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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS)
Derwin Suhartono
Computer Science Department, Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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The influence of sentiment analysis in enhancing early warning system model for credit risk mitigation Karentia, Angel; Suhartono, Derwin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1829-1838

Abstract

One important source of bank income is interest income from credit activities, another part of which is obtained from fee-based income. Rapid credit growth is directly proportional to an increase in potential credit risk (counterparty default). In addition to comprehensive credit assessment at the initial stage of credit initiation, banks need to monitor the condition of existing debtors. Empirically, difficulties in handling non-performing loans often occur due to delays in detection and preparation of action plans. In this case, losses due to non-performing loans can have implications for the bank's reputation and worsen its financial performance. This research aims to determine the effect of sentiment analysis (external sentiment prediction model [positive, neutral, and negative] with certain keywords) on the level of accuracy of the early warning system (EWS) model in predicting the credit quality of bank debtors in the coming months. This study found that upgrading EWS with sentiment analysis will give better accuracy levels compared to traditional EWS models. In addition, the predictive power of EWS (traditional and upgraded) is inversely proportional to the prediction period, the longer the target prediction time, and the less predictive power of the EWS model.
Introversion-Extraversion Prediction using Machine Learning Fieri, Brillian; La'la, Joshua; Suhartono, Derwin
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1019

Abstract

Introversion and extroversion are personality traits that assess the type of interaction between people and others. Introversion and extraversion have their advantages and disadvantages. Knowing their personality, people can utilize these advantages and disadvantages for their benefit. This study compares and evaluates several machine learning models and dataset balancing methods to predict the introversion-extraversion personality based on the survey result conducted by Open-Source Psychometrics Project. The dataset was balanced using three balancing methods, and fifteen questions were chosen as the features based on their correlations with the personality self-identification result. The dataset was used to train several supervised machine-learning models. The best model for the Synthetic Minority Oversampling (SMOTE), Adaptive Synthesis Sampling (ADASYN), and Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) datasets was the Random Forest with the 10-fold cross-validation accuracy of 95.5%, 95.3%, and 71.0%. On the original dataset, the best model was Support Vector Machine, with a 10-fold cross-validation accuracy of 73.5%. Based on the results, the best balancing methods to increase the models’ performance were oversampling. Conversely, the hybrid method of oversampling-undersampling did not significantly increase performance. Furthermore, the tree-like models, like Random Forest and Decision Tree, improved performance substantially from the data balancing. In contrast, the other models, excluding the SVM, did not show a significant rise in performance. This research implies that further study is needed on the hybrid balancing method and another classification model to improve personality classification performance.
Image Captioning with Style Using Generative Adversarial Networks Setiawan, Dennis; Saffachrissa, Maria Astrid Coenradina; Tamara, Shintia; Suhartono, Derwin
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.709

Abstract

Image captioning research, which initially focused on describing images factually, is currently being developed in the direction of incorporating sentiments or styles to produce natural captions that reflect human-generated captions. The problem this research tries to solve the problem that captions produced by existing models are rigid and unnatural due to the lack of sentiment. The purpose of this research is to design a reliable image captioning model that incorporates style based on state-of-the-art SeqCapsGAN architecture. The materials needed are MS COCO and SentiCaps datasets. Research methods are done through literature studies and experiments. While many previous studies compare their works without considering the differences in components and parameters being used, this research proposes a different approach to find more reliable configurations and provide more detailed insights into models’ behavior. This research also does further experiments on the generator part that have not been thoroughly investigated. Experiments are done on the combinations of feature extractor (VGG-19 and ResNet-50), discriminator model (CNN and Capsule), optimizer (Adam, Nadam, and SGD), batch size (8, 16, 32, and 64), and learning rate (0.001 and 0.0001) by doing a grid search. In conclusion, more insights into the models’ behavior can be drawn, and better configuration and result than the baseline can be achieved. Our research implies that research in comparative studies of image recognition models in image captioning context, automated metrics, and larger datasets suited for stylized image captioning might be needed for furthering the research in this field.
A Systematic Literature Review of Different Machine Learning Methods on Hate Speech Detection Rudy Salim, Calvin Erico; Suhartono, Derwin
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.4.476

Abstract

Hate speech is one of the most challenging problem internet is facing today. This systematic literature review examine hate speech detection problem and will be used to do an experimental approach on detecting hate speech and abusive language. This work also provide an overview of previous research, including methods, algorithms, and main features used. We use two research questions in this literature review which will be the foundation of the next experimental research. Correctly classifying a piece of text as an actual hate speech requires a lot of correctly labelled data. Most common challenges are different languages, out of vocabulary words, long range dependencies and many more. 
Chest X-ray Image Classification to Identify Lung Diseases Using Convolutional Neural Network and Convolutional Block Attention Module Halim, Chandra; Eka Putra, Nathanael Geordie; Nugroho, Nico Ardian; Suhartono, Derwin
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1136

Abstract

Image classification, the process of categorizing and labeling groups of pixels or vectors within an image based on specific rules, is continuously developed by many researchers in the world to solve many problems. One of those problems is x-ray image classification to determine lung diseases. This research tries to solve the problem of classifying COVID-19, pneumonia, and healthy lungs using x-ray images. The image datasets were collected from several sources. This research aims to build a reliable and robust Convolutional Neural Network (CNN) enhanced with Convolutional Block Attention Module (CBAM) mechanism. CNN is used to do the feature extraction and the classification, whereas CBAM is used to improve the performance of the CNN by focusing on the important features in given data. Research methods are done through extensive data selection, preprocessing, and parameter tuning to achieve a well-performing model. While there is still a lack of research on x-ray classification using the attention mechanism, this research proposes it as the main method. This research also does a further experiment on the effect of the imbalanced dataset on the model. The evaluation is done using a cross-validation method. This research results reach 97.74% of accuracy, precision, recall, and f1-score. This research concludes that CBAM increases the performance of a CNN module. Using a larger dataset can be beneficial in this kind of research as well as evaluation by radiologists.
Co-Authors Agus, Mikhael Pramodana Alison, Richard Chandra Halim Christanno, Ivan Christian, Hans Ciputri, Marcella Marella Eka Putra, Nathanael Geordie Fieri, Brillian Jonathan, Welly Karentia, Angel La'la, Joshua Manik, Ngarap Imanuel Maulana, Jody Johansyah Nugroho, Nico Ardian Nursalim, Yunggih Ong, Veronica Permana, William Surya Priscilla, Priscilla Putra, Ferlan Gisman Putri Masaling, Nikita Ananda Rini Wongso Rudy Salim, Calvin Erico Saffachrissa, Maria Astrid Coenradina Saputra, Christian Eka Setiawan, Dennis Susilo, Stefanny Tamara, Shintia Wiguna, Antoni