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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MDP Student Conference
Rusbandi Rusbandi
Universitas Multi Data Palembang
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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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
KLASIFIKASI MONKEYPOX MENGGUNAKAN EKSTRAKSI FITUR LBP Gracivo Elsion Victory; Rusbandi Rusbandi; Siska Devella
MDP Student Conference Vol 2 No 1 (2023): The 2nd MDP Student Conference 2023
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (810.12 KB) | DOI: 10.35957/mdp-sc.v2i1.4369

Abstract

Monkeypox merupakan penyakit yang secara klinis sangap mirip dengan cacar air dan campak oleh karena itu orang-orang sulit membedakan monkeypox dan non-monkeypox. Metode ekstraksi fitur teksur yang efektif adalah Local Binary Pattern (LBP). Public dataset monkeypox yang digunakan dalam penelitian ini gambarnya berjumlah 3.192 dan berukuran 224x224 pixels. LBP menghasilkan Output feature vector dengan ukuran 1 x 59 sebagai input untuk metode random forest dengan nilai n_estimator yaitu 100, 500 dan 1000. Hasil pengujian citra monkeypox dibagi menjadi 3 tahap pengujian yaitu dengan proporsi dataset 60:40, 70:30, dan 80:20. Pada pengujian dengan proporsi 60:40 mendapatkan hasil terbaik pada dengan n_estimator 100 mendapatkan accuracy 83% . Pengujian dengan proporsi 70:30 mendapatkan accuracy 83% pada setiap n_estimator dan proporsi dataset 80:20 mendapatkan n_estimator terbaik yaitu 500 karna mandapatkan accuracy tertinggi dari ketiga pengujian dengan nilai 85%. Oleh karena itu dapat dilakukan klasifikasi Monkeypox dengan menggunakan fitur ekstraksi LBP dan Random Forest.
Co-Authors Devella, Siska Gasim Gracivo Elsion Victory Rizani Teguh