|
View: |
Part 1: Document Description
|
|
Citation |
|
|---|---|
|
Title: |
Are Indonesian construction companies financially distressed? A prediction using artificial neural networks |
|
Identification Number: |
doi:10.34820/FK2/2MZ443 |
|
Distributor: |
Telkom University Dataverse |
|
Date of Distribution: |
2024-03-28 |
|
Version: |
1 |
|
Bibliographic Citation: |
Kristanti, Farida Titik; Safriza, Zahra; Salim, Dwi Fitrizal, 2024, "Are Indonesian construction companies financially distressed? A prediction using artificial neural networks", https://doi.org/10.34820/FK2/2MZ443, Telkom University Dataverse, V1 |
|
Citation |
|
|
Title: |
Are Indonesian construction companies financially distressed? A prediction using artificial neural networks |
|
Identification Number: |
doi:10.34820/FK2/2MZ443 |
|
Authoring Entity: |
Kristanti, Farida Titik (Telkom University) |
|
Safriza, Zahra (Telkom University) |
|
|
Salim, Dwi Fitrizal (Telkom University) |
|
|
Distributor: |
Telkom University Dataverse |
|
Access Authority: |
Salim, Dwi Fitrizal |
|
Depositor: |
Salim, Dwi Fitrizal |
|
Date of Deposit: |
2024-03-28 |
|
Study Scope |
|
|
Keywords: |
Business and Management, ANN, distress, financial, model, prediction, financial statements, balance sheet |
|
Abstract: |
Journal Scopus |
|
Methodology and Processing |
|
|
Sources Statement |
|
|
Data Access |
|
|
Notes: |
CC0 Waiver |
|
Other Study Description Materials |
|
|
Label: |
Farida et al 2023.pdf |
|
Notes: |
application/pdf |