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dc.contributor.authorHAKIM, Faruq Abdul
dc.date.accessioned2024-06-19T08:01:57Z
dc.date.available2024-06-19T08:01:57Z
dc.date.issued2024-01-09
dc.identifier.nim202410101064en_US
dc.identifier.urihttps://repository.unej.ac.id/xmlui/handle/123456789/121620
dc.description.abstractIn the machine learning field used for gender classification through facial images, feature extraction is one of the inseparable parts. Several types of features can be extracted from images, such as texture features. In previous gender classification studies, LBG-VQ and MB-LBP can be used to extract texture features from images. When applied to the FEI facial images dataset, LBG-VQ produces suboptimal performance. Meanwhile, when applied to the FERET facial images dataset, MB-LBP produces optimal performance. Therefore, this study was conducted to discover the gender classification performance of the LBG-VQ and MB-LBP methods on the FEI facial images dataset. This study was carried out by implementing the two feature extraction methods separately or in combination with the two. The results of feature extraction were implemented into several classification methods, namely Naive Bayes, SVM, KNN, Random Forest, and Logistic Regression. The K-Fold Cross Validation was used to evaluate the model by splitting the data into 9 folds of data training and 1 fold of data testing. The combination of the Logistic Regression with MB-LBP 12x12 on LBG-VQ quantized images forms the machine learning model with the most optimal performance at 91,928%. To get the most optimal performance there are 3 preprocessing processes, namely noise removal, illumination adjustment, and image conversion to grayscale. Furthermore, there is also a second scenario of preprocessing that consists of first preprocessing processes with additional processes, namely face cropping and size normalization. The best optimal results with the second scenario of preprocessing is at 89,977% using SVM Poly Kernel and MB-LBP 12x12 on LBG-VQ quantized images. In conclusion, roundly the implementation of MB-LBP tends to show an increase in performance than the implementation of LBG-VQ.en_US
dc.language.isootheren_US
dc.publisherFakultas Ilmu Komputeren_US
dc.subjectMachine Learningen_US
dc.subjectGender Classificationen_US
dc.subjectLBG-VQen_US
dc.subjectMB-LBPen_US
dc.titleOptimasi Performa Klasifikasi Jenis Kelamin Berdasarkan Citra Wajah Menggunakan Metode Ekstraksi Fitur LBG-VQ dan MB-LBPen_US
dc.typeSkripsien_US
dc.identifier.prodiSistem Informasien_US
dc.identifier.pembimbing1Tio Dharmawan, S.Kom., M.Kom.en_US
dc.identifier.pembimbing2Muhamad Arief Hidayat, S.Kom., M.Kom.en_US
dc.identifier.validatorrevaen_US
dc.identifier.finalization0a67b73d_2024_06_tanggal 19en_US


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