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All Journal Nominal: Barometer Riset Akuntansi dan Manajemen Trikonomika: Jurnal Ekonomi PROFIT : Kajian Pendidikan Ekonomi dan Ilmu Ekonomi Jurnal Mirai Management Jurnal Manajemen Indonesia Jurnal Mitra Manajemen Agregat: Jurnal Ekonomi dan Bisnis JMM (Jurnal Masyarakat Mandiri) SEIKO : Journal of Management & Business Almana : Jurnal Manajemen dan Bisnis JASa (Jurnal Akuntansi, Audit dan Sistem Informasi Akuntansi) JAE (Jurnal Akuntansi dan Ekonomi) Sosiohumanitas: Jurnal Ilmu-Ilmu Sosial dan Humaniora Jurnal Manajemen Dirgantara Jurnal Riset Akuntansi Kontemporer jurnal syntax admiration Jurnal Riset Akuntansi dan Keuangan Jurnal Riset Manajemen dan Bisnis Indonesian Journal of Business Analytics (IJBA) FRIMA: Festival Riset Ilmiah Manajemen & Akuntansi Jurnal Pengabdian Masyarakat Akademisi Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) eProceedings of Management Community Service Seminar and Community Engagement (COSECANT) Enrichment: Journal of Multidisciplinary Research and Development Multidisciplinary Indonesian Center Journal Jurnal Bisnis dan Pemasaran Digital International Journal of Accounting and Management Information Systems Global Academy of Business Studies
Brady Rikumahu
Universitas Telkom
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Prediction of financial distress in transportation and logistics companies before, during and after the Covid-19 pandemic listed on the Indonesia Stock Exchange Dewi, Sinta; Rikumahu, Brady
Global Academy of Business Studies Vol. 1 No. 2 (2024): October
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/gabs.v1i2.3576

Abstract

Purpose: This study aims to predict financial distress in transportation and logistics companies before, during, and after the Covid-19 pandemic. Research Methodology: A total of 23 transportation and logistics companies were selected as the research sample using purposive sampling method. Secondary data from audited financial statements for 2019–2023 were analyzed. Three financial ratios—Current Ratio (CR), Return on Assets (ROA), and Debt-to-Asset Ratio (DAR)—were used as input variables. An Artificial Neural Network (ANN) with a multilayer perceptron backpropagation algorithm was employed to train and test the prediction models. The best architecture was identified by comparing the model performance across variations in hidden neurons. Results: The results reveal that companies reported as financially distressed have lower average values for the three ratios than companies not experiencing financial distress, making them suitable input variables. The best artificial neural network architecture in this study included an input layer with 60 neurons, a hidden layer with 15 neurons, and an output layer with a single neuron. This architecture achieved a training performance mean square error (MSE) of 0.125004 and an R-value of 50.00%. The study's findings suggest that 12 companies are likely to experience financial distress. Conclusions: Financial ratios are effective indicators of distress, and ANN models can predict potential bankruptcy with a reasonable accuracy. Limitations: This study is limited to three financial ratios and a single sector, which may not fully capture the broader determinants of financial distress. Contribution: This study contributes to the financial distress prediction literature by applying ANN to transportation and logistics firms in Indonesia and offers practical tools for stakeholders to anticipate risks and design preventive strategies.
Co-Authors A. Mukti Soma Ade Romadhony Adhi Prasetio Adhia Ika Wahyuni Akhmad, Mu’arif Fandhi Ali Riza Fahlevi Alza, Hanifa Fauzia Anisah Firli Anisah Firly Anna Quraisy Haq Arif Kuswanto Athifah Nadya Akmalia Brigitta Priscilla DivinaRianti Dadan Rahadian Danial, Rahmadiva Dianitha Dembi Tri Al-Fikri Dhaifan, Adlan Dicky Hidayat Dillak, Vaya Juliana Dini, Farah Azzahra Divya Dwika Pramesti Dwi Fitrizal Salim Fajra Octrina Fatimah Kurniawati Putri Frima, Mariana Hanifa Fauzia Alza Hergijantono, Muhammad Rafi Imanuddin Hasbi Indrawan, Ida Bagus Agung Keysha Salsabila Saputra Khairunnisa Khairunnisa Kurniawati Kurniawati Leonie Syafira Mandasari, Riska Maulidia Isnaini Nauval Nurvian Mugni Nomba, Aditya Novanto Nugraha, Dandi Sukma Nur Sheha Gunawan, Ahmad Nurmayanti Alifia Nuslih Jamiat Putri Fariska Sugestie Putri Sulistyowati Putu Nina Madiawati Rafi Irbah Kusumawati Rahmadiva Dianitha Danial Rahmawati, Inge Refianti Andani Katiya Riska Mandasari Riswanda, Cecep Rivanza Dhia Ramadhan Rizky Wulan Suci Rizky Wulan Suci Salsabila Putri Yuantari Saragih, Yulia Wahyuni Sinta Dewi Suci Hayaty Tiara Indah Sukmasari Vincenthya Verena Viorentina Wahyuni Nur Syahril Widyandari, Regina Rhisanty Wiwin Aminah Yana Ayu Pradana Yana Ayu Pradana, Yana Ayu Yeny Ekaristi Purba Zhafira, Faza Fariha Zukhruf Ramadhani