Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network
The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architec...
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Universitas Mercu Buana
2025-01-01
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| Series: | Jurnal Ilmiah SINERGI |
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| Online Access: | https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/27016 |
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| author | Rudi Kurniawan Samsuryadi Samsuryadi Fatma Susilawati Mohamad Harma Oktafia Lingga Wijaya Budi Santoso |
| author_facet | Rudi Kurniawan Samsuryadi Samsuryadi Fatma Susilawati Mohamad Harma Oktafia Lingga Wijaya Budi Santoso |
| author_sort | Rudi Kurniawan |
| collection | DOAJ |
| description | The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of palm oil fruits, meticulously categorized into three classes: raw, ripe, and rotten. The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. Furthermore, AlexNet achieved superior precision, recall, and F-1 scores, each reaching 0.99, while the CNN scores were 0.98. These findings suggest that adopting AlexNet can enhance the palm oil industry's operational efficiency and product quality. The improved classification accuracy ensures that fruits are harvested at optimal ripeness, leading to better oil yield and quality. Reducing classification errors and manual labor can also lead to substantial cost savings and increased profitability. This study underscores the potential of advanced deep learning models like AlexNet in revolutionizing agricultural practices and improving industrial outcomes. |
| format | Article |
| id | doaj-art-6856efb3b74749029da61f5f47a1349a |
| institution | Kabale University |
| issn | 1410-2331 2460-1217 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Mercu Buana |
| record_format | Article |
| series | Jurnal Ilmiah SINERGI |
| spelling | doaj-art-6856efb3b74749029da61f5f47a1349a2025-01-13T04:38:19ZengUniversitas Mercu BuanaJurnal Ilmiah SINERGI1410-23312460-12172025-01-0129120722010.22441/sinergi.2025.1.0197916Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural NetworkRudi Kurniawan0Samsuryadi Samsuryadi1Fatma Susilawati Mohamad2Harma Oktafia Lingga Wijaya3Budi Santoso4Department of Computer System Engineering, Faculty of Engineering Science, Universitas Bina InsanDepartment of Informatics Engineering, Faculty of Computer Science, Universitas SriwijayaDepartment of Information Technology, Faculty of Informatics and Computing, University Sultan Zainal AbidinDepartment of Information System, Faculty of Engineering Science, Universitas Bina InsanDepartment of Informatics, Faculty of Engineering Science, Universitas Bina InsanThe palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of palm oil fruits, meticulously categorized into three classes: raw, ripe, and rotten. The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. Furthermore, AlexNet achieved superior precision, recall, and F-1 scores, each reaching 0.99, while the CNN scores were 0.98. These findings suggest that adopting AlexNet can enhance the palm oil industry's operational efficiency and product quality. The improved classification accuracy ensures that fruits are harvested at optimal ripeness, leading to better oil yield and quality. Reducing classification errors and manual labor can also lead to substantial cost savings and increased profitability. This study underscores the potential of advanced deep learning models like AlexNet in revolutionizing agricultural practices and improving industrial outcomes.https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/27016alexnetclassificationdeep learningfruit ripenessimage classificationpalm oil industry |
| spellingShingle | Rudi Kurniawan Samsuryadi Samsuryadi Fatma Susilawati Mohamad Harma Oktafia Lingga Wijaya Budi Santoso Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network Jurnal Ilmiah SINERGI alexnet classification deep learning fruit ripeness image classification palm oil industry |
| title | Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network |
| title_full | Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network |
| title_fullStr | Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network |
| title_full_unstemmed | Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network |
| title_short | Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network |
| title_sort | classification of palm oil fruit ripeness based on alexnet deep convolutional neural network |
| topic | alexnet classification deep learning fruit ripeness image classification palm oil industry |
| url | https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/27016 |
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