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All Journal TEKNIK Mechatronics, Electrical Power, and Vehicular Technology Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Jurnal Otomasi Kontrol dan Instrumentasi Automotive Experiences Jurnal Rekayasa elektrika
Endra Joelianto
KK Instrumentasi Dan Kontrol, Fakultas Teknologi Industri, Institut Teknologi Bandung
Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics

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Analisis Pengaruh Waktu Latensi Terhadap Akurasi Sistem SCADA Bacaan Metering Listrik Waktu Nyata Melalui Jaringan Internet Endra Joelianto; Fuady Ramdhani; Eko Mursito Budi
Jurnal Rekayasa Elektrika Vol 16, No 3 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i3.16465

Abstract

SCADA (Supervisory Control and Data Acquisition) system of electricity metering using the internet network aims to monitor electrical energy remotely by utilizing internet services. The system consists of a meter that measures electric quantities acquired by a server located close to the meter. The client reads data acquired by the server through the internet network. The use of internet networks for data transmission generally results in latency time, which affects the validity of the data read by the client, resulting in reduced cumulative power calculation accuracy. In this article, energy calculations using current, voltage and power factor data on the client are compared with the energy value calculated by the power meter. Errors that occur are used to calculate the accuracy of the system. The experiment resulted in latency times ranging from 110 ms - 11219 ms with an average of 572.3025 ms with valid data ranging from 93% of population data and accuracy values ranging from 99.2974% to 99.8648%. The resulting accuracy is within the ANSI C12.20 standard. 
Sistem Kontrol Swarm untuk Flocking Wahana NR-Awak Quadrotor dengan Optimasi Algoritma Genetik Joelianto, Endra; Rajasa, Winarendra Satya; Samsi, Agus
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 6: Desember 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021863467

Abstract

Quadrotor merupakan wahana udara nir-awak jenis lepas landas atau pendaratan vertikal berbentuk silang dan memiliki sebuah rotor pada setiap ujung lengannya dengan kemampuan manuver yang tinggi. Swarm quadrotor yang terdiri dari sekumpulan quadrotor akan menjadi suatu swarm yang baik, sesuai dengan kriteria swarm oleh Reynold yaitu dapat menghindari tumbukan, menyamakan kecepatan, dan pemusatan swarm. Pengontrolan swarm quadrotor memiliki tingkat kerumitan yang tinggi karena melibatkan banyak agen. Riset pengembangan swarm quadrotor masih belum banyak dilakukan dan masih membuka peluang untuk meneliti dengan metoda lain yang lebih baik dalam menghasilkan swarm. Makalah ini mengusulkan pengontrolan swarm quadrotor yang terdiri dari dua tingkat lup kontrol. Lup pertama adalah pengontrol sistem model swarm untuk membangkitkan lintasan swarm dan lup kedua merupakan pengontrol pada quadrotor untuk melakukan penjejakan lintasan swarm. Pengontrol pertama menggunakan pengontrol proporsional derivatif (PD), sedangkan pengontrol kedua menggunakan regulator linier kuadratik (RLK). Pengontrol yang dirancang memiliki parameter yang banyak, sehingga pemilihan parameter yang optimal sangat sulit. Pencarian parameter optimal pada pengontrol model swarm quadrotor membutuhkan teknik optimasi seperti algoritma genetik (AG) untuk mengarahkan pencarian menuju solusi yang menghasilkan kinerja terbaik. Pada makalah ini, penalaan dengan optimasi AG hanya dilakukan pada pengontrol PD untuk menghasilkan lintasan swarm terbaik, sedangkan matrik bobot RLK dilakukan secara uji coba. Hasil simulasi swarm pada model quadrotor menunjukkan parameter , . , dan  yang diperoleh menggunakan AG menghasilkan pergerakan swarm yang baik dengan kesalahan RMS pelacakan 0,0094 m terhadap fungsi obyektif. Sedangkan ketika parameter ,  dan  dicari menggunakan AG, tidak berpengaruh banyak dalam memperbaiki hasil simulasi swarm quadrotor. AbstractThe quadrotor is a type of take-off or vertical landing unmanned aerial vehicles with a cross shape and has one rotor at each end of its arm with high maneuverability. A quadrotor swarm consisting of a group of quadrotors leads to a good swarm, according to Reynold's swarm criteria, which accomplishes collision avoidance, velocity matching, and flock centering. Quadrotor swarm control has a high level of complexity because it involves many agents. Research on the development of quadrotor swarm has received insignificant attention and it still opens opportunities to research other methods that are better at producing swarm. The paper proposes the control of a quadrotor swarm consisted of two levels of control loops. The first loop controls the swarm model system to generate the swarm trajectory and the second loop is the controller on the quadrotor to track the swarm path. The first controller uses a proportional derivative controller (PD), while the second controller uses the linear quadratic regulator (LQR). The controller that is designed has many parameters, so the optimal parameter selection is very difficult. The search for optimal parameters in the swarm model controller requires optimization techniques such as the genetic algorithm (GA) to direct the search for solutions that produce the best performance. In this paper, tuning with the optimization of GA is only done for the PD controller in order to produce the best swarm trajectory, while the weight matrices of the LQR are done on a trial error basis. Swarm simulation results of a quadrotor model system show the parameters , . , and  obtained using GA produce a good swarm movement with RMS error 0.0094 m of the objective function. Whereas when parameters ,  and  are searched using GA, it does not have much effect in improving the quadrotor swarm simulation results.
Static Structural Analysis of Checking Fixture Frame of Car Interior Using Finite Element Method Hanif Setya Hanandita; Ubaidillah Ubaidillah; Aditya Rio Prabowo; Bhre Wangsa Lenggana; Arjon Turnip; Endra Joelianto
Automotive Experiences Vol. 6 No. 3 (2023)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.9860

