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All Journal Sinergi Infolitika Journal of Data Science Journal of Engineering and Technological Sciences
Saputra, Huzair
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Enhancing the Red Wine Quality Classification Using Ensemble Voting Classifiers Supriatna, Deny Joefakri Iwa; Saputra, Huzair; Hasan, Khaidir
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.95

Abstract

This study introduces an ensemble voting classifier for red wine quality classification using machine learning algorithms. Wine quality assessment, traditionally reliant on subjective expert evaluations, is addressed through data-driven methodologies. The dataset comprises physicochemical attributes and quality ratings of red wines. Results reveal individual models with accuracy ranging from 0.816 to 0.873, while the ensemble approach significantly enhances accuracy. The combination of Random Forest and XGBoost achieves an accuracy of 0.885, demonstrating its potential in red wine quality assessment. In conclusion, this study showcases the potential of machine learning in enhancing the classification of red wine quality, offering a more objective and precise alternative to traditional sensory evaluation. The ensemble voting classifier, especially when combining Random Forest and XGBoost, provides a robust solution for this task, improving the accuracy of wine quality assessments.
Performance evaluation of hyper-parameter tuning automation in YOLOV8 and YOLO-NAS for corn leaf disease detection Saputra, Huzair; Muchtar, Kahlil; Chitraningrum, Nidya; Andria, Agus; Febriana, Alifya
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.018

Abstract

Corn cultivation was crucial in Southeast Asia, significantly contributing to regional food security and economies. However, leaf diseases posed a significant threat, causing substantial losses in production and quality. This research utilized artificial intelligence (AI) technology to address this issue by automating the hyper-parameter tuning process in YOLO (You Only Look Once) object detection models for early corn leaf disease detection. High-resolution images of corn leaves were captured and preprocessed for consistency. The preprocessing stage involved creating new dataset folders for images and labels, resizing images while preserving their aspect ratio, and rotating them if necessary. The images, containing 11,596 labeled instances, were analyzed using YOLOv8 and YOLO-NAS models. Each image's detected disease regions were converted into YOLO-format text files with x, y, width, and height coordinates, describing the presence and severity of infections. The models' performances were evaluated using precision, recall, mAP50, and mAP50-95 metrics. YOLOv8m achieved a mAP50 of 98.5% and mAP50-95 of 67.8%, while YOLO-NAS-L demonstrated superior detection capabilities with a mAP50 of 70.3% and mAP50-95 of 38.9%. This automated system facilitated early disease identification and enabled prompt preventive measures, thereby enhancing crop yields and mitigating losses. The findings highlighted the potential of advanced AI-driven detection systems in revolutionizing crop management and supporting global food security. 
Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8 Chitraningrum, Nidya; Banowati, Lies; Herdiana, Dina; Mulyati, Budi; Sakti, Indra; Fudholi, Ahmad; Saputra, Huzair; Farishi, Salman; Muchtar, Kahlil; Andria, Agus
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.1.5

Abstract

Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algorithms, namely YOLO-v5 and YOLO-v8, to detect infected corn leaves in the public data set called ‘Corn Leaf Infection Data set’ from the Kaggle repository. We compared the mean average precision (mAP) of mAP 50 and mAP 50-95 between YOLO-v5 and YOLO-v8. YOLO-v8 showed better accuracy at an mAP 50 of 0.965 and an mAP 50-95 of 0.727. YOLO-v8 also showed a higher detection number of 12 detections than YOLO-v5 at 11 detections. Both YOLO algorithms required about 2.49 to 3.75 hours to detect the infected corn leaves. This all-trained model could be an effective solution for early disease detection in future corn plantations.
Co-Authors Ahmad Fudholi Andria, Agus Banowati, Lies Budi Mulyati, Budi Chitraningrum, Nidya Farishi, Salman Febriana, Alifya Hasan, Khaidir Herdiana, Dina Indra Sakti Muchtar, Kahlil Supriatna, Deny Joefakri Iwa