DETEKSI OBJEK PENGENALAN TINGKAT KEMATANGAN TANDAN BUAH SEGAR KELAPA SAWIT MENGGUNAKAN CENTERNET BERBASIS ANDROID

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dc.contributor.authorSatria, Muhamad Ilsyam
dc.contributor.supervisorBahri, Zaiful
dc.date.accessioned2023-08-21T03:00:46Z
dc.date.available2023-08-21T03:00:46Z
dc.date.issued2023-06
dc.description.abstractThe maturity level of oil palm Fresh Fruit Bunches (FFB) is a determining factor for the quality of Crude Palm Oil (CPO). The sorting method after harvest or before entering the boiling process is generally done manually relying on vision and experience. This method is prone to subjective errors. Imaging methods are developing rapidly due to advances in computers and image processing techniques, particularly for sorting and grading systems. CenterNet is an architecture for object detection that is significantly enhanced with the help of deep learning, CenterNet is a method for exploring the center of a geometric grid. Plus a keypoint. This study used 18,000 FFB image data, the model can detect the maturity level of oil palm from five maturity levels, namely ripe 1 in the black-red color category, ripe 2 in the red color category, ripe 3 in the red-yellow color category, in the yellow-orange category, and tanko with gray color category. The performance of the results of the model has an accuracy rate of 88%.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alamen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.otherElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/11122
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectCenterNeten_US
dc.subjectFFBen_US
dc.subjectObject Detectionen_US
dc.subjectPalm Oilen_US
dc.titleDETEKSI OBJEK PENGENALAN TINGKAT KEMATANGAN TANDAN BUAH SEGAR KELAPA SAWIT MENGGUNAKAN CENTERNET BERBASIS ANDROIDen_US
dc.typeArticleen_US

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