Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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All Journal EAJ (ECONOMICS AND ACCOUNTING JOURNAL) International Journal for Applied Information Management
Nabila, Zahara
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Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Predicting Pharmaceutical Product Discontinuation Using Decision Tree and Random Forest Algorithms Based on Product Attributes Suhartono, Susilo; Nabila, Zahara
International Journal for Applied Information Management Vol. 5 No. 2 (2025): Regular Issue: July 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i2.101

Abstract

This study aims to predict the discontinuation of pharmaceutical products using machine learning models, focusing on key product attributes such as manufacturer, composition, price, and packaging. A comprehensive dataset of over 250,000 pharmaceutical products from India was analyzed, with two models—Decision Tree and Random Forest—being employed for prediction. The models were evaluated based on accuracy, precision, recall, and F1-score. The Random Forest model outperformed the Decision Tree with a higher accuracy, but both models struggled with the imbalanced dataset, showing low recall for the minority class (discontinued products). Feature importance analysis identified manufacturer and composition as the most influential factors in predicting product discontinuation. These findings offer valuable insights for pharmaceutical companies in managing product portfolios and optimizing their lifecycle strategies. Despite limitations in data quality and class imbalance, this study provides a foundation for future research, suggesting the integration of additional data sources and the application of deep learning techniques to further enhance prediction accuracy.
Systematic Literature Review: Analysis Of Financial Distress in Manufacturing Companies on The Indonesia Stock Exchange Using The Altman Z-Score Method Nabila, Zahara; Ariyani, Reni; Olifia, Norma; Uyun, Qurotul; Rahmatika, Dien Noviani
EAJ (Economic and Accounting Journal) Vol. 9 No. 1 (2026): EAJ (Economics and Accounting Journal)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/eaj.v9i1.y2026.p71-85

Abstract

This study aims to synthesize scientific evidence through the Systematic Literature Review (SLR) method to map and analyze financial distress predictions in manufacturing companies listed on the Indonesia Stock Exchange (IDX) with a focus on the application of the Altman Z-Score Model. Given the strategic role of the manufacturing sector in the national economy, early detection of bankruptcy risk is crucial. This study uses the PRISMA protocol by analyzing 30 accredited journal articles published in the period 2020–2025, with the inclusion criteria of IDX manufacturing companies that apply the Altman Z-Score. The synthesis results show that the level of financial distress in IDX manufacturing companies fluctuates between sub-sectors and is influenced by time dynamics, especially during the pandemic. Most studies indicate that many companies are in the gray area, reflecting moderate financial vulnerability. In general, the Altman Z-Score shows a high level of prediction accuracy (above 70%), although in certain sub-sectors alternative models such as Zmijewski and Springate are considered superior. In addition, components X3 (EBIT/Total Assets) and X2 (Retained Earnings/Total Assets) are consistently the most dominant variables in determining financial distress. The conclusion of this SLR confirms that the Altman Z-Score remains relevant and reliable as a bankruptcy prediction tool for BEI manufacturing companies. These findings contribute theoretically to mapping the latest research developments and provide practical implications for investors, creditors, and management in decision-making and financial risk mitigation.
Co-Authors Ariyani, Reni Olifia, Norma Qurotul Uyun Rahmatika, Dien Noviani Suhartono, Susilo