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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Data and Science
Hayatou Oumarou
University of Maroua
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm Hayatou Oumarou; Nurul Rismayanti
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.
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.
Co-Authors Donatien Koulla Moulla Faouzi El Mansour kolyang Kolyang Nurul Rismayanti Nurul Rismayanti