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dc.contributor.advisorNababan, Anandhini Medianty
dc.contributor.advisorZamzami, Elviawaty Muisa
dc.contributor.authorTarihoran, Raynhard
dc.date.accessioned2025-03-13T01:47:12Z
dc.date.available2025-03-13T01:47:12Z
dc.date.issued2025
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/102045
dc.description.abstractUlos is a traditioal cloth typical of the Toba ethnic Batak tribe that is often used during various traditional ceremonies. Ulos has various types, each with different functions in its use. The motifs on each ulos also vary, although at first glance the look similar if not observed in detail. The problem that often arises is the public’s mistake in recognizing the type of ulos, so ulos are often considered the same without knowing the difference. To overcome this, this research aims to classify ulos to facilitate the identification of the types of Toba Batak ulos. This research compares the Visual Geometry Group (VGG16) and Residual Network (ResNet50) models in the classification process. The research was conducted on six types of Toba Batak ulos, namely Ulos Bintang Maratur, Ulos Mangiring, Ulos Ragi Hidup, Ulos Ragi Hotang, Ulos Sadum, and Ulos Sibolang, which are distinguished by the way they are made, namely manual and machine weaving. The test was conducted using images of ulos taken with a camera on full motifs without interference from other objects and full motifs with interference from other objects. The dataset used amounted to 3,189 data divided into training data and test data. The results showed that the classification model using VGG16 and ResNet50 both achieved maximum performance with accuracy, precision, recall, and F1-Score values of 1.0 on the Confusion matrix assessment. In testing on mobile applications, the VGG16 and ResNet50 models were able to identify each type of Toba Batak ulos well at close range. However, at longer distances, VGG16 showed superiority in distinguishing the types of ulos compared to ResNet50.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectUlosen_US
dc.subjectClassificationen_US
dc.subjectDeep Learningen_US
dc.subjectVisual Geometry Group (VGG16)en_US
dc.subjectResidual Network (ResNet50)en_US
dc.titlePerbandingan Arsitektur Vgg16 dan ResNet50 dalam Klasifikasi Jenis Ulos Batak Tobaen_US
dc.title.alternativeComparison of Vgg16 and ResNet50 Architectures in the Classification of Toba Batak Ulos Typesen_US
dc.typeThesisen_US
dc.identifier.nimNIM201401010
dc.identifier.nidnNIDN0013049304
dc.identifier.nidnNIDN0016077001
dc.identifier.kodeprodiKODEPRODI55201#Ilmu Komputer
dc.description.pages83 Pagesen_US
dc.description.typeSkripsi Sarjanaen_US
dc.subject.sdgsSDGs 4. Quality Educationen_US


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