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All Journal Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer
Sanrina Natalia Evelin Tolan
STIKOM Uyelindo Kupang
Computer Science & IT

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Ekstrasi Fitur Dan Kontur Pada Kain Tenun Sabu Menggunakan Metode GLCM (Gray Level Co-occurrence Matrix) Sanrina Natalia Evelin Tolan; Abraham Do Hina; Yampi R. Kaesmetan
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 3 (2024): Juni : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i3.99

Abstract

Sabu woven fabric is one of the cultural heritages of Sabu Island. In addition to being a cultural heritage, Sabu woven fabric is one of the handicrafts that still exist today which is preserved by Sabu women. Based on its manufacture, the classification process of Sabu woven fabric is based on color or motif identification. However, the classification process is not an easy process, because the classification process requires time and experts in the field of Sabu woven fabric. In addition to the classification process, the wider community also does not get much information about Sabu woven fabric clearly, because it is necessary to introduce the type of Sabu woven fabric, so that people can know or recognize the type of Sabu ikat woven fabric based on its type. Digital image processing techniques are utilized to build a system that can overcome the problems faced. Furthermore, image feature extraction will be carried out using gray level co-occurrence matrix (GLCM) with 4 features namely contrast, correlation, energy, and homogeneity with angles of 0°, 45°, 90°, and 135°. Each GLCM feature shows the same value even though the original image is rotated. After image feature extraction, the extracted data will be classified using the TensorFlow library. From these results it can be concluded that the program succeeded in selecting the type of Sabu ikat woven fabric class.
Co-Authors Abraham Do Hina Yampi R. Kaesmetan