Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network
The ripeness of crystal guava fruit is currently sorted conventionally by analyzing the colour of the rind visually with the human eye. However, this method has several weaknesses that result in low accuracy and inconsistency. Therefore, automatic determination of ripeness level is necessary to incr...
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Udayana University, Institute for Research and Community Services
2025-01-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/115292 |
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| author | Shofia Nabila Azzahra Ahmad Kamsyakawuni Abduh Riski |
| author_facet | Shofia Nabila Azzahra Ahmad Kamsyakawuni Abduh Riski |
| author_sort | Shofia Nabila Azzahra |
| collection | DOAJ |
| description | The ripeness of crystal guava fruit is currently sorted conventionally by analyzing the colour of the rind visually with the human eye. However, this method has several weaknesses that result in low accuracy and inconsistency. Therefore, automatic determination of ripeness level is necessary to increase accuracy and obtain precise information. This research uses the HSI colour space as an interpretation of fruit image characteristics and uses the Backpropagation algorithm to perform classification. This study utilizes image data of crystal guava fruit, categorizing them into four stages of ripeness: unripe, half-ripe, ripe, and very ripe. There are 140 fruit image data with 35 data for each ripeness category. Each image will be processed with median filter, cropping and segmentation. The HSI value will be taken from the image and processed at the classification stage using the Backpropagation algorithm. In classification using Backpropagation Neural Network, the best network model in this study was achieved in the 3 10 4 network architecture with a binary sigmoid activation function, learning rate = 0.3, and batch size = 64. This model produces a loss value of 0.5364 with an accuracy of 0.9 in testing process. |
| format | Article |
| id | doaj-art-632084290951412f82134e0ea9849204 |
| institution | Kabale University |
| issn | 2088-1541 2541-5832 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj-art-632084290951412f82134e0ea98492042025-01-31T23:56:26ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322025-01-01150318619310.24843/LKJITI.2024.v15.i03.p04115292Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural NetworkShofia Nabila Azzahra0Ahmad Kamsyakawuni1Abduh Riski2Universitas JemberDepartment of Mathematics, Faculty of Mathematics and Natural Science, Jember UniversityDepartment of Mathematics, Faculty of Mathematics and Natural Science, Jember UniversityThe ripeness of crystal guava fruit is currently sorted conventionally by analyzing the colour of the rind visually with the human eye. However, this method has several weaknesses that result in low accuracy and inconsistency. Therefore, automatic determination of ripeness level is necessary to increase accuracy and obtain precise information. This research uses the HSI colour space as an interpretation of fruit image characteristics and uses the Backpropagation algorithm to perform classification. This study utilizes image data of crystal guava fruit, categorizing them into four stages of ripeness: unripe, half-ripe, ripe, and very ripe. There are 140 fruit image data with 35 data for each ripeness category. Each image will be processed with median filter, cropping and segmentation. The HSI value will be taken from the image and processed at the classification stage using the Backpropagation algorithm. In classification using Backpropagation Neural Network, the best network model in this study was achieved in the 3 10 4 network architecture with a binary sigmoid activation function, learning rate = 0.3, and batch size = 64. This model produces a loss value of 0.5364 with an accuracy of 0.9 in testing process.https://ojs.unud.ac.id/index.php/lontar/article/view/115292 |
| spellingShingle | Shofia Nabila Azzahra Ahmad Kamsyakawuni Abduh Riski Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network Lontar Komputer |
| title | Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network |
| title_full | Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network |
| title_fullStr | Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network |
| title_full_unstemmed | Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network |
| title_short | Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network |
| title_sort | determining the ripeness level of crystal guava fruit using backpropagation neural network |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/115292 |
| work_keys_str_mv | AT shofianabilaazzahra determiningtheripenesslevelofcrystalguavafruitusingbackpropagationneuralnetwork AT ahmadkamsyakawuni determiningtheripenesslevelofcrystalguavafruitusingbackpropagationneuralnetwork AT abduhriski determiningtheripenesslevelofcrystalguavafruitusingbackpropagationneuralnetwork |