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All Journal Rotasi Mechatronics, Electrical Power, and Vehicular Technology JURNAL TEKNIK MESIN Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Rekayasa Mesin Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknik Mesin Indonesia Journal of Robotics and Control (JRC)
Joga Dharma Setiawan
Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro, Jl. Prof. Soedarto, SH, Tembalang, Semarang, Jawa Tengah, 50275
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Simulasi Dinamika dan Uji Penerimaan Komunikasi Antena Penjejak Satelit POES Setiawan, Joga Dharma; Haryanto, Ismoyo; Kurnianto, Andhieka Rizky
ROTASI Vol 26, No 2 (2024): VOLUME 26, NOMOR 2, APRIL 2024
Publisher : Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/rotasi.26.2.53-61

Abstract

Studi ini bertujuan untuk memvalidasi gerak penjejak satelit orbit bumi rendah (LEO) dengan perangkat lunak Systems Tool Kit (STK) dan membandingkan gerakan azimuth dan elevasi dengan data dari website penyedia penjejak satelit seperti www.n2yo.com, lalu membuat model animasi dalam Matlab/Simulink menggunakan data simulasi dari software STK dan menentukan lintasan sudut azimuth yang diinginkan serta memastikan gerakan antena dalam batas-batas ruang yang tersedia secara aktual. Selanjutnya sistem kontrol antena penjejak satelit ini diimplementasikan untuk menentukan hubungan antara durasi komunikasi data dengan maksimum elevasi, daya yang diterima dan menghasilkan gambar permukaan bumi dari hasil penerimaan komunikasi data. Penelitian ini menggunakan fasilitas yang disediakan oleh satelit LEO POES (Polar-Orbiting Environmental Satellites) yaitu NOAA-15, 18 and 19. Secara khusus hasil uji coba penerimaan transmisi gambar dari NOAA-18, telah berhasil didapatkan beberapa gambar untuk ditampilkan, yaitu gambar asli berwarna hitam dan putih, contrast A, MSA (multi spectral analysis), suhu permukaan laut, dan thermal.
Accuracy Assessment of Azimuth, Elevation, and Time data resulted from using actual TLE and simulated-outdated TLE for tracking LAPAN-A2 Satellite Setiawan, Joga Dharma; Ilham F, Muhammad Ario; Haryanto, Ismoyo; Muhida, Riza
ROTASI Vol 25, No 4 (2023): VOLUME 25, NOMOR 4, OKTOBER 2023
Publisher : Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/rotasi.25.4.%p

Abstract

This study aims to assess the pointing accuracy of a directional antenna at a ground station using outdated Two-Line Element sets (TLEs) to track an equatorial Low-Earth Orbit (LEO) satellite, LAPAN-A2,  using AGI System Tool Kit (STK) software by analyzing the Azimuth, Elevation, and Time-lapse (AET). Simulations were performed using 1, 3, 6, and 12-month outdated TLEs.  Validation was successfully conducted at the initial work by comparing the orbital parameter data extracted from n2yo.com  to the STK simulation results using an updated TLE. It was found that maximum pointing errors using 1, 3, 6, and 12-month outdated TLEs, respectively, were: for the Azimuth 32º, 38º, 167º, and greater than 173º; for the Elevation: 5º, 8º, 7º, and greater than 73º; for the Time-lapse: 24, 35, 488, and 1052 seconds. It can be argued that the maximum pointing errors for up to the three-month outdated TLE are considered small such that a good communication quality between the ground station with a standard directional antenna could still be feasible. However the results of this study is limited only for a selected location in Central Java province in Indonesia. These findings have important implications for real-time satellite tracking when the ground station location becomes remote and disconnected from the internet network due to certain circumstances, such as natural disasters or other kinds of disruption, in which updating TLEs is not possible for a while.
Pengaruh Bilangan Reynold Pada Optimasi Airfoil Dengan Metode Panel Dan Algoritma Genetika Haryanto, Ismoyo; Rozi, Khoiri; Setiawan, Joga Dharma
ROTASI Vol 26, No 2 (2024): VOLUME 26, NOMOR 2, APRIL 2024
Publisher : Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/rotasi.26.2.62-72

