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All Journal Teknika Jupiter Media Penelitian dan Pengembangan Kesehatan
Indera Indera
Departemen Neurology, Fakultas Kedokteran Universitas Gajah Mada
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Public Health

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Evaluasi Kinerja Model YOLOv8 dalam Deteksi Kesegaran Buah ardiansyah, arie; Triloka, Joko; indera, indera
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 2 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.11296226

Abstract

A The classification of fruit freshness has significant implications in the food and agriculture industry in Indonesia. Fruit freshness not only affects the quality of the products sold but is also crucial in ensuring consumer safety and reducing food wastage. Non-fresh fruits can become a source of microbial contamination and can cause food poisoning if consumed. Based on these factors, various image processing technologies such as ResNet, DenseNet, MobileNetV2, NASNet, and EfficientNet, CNN, DCNN, etc., have been developed to assist the industry in real-time and efficient classification and detection of fruit freshness. Therefore, this research aims to explore another algorithm, namely YOLOv8, to evaluate the accuracy and precision performance in detecting fruit freshness. In this study, it was found that the YOLOv8 model with 100 epochs and a batch size of 8 yielded a confusion matrix result with an accuracy of 88%. At high confidence levels, the model was able to detect fruit freshness with an average precision of 97%. However, at low confidence levels, the average recall reached 87%. At a balanced confidence level (0.50), the precision and recall F1-Score reached an average of 73%. Evaluation beyond the confidence level showed a precision of 74% and a recall of 75%. These results indicate that the YOLOv8 model has not yet reached an optimal level compared to other algorithms that have an accuracy above 90%. This could possibly be due to the limited number
Kamera Pintar Untuk Pengawasan Penggunaan Masker Di Rumah Sakit Linda, Deppi; Agus, Isnandar; Indera, Indera; Zulkarnaini, Zulkarnaini
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 2 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.12595867

Abstract

A Hospitals have a high potential as sources for the spread of infectious diseases, including viruses and bacteria. The use of masks by medical staff, patients, and visitors is a crucial measure to minimize the risk of infection. However, manually monitoring mask compliance in hospitals is challenging. With the development of artificial intelligence, automated monitoring systems can be implemented to more efficiently and effectively monitor mask compliance. This study employs YOLOv8 and IP Cameras for mask detection in hospitals. The dataset used consists of 2130 training images, 34 validation images, and 27 test images. The model was trained with parameters of 300 epochs, a batch size of 8, and a patience of 128 to prevent overfitting. Experimental results indicate that the model achieved a precision and recall of 98.3%, with an overall accuracy of 97%. The Precision-Recall and F1-Confidence curves demonstrate that the model is highly effective in detecting mask usage with minimal errors. The confusion matrix indicates that 97% of all mask usage cases were correctly detected, while only 3% were missed. This YOLOv8 and IP Camera-based mask detection system shows great potential for application in hospitals, enhancing mask compliance and aiding in the prevention of disease spread
Co-Authors Abdul Gofir Agus Rahardi, Agus Agus, Isnandar Anastiwi, Vitha Arie Ardiansyah, Arie Deppi Linda Dona Yuliawati Fikri, Ruki Rizal Nul Kristian, Agustinus Muchirdyantoro, Dede Nursiyanto Ochi Marshella Febriani Sushanty Saleh Sutaqwa, Eling Tengku Riza Zarzani N Triloka, Joko Wasillah, Wasillah Zakaria, Asrul