Temu Kembali Citra Tenun Nusa Tenggara Timur Menggunakan Ekstraksi Fitur yang Robust Terhadap Perubahan Skala, Rotasi dan Pencahayaan

Baso, Budiman (2019) Temu Kembali Citra Tenun Nusa Tenggara Timur Menggunakan Ekstraksi Fitur yang Robust Terhadap Perubahan Skala, Rotasi dan Pencahayaan. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Ragam motif pada tenun Nusa Tenggara Timur (NTT) seperti flora, fauna dan geometris menjadi suatu keunikan yang dapat membedakan daerah asal dan jenis dari tenun tersebut. Pada penelitian ini, sistem temu kembali citra berbasis isi atau Content-Based Image Retrieval (CBIR) diimplementasikan pada citra tenun NTT sehingga user dapat mencari citra tenun pada database menggunakan citra query berdasarkan fitur visual yang terkandung dalam citra. Seringkali citra query yang diinputkan user memiliki skala, rotasi dan pencahayaan yang bervariasi, sehingga diperlukan suatu metode ektraksi fitur yang dapat mengakomodasi variasi tersebut. Sistem temu kembali citra tenun pada penelitian ini menggunakan model Bag of Visual Words (BoVW) dari keypoints pada citra yang diekstrak dengan metode Speeded Up Robust Feature (SURF). BoVW dibangun menggunakan K-Means untuk menghasilkan visual vocabulary dari keypoints pada seluruh citra training. Representasi BoVW diharapkan dapat menangani variasi skala dan rotasi pada citra. Sedangkan untuk mengatasi variasi pencahayaan pada citra, dilakukan perbaikan kualitas citra dengan menggunakan Contrast Limited Adaptive Histogram Equalization (CLAHE). Hasil uji coba menunjukkan bahwa dengan penggunaan seluruh keypoint dengan 5000 cluster sistem mendapatkan akurasi rata-rata pada semua kondisi data citra query sebesar 97,74% sedangkan hasil precision yang diperolah yaitu 89,86% dengan waktu komputasi 9.94 detik.
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The variety of pattern on the tenun of East Nusa Tenggara (NTT) such as flora, fauna and geometric become a unique thing that can distinguish the origin and type of weaving. In this study, a content-based image retrieval (CBIR) system was implemented in NTT's tenun image so that users can search the images on the database using query images based on visual features contained in the image. Often the query image entered by the user has varying scale, rotation and lighting, so a feature extraction method is needed that can accommodate these variations. The tenun image retrieval system in this study used the Bag of Visual Words (BoVW) model of the keypoints in the extracted image using the Speeded Up Robust Feature (SURF) method. BoVW was built using K-Means to produce visual vocabulary from keypoints on all training images. The representation of BoVW is expected to be able to handle scale variations and rotations in images. Whereas to overcome the lighting variations in the image, image quality improvement is done by using Contrast Limited Adaptive Histogram Equalization (CLAHE). The results of the trial show that by using all keypoints with 5000 system clusters obtaining average accuracy on all conditions the query image data is 97.74% while the precision results obtained are 89.86% with a computation time of 9.94 seconds.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Content-Based Image Retrieval, Tenun Nusa Tenggara Timur, CLAHE, Bag of Visual Words, Speeded Up Robust Feature.
Subjects:	Q Science > Q Science (General) > Q337.5 Pattern recognition systems T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7882.P3 Pattern recognition systems T Technology > TR Photography T Technology > TR Photography > TR267.733.M85 Multispectral imaging Z Bibliography. Library Science. Information Resources > ZA Information resources > Z699.5 Information storage and retrieval systems
Divisions:	Faculty of Information and Communication Technology > Informatics > 55101-(S2) Master Thesis
Depositing User:	Budiman Baso
Date Deposited:	23 Jul 2026 03:35
Last Modified:	23 Jul 2026 03:35
URI:	http://repository.its.ac.id/id/eprint/66234

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