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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Informatika Global Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication
Gasim
Universitas Indo Global Mandiri
Computer Science & IT Engineering Social Sciences Other

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Journal : JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI

 1 
Identifikasi Kadar Semen dan Pasir Melalui Citra Permukaan Menggunakan Teknik Blok Citra Gasim Gasim
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 7 No 2 (2020): 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.v7i2.371

Abstract

This research raises the topic of identifying the types of cement and sand mixtures on dry material using artificial intelligence. This is done because the comparison of the mixture between cement and sand is very influential on the quality of the material produced. Several experimental models affect the level of recognition accuracy. In this study the experimental model used was the image block and LBP image techniques, with a mixture of cement and sand used was 1: 1, 1: 1.5, 1: 2, 1: 2.5, 1: 3, and 1: 3.5. The recognition method used is Artificial Neural Network (ANN) with back propagation algorithm. The number of ANN training samples is 600 samples, and 120 samples for testing. This research uses image block technique before feature extraction is carried out. The features used are the mean, standard deviation, entropy, skewness, and kurtosis of LBP images. ANN training results get a three-layer hidden architecture, with testing showing an accuracy rate of 80% recognition.
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 David Ricardo Derry Alamsyah Dewi Sartika Dicky Pratama Dina Agustina Dini Amputri Edgar Utama Erick Brilliant Wilsen Erven Tanjungan Evi Purnamasari Fika Rusilawati Fikario Yapputra Hardianing Wahyu Kinasih Hartati, Ery Inayatullah Inayatullah Indah Permatasari Jesslyn K Kevita Titany Suhanto Leonardo Chandra B Michael Michael Muhammad Afif Naufal Muhammad Azwar Tsar Siregar Muhammad Ezar Al Rivan Mulia Octavia Nurdiana Dewi Saputri Ricoida, Desy Iba Rizani Teguh Rizani Teguh Rudi Heriasyah Rusbandi Rusbandi Rusbandi Rusbandi Rycci Juniawan Satria A Siti Nadra Sudiadi Sudiadi Sudiadi Sudiadi Sudiadi, Sudiadi Tinaliah, Tinaliah Triana Elizabeth, Triana Yoannita Yoannita Zaid Romegar Mair