Article Per Year (5 Year)
p-Index From 2020 - 2025
1.347
P-Index
This Author published in this journals
All Journal International Research Journal of Business Studies (E-Journal) Owner : Riset dan Jurnal Akuntansi Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Jurnal Proaksi Jurnal Akuntansi dan Keuangan Indonesia Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Jurnal Indonesia Sosial Teknologi
Harahap, Siti Nurwahyuningsih
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Indonesia Sosial Teknologi

 1 
Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of Manufacturing Companies Listed on the Stock Exchange) Pratiwi, Citra Yustika; Harahap, Siti Nurwahyuningsih
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 8 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i8.1278

Abstract

This study aims to analyze bankruptcy prediction for manufacturing companies using machine learning. Financial data from manufacturing companies listed on the Indonesia Stock Exchange for the period from 2013 to 2023 are used in this study. The analytical methods employed include Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). The results of this study are expected to provide benefits to various stakeholders: manufacturing companies in identifying early signs of bankruptcy, creditors in evaluating the feasibility of extending credit, investors in making investment decisions, academics in advancing research in bankruptcy prediction, and market regulators (OJK) in enhancing the efficiency of supervision over manufacturing companies. The results indicate that SVM is effective in predicting historical data with consistent performance, while LSTM excels in handling variations and patterns in new data.
Co-Authors Arief Fadhilah Desyandri Desyandri Dina Anggraeni, Dina Gultom, Corry Margaretha Jayanti Mahardika, Surya Aditama Pratiwi, Citra Yustika Putri, Nita Winatasari Rofifah, Fathia Satyarini, Erning Ditta Dyah Utie, Maulida Salmi Vivian Vivian