Cash, Working Capital, Receivables, Profitability
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.
Co-Authors Deki Fujiansyah Iskandar Malian Melia Andayani Nidyawati Nidyawati Pahlan Pahlan