Abstract

Pengaruh bilangan Reynold pada optimasi airfoil telah dikaji. Pada penelitian ini analisis aerodinamika airfoil dilakukan menggunakan metode panel sedangkan optimasi dilakukan dengan Algoritma Genetika (GA). Sedangkan sebagai pembangkit geometri airfoil digunakan transformasi Joukowski. Dengan transformasi ini geometri airfoil dapat diperoleh dengan mentransformasikan bentuk lingkaran dengan koordinat titik pusat tertentu. Adapun analisis karakteristik aerodinamikanya dilakukan dengan menggunakan metode panel dimana aliran dianggap bersifat tak viskos (inviscid) dan tak mampat (incompressible). Pada penelitian ini efek viskositas dikaji dengan menerapkan metode interaksi viskos-tak viskos. Untuk keperluan tersebut analisis lapisan batas (boundary layer) dilakukan untuk mendapatkan tebal perpindahan (displacement thickness). Informasi tebal perpindahan ini selanjutnya digunakan untuk update geometri airfoil. Langkah berikutnya adalah optimasi guna mendapatkan geometri airfoil yang mempunya rasio gaya angkat terhadap gaya hambat maksimum terbesar dengan koordinat titik pusat lingkaran sebagai variabel perancangan. Sebagai pengoptimum (optimizer) dipilih GA karena algoritma ini mampu memberikan solusi optimum global. Pada penelitian ini optimasi dilakukan untuk beberapa harga bilangan Reynold. Dari hasil yang diperoleh menunjukkan bahwa analisis karakteristik aerodinamika menggunakan metode panel dengan melibatkan interaksi viskos-tak viskos memberikan hasil yang cukup akurat terhadap hasil eksperimen. Sedangkan hasil optimasi menunjukkan bahwa kondisi optimum sangat dipengaruhi oleh bilangan Reynold. Sekalipun secara umum hasil optimasi yang diperoleh cukup baik akan tetapi memerlukan validasi dan variasi lebih lanjut
Design of image classification system for fabric inspection process using Raspberry Pi Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; Munadi, Munadi; Diki, Diki
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.863

Abstract

This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %.
Non-linear model predictive control with single-shooting method for autonomous personal mobility vehicle Pratama, Rakha Rahmadani; Baskoro, Catur Hilman Adritya Haryo Bhakti; Setiawan, Joga Dharma; Dewi, Dyah Kusuma; Paryanto, Paryanto; Ariyanto, Mochammad; Saputra, Roni Permana
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1105

Abstract

The advancement of autonomous vehicle technology has markedly evolved during the last decades. Reliable vehicle control is one of the essential technologies in this domain. This study aims to develop a proposed method for controlling an autonomous personal mobility vehicle called SEATER (Single-passenger Electric Autonomous Transporter), using Non-linear Model Predictive Control (NMPC). We propose a single-shooting technique to solve the optimal control problem (OCP) via non-linear programming (NLP). The NMPC is applied to a non-holonomic vehicle with a differential drive setup. The vehicle utilizes odometry data as feedback to help guide it to its target position while complying with constraints, such as vehicle constraints and avoiding obstacles. To evaluate the method's performance, we have developed the SEATER model and testing environment in the Gazebo Simulation and implemented the NMPC via the Robot Operating System (ROS) framework. Several simulations have been done in both obstacle-free and obstacle-filled areas. Based on the simulation results, the NMPC approach effectively directed the vehicle to the desired pose while satisfying the set constraints. In addition, the results from this study have also pointed out the reliability and real-time performance of NMPC with a single-shooting method for controlling SEATER in the various tested scenarios.
Penerapan Teachable Machine Dan Raspberry Pi Pada Sistem Klasifikasi Citra Untuk Inspeksi Cacat Kain Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; M, Munadi; Rustiyanti, Alifa
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20251218932

