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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Rizani Teguh
Program Studi Sistem Informasi, FIKR, Universitas Multi Data Palembang
Computer Science & IT

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Identifikasi Kadar Campuran Material pada Beton Keras Melalui Citra Menggunakan Jaringan Saraf Tiruan Propagasi Balik dengan Fitur LBP Gasim Gasim; Rusbandi Rusbandi; Rizani Teguh
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 4 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i4.3198

Abstract

Concrete can be found in permanent buildings, such as houses, buildings, bridges. Concrete is usually used for foundations, columns, beams, slabs. Concrete is a mixture of cement, fine aggregate, coarse aggregate and water. One of the determinants of the quality of concrete is the content of the concrete-forming mixture, so it is very important to know it. The problem arises about how to find out the mixture content in hardened concrete. Civil engineering discipline has a way to determine the content of the mixture forming the hardened concrete. However, it is possible to find out the level of this mixture using other disciplines, for example, from the discipline of computer science, in this case, artificial intelligence. Then, the problem is how to identify the mixture content in hardened concrete? This study uses the features of the Local Binary Pattern (LBP) image with the Artificial Neural Network (ANN) recognition method. There are 5 types of mixture used, each of which is represented by 5 samples of each type. Using a 14 MP camera, the shooting distance is approximately 27 cm. There are 1,000 training images from 3 samples of each type with 200 images each, and 500 test images from 2 samples from each type with 100 images each. The overall recognition accuracy rate is 67.6%. Keywords— Identification, Hardened Concrete, Local Binary Pattern, ANN
Co-Authors Gasim Rusbandi Rusbandi