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All Journal International Journal of Advances in Applied Sciences Resital: Jurnal Seni Pertunjukan (Journal of Performing Arts) Dinamika Pendidikan Journal of Economic Education Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOURNAL SCIENTIFIC OF MANDALIKA (JSM) Jurnal Litbang Kota Pekalongan Locus Journal of Academic Literature Review ASEAN Marketing Journal IC Tech: Majalah Ilmiah JAZIRAH: JURNAL PERADABAN DAN KEBUDAYAAN Riwayat: Educational Journal of History and Humanities SmartComp IDEA: Jurnal Seni Pertunjukan CEFARS : JURNAL AGRIBISNIS DAN PENGEMBANGAN WILAYAH Jurnal Agribisnis dan Pengembangan Wilayah IC Tech: Majalah Ilmiah
Taryadi Taryadi
SMP Nasima Semarang
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Financial distress prediction for batik small and medium enterprises credit financing based on deep learning algorithm Taryadi, Taryadi; Sudiyatno, Bambang; Basiya, Robertus; Yunianto, Era
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp245-252

Abstract

One of the biggest obstacles that any finance provider has when evaluating a borrower's creditworthiness is the prediction of financial trouble. The credit decision-making process is made more difficult for small and medium enterprises (SMEs) due to their inherent ambiguity, which raises financing costs and lowers the chance of approval. In order to estimate a binomial classifier for predicting financial hardship using logistic regression (LR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) techniques, this study examines data from batik SMEs in Pekalongan city. Financial ratios predict the first period and grow in a multi-period model based on temporal factors, credit history, and age. Financial distress is defined as a substantial obstacle to a business's capacity to pay its debts as opposed to the potential for bankruptcy. The long short-term memory (LSTM) algorithm with more variables yields the best prediction accuracy. The study's conclusion indicates that in order to guarantee the accuracy of financial distress prediction, the time at risk must be adjusted.
Co-Authors Abdul Halim Anshor Aguswin, Ahmad Ali Imron Arga Hananto Arifin, Faizal Ariyanto, Tori Bakhrudin Latif Bambang Sudiyatno Era Yunianto Faizal Arifin Fernanda, Arif Ika Royanti, Nur Is Zunaini Nursinah KARYOTO, KARYOTO Kasmari Kasmari . Maulana, Fariz Mulyadi Cahyoraharjo Nugraha, Rahmat Mulya Nur Ika Royani Prasetiono, Slamet Joko Radhali Respiar, Boby Robertus Basiya Rusdarti - Samosir, Timothy Harris Sudiyanto, Bambang Tory Ariyanto Widiyono, Widiyono Wiyanto - Yunianto, Era