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All Journal Kitektro JURIKOM (Jurnal Riset Komputer)
Ahmadiar Ahmadiar
Jurusan Teknik Elektro Dan Komputer, Fakultas Teknik, Universitas Syiah Kuala
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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SIMULASI ANTENA MIKROSTRIP RECTANGULAR PATCH ARRAY EMPAT ELEMEN UNTUK PENERIMA FPV 5,8 GHZ PADA WAHANA UAV Ferry Ferry; Syahrial Syahrial; Hubbul Walidainy; Ahmadiar Ahmadiar
Jurnal Komputer, Informasi Teknologi, dan Elektro Vol 6, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/kitektro.v6i2.21431

Abstract

Antena merupakan faktor utama pada sistem First Person View (FPV) untuk mentransmisi video secara real time dari Unmaned Aerial Vehicle (UAV) ke pengguna di Ground Control Station (GCS). Penelitian ini bertujuan untuk merancang antena mikrostrip rectangular patch array empat elemen menggunakan teknik pencatu Line Feed untuk penerima FPV 5,8 GHz, metode yang dipakai adalah simulasi dengan software Advanced Design System (ADS). Bahan yang digunakan Epoxy fiberglass FR4, ketebalan (h) = 1,6 mm, konstanta dielektrik (εr) = 4,5, dan Loss tangent = 0,018. Berdasarkan hasil simulasi diperoleh nilai return loss = -23,018 dB, VSWR = 1,152, gain = 9,442 dBi, bandwidth = 284 MHz serta memiliki pola radiasi directional.
Perbandingan Kinerja Deep Learning Dalam Pendeteksian Kerusakan Biji Kopi Yayang Hafifah; Kahlil Muchtar; Ahmadiar Ahmadiar; Shinta Esabella
JURIKOM (Jurnal Riset Komputer) Vol 9, No 6 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i6.5151

Abstract

Coffee is one of the most consumed beverages today. The coffee beans are first sorted by the farmers. This is because there are many types of coffee beans that differ in terms of shape and texture. After sorting, farmers must detect whether the coffee beans are damaged or not. The process is still done manually by coffee farmers so it takes a long time and results in errors due to lack of knowledge about coffee. In addition, efforts are also being made to improve the quality of the coffee beans which will affect the selling value of the coffee beans. Based on these problems, this study aims to design a deep learning model to detect coffee bean damage and evaluate the architecture of ResNet-34 and VGG-16. The classification model built using a Convolutional Neural Network (CNN) is expected to be able to know a better architecture and be able to detect damaged or normal coffee beans accurately and precisely
Co-Authors Ferry Ferry Hubbul Walidainy Kahlil Muchtar Shinta Esabella Syahrial Syahrial Yayang Hafifah