Abstract

An inspection is the most important step for the manufacturers producing their cars. This ensures the seamless compatibility of each car part, as even minor errors can lead to user discomfort during operation. To achieve that goal, the utilization of inspection tools, such as a checking fixture is essential. In this research, we will study the structure analysis of a checking fixture with Ansys software. This study aims to examine the structural strength by analyzing the impact of various design variations on the overall strength outcomes. The requirement for checking fixture is that it must meet the datum tolerance of the car with value of ± 2mm. Due to that factor, a rigid checking fixture is needed for inspecting the part without experiencing significant deformation. In static loading, the result of the first variation frame has a stress of 5.71 MPa and deformation of 0.051 mm, the second variation frame has a stress of 6.16 MPa and deformation of 0.049 mm and the third variation frame has a stress of 5.63 MPa and deformation 0.042 mm. In terms of weight, the first variation structure has 2470.48 kg, the second variation structure has 2179.93 kg and the third variation structure has 2210 kg. The second variation frame has the highest stress but it has the lightest weight, and the third variation frame has lower stress and deformation but it has a heavier weight than the second variation model. The study results that the second variation model is superior because it has the lightest weight while the three designs have small stress and deformation that still satisfy the requirement of the fixture.
Co-Authors Aditya Rio Prabowo Agus Samsi Agus Samsi Alfeus Leonardo Anna Antonyova Anna Antonyova, Anna Arjon Turnip Arjon Turnip Bentang Arief Budiman Bhre Wangsa Lenggana Dananjaya, Alex Daniel Christian daniel christian Eko Mursito Budi Estiyanti Ekawati Faisal Hendri Fuady Ramdhani Hanif Setya Hanandita Herman Y. Sutarto Herman Y. Sutarto Le Hoa Nguyen Muhammad Zibny Mukhlish, Faqihza Nadana Ayzah Azis Nicco Avinta Purwanto oberli tansri Parlindungan H. Sitanggang Parlindungan H. Sitanggang Peter Antony Peter Antony, Peter R K A Kusumah Rajasa, Winarendra Satya Rizky Cahya Kirana Samsi, Agus Santosa, Sigit Puji Steven Aurecianus Sutanto Hadisupadmo Ubaidillah Ubaidillah