Abstract

Industri tekstil memainkan peran krusial dalam ekonomi nasional, menghadapi tantangan signifikan dalam menjaga kualitas produk untuk memenuhi kepuasan konsumen. Cacat produksi, seperti cacat jarang dan cacat slap pada kain, merupakan salah satu faktor utama yang mempengaruhi kualitas produk tekstil. Penelitian ini bertujuan untuk mengembangkan sistem inspeksi cacat kain secara otomatis dengan menggunakan metode pemrosesan citra digital dan machine learning. Sistem ini dirancang untuk diintegrasikan pada mesin penggulungan kain sebagai sistem inspeksi awal sebelum kain didistribusikan. Metode yang digunakan meliputi supervised learning untuk klasifikasi citra kain, memanfaatkan perangkat lunak Google Teachable Machine dan algoritma Convolutional Neural Network (CNN) yang diimplementasikan dengan OpenCV. Perangkat keras yang digunakan terdiri dari kamera web Logitech D320 untuk akuisisi gambar dan Raspberry Pi-3B sebagai pengolah citra. Sistem ini diuji untuk mendeteksi tiga kategori kain: kain bagus, cacat jarang, dan cacat slap. Hasil pengujian menunjukkan bahwa sistem memiliki rata-rata waktu inferensi sebesar 142,47 ms dengan kecepatan rata-rata 6,46 frame per detik (FPS) dan akurasi klasifikasi mencapai 98,48%. Dengan implementasi sistem ini, diharapkan dapat meningkatkan efisiensi produksi, memperkuat kontrol kualitas di industri tekstil, mengurangi intervensi manual, dan menurunkan potensi kerugian akibat produk cacat.   Abstract The textile industry plays a crucial role in the national economy, facing significant challenges in maintaining product quality to meet consumer satisfaction. Production defects, such as rare defects and slap defects in fabrics, are key factors that affect the quality of textile products. This research aims to develop an automated fabric defect inspection system using digital image processing and machine learning methods. The system is designed to be integrated into fabric winding machines as an initial inspection system before the fabric is distributed. The methods used include supervised learning for fabric image classification, utilizing Google Teachable Machine software and the Convolutional Neural Network (CNN) algorithm implemented with OpenCV. The hardware used consists of a Logitech D320 webcam for image acquisition and a Raspberry Pi-3B as the image processor. The system was tested to detect three categories of fabric: good fabric, rare defects, and slap defects. The test results showed that the system achieved an average inference time of 142.47 ms with an average speed of 6.46 frames per second (FPS) and a classification accuracy of 98.48%. With the implementation of this system, it is expected to enhance production efficiency, strengthen quality control in the textile industry, reduce manual intervention, and decrease potential losses due to defective products.
Co-Authors AA Sudharmawan, AA Achmad Widodo Agus Mukhtar Anggi Arivian Arifin, Zainal Ariyanto, Mochammad Aulia Syukri Aulia, Dito Rahmat Baskoro, Catur Hilman Adritya Haryo Bhakti Budi Setiyana D. Michael Hendra Damar Yusuf Sutrisno Diki, Diki Dimas Bimo Nugroho Dwiyanto, Yahya Fikri Dyah Kusuma Dewi Eden Wawi Putra Eflita Yohana Emmanuel Agung Nugroho Gunawan Dwi Haryadi Hanif Muhammad Rakha Hujjatul Anam I Made Wicaksana Ekaputra Ilham F, Muhammad Ario Ismoyo Haryanto J. Jamari Khoiri Rozi Kurnianto, Andhieka Rizky La Ode Muhamad Munadi M, Munadi M.Pd S.T. S.Pd. I Gde Wawan Sudatha . Mochammad Ariyanto Mochammad Ariyanto Mochammad Ariyanto Moh. Arozi Muhammad Ritzky Novhar Munadi Munadi Munadi Munadi Nana Supriyana Nugraha, Faryan Nurminda Eka Indra Pramana Paryanto Paryanto Paryanto Petrus Londa Pratama, Rakha Rahmadani Purwiantoro, Arman Rifka Hana Indraswari Rifky Ismail Riza Muhida Roni Permana Saputra RR. Ella Evrita Hestiandari Rusnaldy Rusnaldy Rustiyanti, Alifa Setiawan, Muhammad Farouk Toni Prahasto Wahyu Caesarendra Yuwana, Rizkia Wira Zainal Arifin