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All Journal Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Rekayasa elektrika Jurnal Polimesin Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Engineering and Technological Sciences
Muchtar, Kahlil
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : Sinergi

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Students’ emotion classification system through an ensemble approach Muhajir, Muhajir; Muchtar, Kahlil; Oktiana, Maulisa; Bintang, Akhyar
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.020

Abstract

Emotion is a psychological and physiological response to an event or stimulus. Understanding students' emotions helps teachers and educators interact more effectively with students and create a better learning environment. The importance of understanding students' emotions in the learning process has led to exploring the use of facial emotion classification technology. In this research, an ensemble approach consisting of ResNet, MobileNet, and Inception is applied to identify emotional expressions on the faces of school students using a dataset that includes emotions such as happiness, sadness, anger, surprise, and boredom, acquired from students of Darul Imarah State Junior High School, Great Aceh District, Indonesia. Our dataset is available publicly, and so-called USK-FEMO. The performance evaluation results show that each model and approach has significant capabilities in classifying facial emotions. The ResNet model shows the best performance with the highest accuracy, precision, recall, and F1-score, which is 86%. MobileNet and Inception also demonstrate good performance, indicating potential in handling complex expression variations. The most interesting finding is that the ensemble approach achieves the highest accuracy, precision, recall, and F1-score of 90%. By combining predictions from the three models, the ensemble approach can consistently and accurately address emotion variations. Implementing emotion classification models, individually and in an ensemble format, can improve teacher-student interactions and optimize learning strategies that are responsive to students' emotional needs. 
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. 
Co-Authors . Roslidar Ahmad Fudholi Alhabibie, Khalid Alhamdi, Muhammad Jurej Andria, Agus Anshari, Nyak Twoman Aulia Rahman Banowati, Lies Bintang, Akhyar Budi Mulyati, Budi Chairuman Chairuman Chitraningrum, Nidya Farishi, Salman Febriana, Alifya Herdiana, Dina Indra Sakti Isma, Teuku Reza Auliandra Khairul Munadi Maya Fitria Muhajir Muhajir, Muhajir Oktiana, Maulisa Razali, Safrizal Riki Effendi Rinaldi, Muhammad Hafiz Saddami, Khairun Saputra, Huzair Yassar, Muhammad Keysha Al Yudha Nurdin Yufnanda, Muhammad Aditya