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All Journal Jurnal Ilmu Komputer dan Informasi JOIV : International Journal on Informatics Visualization Makara Journal of Technology Jurnal Sistem Informasi
Heru Suhartanto
Fakultas Ilmu Komputer, Universitas Indonesia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering

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Journal : JOIV : International Journal on Informatics Visualization

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Assessing Data Imbalance in Financial Distress Prediction: A Comparative Approach of Machine Learning and Economic Models Rahayu, Dyah Sulistyowati; Suhartanto, Heru; Husodo, Zaäfri Ananto
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3397

Abstract

This study aims to compare the effectiveness of machine learning models and economic models in predicting corporate bankruptcy, with a focus on addressing the issue of data imbalance. In this context, the number of companies experiencing financial difficulties is significantly smaller than that of healthy companies, which can lead to bias in predictions. The method used is an experiment with various data handling techniques and involves several classification models, namely Decision Tree, Neural Network (NN), K-Nearest Neighbors (KNN), Case-Based Reasoning (CBR), Support Vector Machine (SVM), and Merton Structural Model, which are tested on several data scenarios with resampling techniques, including Random Oversampling (ROS), Random Undersampling (RUS), and a combination of both. The evaluation results show that the Decision Tree, excluding ROA variables, and the Neural Network provide the best performance, with the Decision Tree achieving 86% accuracy and an AUC of 77.75, and the Neural Network achieving 86.76% accuracy and an AUC of 90.5. Other models, such as KNN and SVM, exhibit lower performance, achieving around 80% accuracy and a lower AUC. Based on these results, Decision Tree without ROA and Neural Networks are the best choices for predicting corporate bankruptcy. This study also demonstrates that financial models, such as the Merton Structural Model, are not significantly affected by data imbalance. The ultimate goal of this study is to provide recommendations for more reliable prediction models that enable financial institutions, investors, and companies to make more informed strategic decisions, as well as reduce financial risks through the early detection of companies at risk of failure.
SCOV-CNN: A Simple CNN Architecture for COVID-19 Identification Based on the CT Images Haryanto, Toto; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Yusoff, Marina; Zain, Jasni Mohammad
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1750

Abstract

Since the coronavirus was first discovered in Wuhan, it has widely spread and was finally declared a global pandemic by the WHO. Image processing plays an essential role in examining the lungs of affected patients. Computed Tomography (CT) and X-ray images have been popularly used to examine the lungs of COVID-19 patients. This research aims to design a simple Convolution Neural Network (CNN) architecture called SCOV-CNN for the classification of the virus based on CT images and implementation on the web-based application. The data used in this work were CT images of 120 patients from hospitals in Brazil. SCOV-CNN was inspired by the LeNet architecture, but it has a deeper convolution and pooling layer structure. Combining seven and five kernel sizes for convolution and padding schemes can preserve the feature information from the images.  Furthermore, it has three fully connected (FC) layers with a dropout of 0.3 on each. In addition, the model was evaluated using the sensitivity, specificity, precision, F1 score, and ROC curve values. The results showed that the architecture we proposed was comparable to some prominent deep learning techniques in terms of accuracy (0.96), precision (0.98), and F1 score (0.95). The best model was integrated into a website-based system to help and facilitate the users' activities. We use Python Flask Pam tools as a web server on the server side and JavaScript for the User Interface (UI) Design
Co-Authors Ade Azurat Ade Azurat Aniati Murni Api Perdana Api Perdana Api Perdana Dyah Sulistyowati Rahayu Herry . Husodo, Zaafri Ananto Jasni Mohammad Zain Kusmardi Kusmardi Perdana, Api Rikky Wenang Purbojati Rikky Wenang Purbojati Rikky Wenang Purbojati Toto Haryanto Wenang Purbojati, Rikky Yusoff, Marina