English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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). Percobaan dilakukan dengan membandingkan kinerja dari representasi BoVW yang dibangun menggunakan fitur SURF dengan  Maximally Stable Extremal Regions  (MSER) pada temu kembali citra tenun. Hasil uji coba menunjukkan bahwa metode SURF menghasilkan rata-rata akurasi 89,86% dan waktu komputasi 9,94 detik, sedangkan MSER menghasilkan rata-rata akurasi 84,04% dan waktu komputasi 1,95 detik.       AbstractThe variety of motifs in East Nusa Tenggara tenun such as flora, fauna and geometric is an unique thing that can distinguish the region of origin and type of the tenun. In this study, the Content-Based Image Retrieval (CBIR) system is implemented in the tenun image. With Content-based techniques Users can search tenun images on the image database by using query images based on visual features contained in the image. Often the query image that the user enters has a different scale, rotation and lighting, so a feature extraction method is needed that can accommodate these differences. 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 experiment was conducted by comparing the performance of the BoVW representation which was built using the SURF feature with Maximally Stable Extremal Regions (MSER) at the tenun image retrieval. The results of the trial showed that SURF obtained higher accuracy in all conditions of tenun image data with an average value of 89.86% whereas MSER obtained an average accuracy value of 84.04%. But MSER's computation time is 1.95 seconds faster than SURF which is 9.94 seconds.

Temu Kembali Citra Tenun Nusa Tenggara Timur menggunakan Esktraksi Fitur yang Robust terhadap Perubahan Skala, Rotasi, dan Pencahayaan