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All Journal Indonesian Journal of Data and Science International Journal of Artificial Intelligence in Medical Issues The Indonesian Journal of Computer Science
Hayatou Oumarou
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Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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M2SmallLint : software health monitoring tool Hayatou Oumarou; Nurul Rismayanti
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.90

Abstract

Developing error-free applications is a major challenge for computer scientists. Tools to remedy this problem have been developed, notably Rule Checkers and proof assistants. As a particular case of error, a bug is by nature intangible, invisible and difficult to trace. We propose to investigate the correlations between the alerts generated by rule checkers and the internal quality of the software system. In this first version of the work, we present M2SmallLint, a tool for visualizing and navigating through source code properties in order to locate potential errors. This tool enables the visualization of software health.
Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm Hayatou Oumarou; Rismayanti, Nurul
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.115

Abstract

The study "Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm" investigates the potential of image processing and machine learning techniques in the classification of empon plants, specifically ginger and turmeric. Utilizing a dataset of leaf images, the research employed the Canny method for image segmentation and Hu Moments for feature extraction, followed by classification using the K-Nearest Neighbors (K-NN) algorithm. The performance of the model was evaluated through a 5-fold cross-validation method, focusing on metrics such as accuracy, precision, recall, and F1-score. The results showcased the model's variable performance, with the highest accuracy reaching 65.33%. The study contributes to the field by demonstrating the application of Hu Moments in plant classification and by assessing the K-NN algorithm's effectiveness in this context. These findings offer insights into the potential of combining image processing techniques with machine learning for accurate plant classification, paving the way for further research in the area.
Classification of Rice Grain Varieties Using Ensemble Learning and Image Analysis Techniques Setiawan, Rudi; Hayatou Oumarou
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.129

Abstract

This research explored the efficacy of machine learning techniques, specifically the Bagging meta-estimator, in the classification of rice grain images. Utilizing a dataset composed of 45,000 images of Arborio, Basmati, and Jasmine rice varieties, a 5-fold cross-validation was employed to evaluate the model's performance. The results were highly promising, with the model consistently achieving over 96% in accuracy, precision, recall, and F1-score across all folds, indicating its robustness and reliability. The study confirmed that ensemble learning techniques could significantly improve the classification accuracy over single classifier systems in agricultural applications. The findings offer a significant contribution to automated agricultural processes, suggesting that machine learning can greatly enhance the efficiency and precision of rice variety classification. These results pave the way for further research into the integration of such models into agricultural quality control and provide a foundation for the exploration of advanced image processing and deep learning techniques for improved performance. Future research directions include expanding the model to encompass a wider variety of crops and integrating additional data modalities to refine classification accuracy further. Practical applications should explore the incorporation of this technology into existing agricultural systems to maximize the benefits of automation in quality control.
Detection and Classification of Bacterial Skin Infections Using K-Nearest Neighbors Algorithm Hayatou Oumarou
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.149

Abstract

Bacterial skin infections, including cellulitis and impetigo, pose significant health challenges requiring timely and accurate diagnosis for effective treatment. This research aims to develop an automated classification system for these infections using image processing and machine learning techniques. The study utilizes the Sobel method for image segmentation and Hu Moments for feature extraction. The classification is performed using the K-Nearest Neighbors (K-NN) algorithm with . The dataset, sourced from Kaggle, consists of imbalanced images of the two infection types. After pre-processing and feature extraction, the dataset is scaled to zero mean and unit variance. The model's performance is evaluated using cross-validation, yielding mean accuracy, precision, recall, F1-score, and ROC-AUC values of 65.95%, 65.18%, 65.95%, 63.06%, and 64.13%, respectively. Visualizations, including scatter plots, boxplots, histograms, correlation heatmaps, PCA, t-SNE, and UMAP, provide insights into the feature distributions and separability of classes. The results indicate that the combination of Sobel segmentation, Hu Moments, and K-NN can effectively classify bacterial skin infections. The study's contributions include demonstrating the applicability of these techniques to dermatological diagnostics and highlighting the potential for improved diagnostic accuracy and efficiency. However, the study acknowledges limitations such as data imbalance and variability in performance, suggesting the need for further research using advanced models like convolutional neural networks (CNNs) and enhanced data pre-processing techniques. These findings underscore the importance of machine learning in developing practical tools for clinical use, ultimately improving patient outcomes through early and accurate diagnosis.
Classification of Pseudopapilledema and Papilledema Using Decision Tree and Hu Moments Hayatou Oumarou
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v2i2.176

Abstract

Pseudopapilledema, characterized by an anomalous elevation of the optic disc without retinal nerve fiber layer edema, often mimics the presentation of true papilledema caused by increased intracranial pressure. Accurate differentiation between these conditions is critical to avoid unnecessary invasive procedures. This study employs a Decision Tree classifier to classify optic disc images into three categories: normal, papilledema, and pseudopapilledema. The dataset, obtained from Kaggle, consists of imbalanced images segmented using the Canny edge detection method and features extracted using Hu Moments. The dataset was divided into 80% training and 20% testing sets. Performance was evaluated using 5-fold cross-validation, yielding an average accuracy of 53.61%, precision of 55.20%, recall of 54.12%, and F1-score of 55.17%. The study provides a comprehensive analysis of the classifier's performance, including visualizations such as segmentation results, scatter plots of Hu Moments, and confusion matrices. The results indicate that while the Decision Tree classifier demonstrates moderate effectiveness, there is significant room for improvement. The research highlights the potential of machine learning models in medical diagnostics but also underscores the need for more robust algorithms and diverse datasets. Future work should focus on incorporating more complex models and expanding the dataset to enhance diagnostic accuracy. These findings contribute to the field of medical image analysis and propose a non-invasive diagnostic tool that, when integrated with clinical expertise, could improve patient outcomes and reduce unnecessary procedures
Automatic Generation of Unit Test Data for Dynamically Typed Languages Hayatou Oumarou; El Mansour, Faouzi
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3396

Abstract

Testing is the major means of verifying and validating software. It is a repetitive and time-consuming activity. Testing is neglected because of its high cost and the fact that it does not add functionality to the system. As a result, many programmers don't write tests. To remedy this, some researcher proposed automatic test generation. Test generation is a solution that reduces workload and increases productivity. In this paper, we propose a test data generation approach for unit tests in dynamically typed languages. Our approach is based on the analysis and decomposition of the AST (Abstract Syntax Tree) obtained when compiling the source code of the method under test. We validate this approach in Pharo a real system. The results on three systems show the effectiveness of the approach.
Co-Authors El Mansour, Faouzi Nurul Rismayanti Rismayanti, Nurul Rudi Setiawan