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All Journal Aceh International Journal of Science and Technology Electrician : Jurnal Rekayasa dan Teknologi Elektro
Sasmita, Ismoyo Aji
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Comparison Study the Modeling of Limiting Current in the Magneto Electrodeposition of Vanadium using Neural-Wiener Model and Feed Forward Neural Network Nulhakim, Lukman; Sasmita, Ismoyo Aji; Rozana, Monna; Sudibyo, Sudibyo
Aceh International Journal of Science and Technology Vol 12, No 1 (2023): April 2023
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.1.29846

Abstract

Vanadium has long been used as a corrosion-resistant coating, including as a metal alloy for battery cathodes. However, batteries discovered with non-smooth cathode surfaces due to the fabrication process have a short battery life. So, a cathode coating stage is required via the electroplating method under the influence of a magnetic field or Magneto Electro Deposition (MED). Knowing the limiting current in MED is very important because the optimum mass transport achieves at the limiting current (iB). The smoothest and most compact electrodeposit surface will occur at this limiting current. In this study, Feed Forward Neural Network and Neural-Wiener are suggested and compared as a nonlinear modeling approach to determine the ideal limiting current because of their strong capacity to anticipate the link between input and output from experiment data. The Levenberg-Marquadt optimization technique with hidden neurons was used to evaluate and compare the modeling capabilities of two neural networks, the Feed Forward Neural Network, and the Neural Wiener. The results of this study are presented as a comparison of the Mean Square Error (MSE) values obtained from the nonlinear modeling of two artificial neural network algorithms. The algorithm that models the ideal current limiting has the lowest MSE value (iB).
Perancangan Sistem Deteksi Peta Panas (Heatmap) Keramaian Pengunjung di Area Publik Selama Pandemi COVID-19 Berbasis YOLOV4-Tiny Harahap, Al Barra; Drantantiyas, Nike Dwi Grevika; Sasmita, Ismoyo Aji
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 19 No. 3 (2025)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v19n3.2796

Abstract

Pandemi COVID-19 mengakibatkan masyarakat melakukan aktivitas dengan cara baru yaitu dengan penerapan protokol kesehatan. Pengawasan yang ketat sangat penting dilakukan pada kawasan wisata di area publik untuk menjaga jarak aman dan menghindari kerumuman untuk menekan angka penyebaran virus. Salah satu metode pemantauan aktivitas di ruang terbuka yang dapat digunakan adalah dengan penerapan deep learning. Pada penelitian ini dilakukan perancangan sistem deteksi objek untuk pemetaan kerumunan, kemudian dilakukan penilaian akurasi metode algoritma deteksi objek, dan dilakukan pengujian kemampuan metode algoritma deteksi objek dalam melakukan pemetaan kerumunan. Peta Panas kerumunan dibentuk dengan pendekatan prediktif menggunakan algoritma deteksi objek satu tahap You Only Look Once (YOLO) v4-Tiny berdasarkan pengurangan latar belakang (background subtraction). Pada penelitian ini berhasil dirancang algoritma deteksi objek di area keramaian dengan akurasi 95% dan model akurasi rata-rata 59,45%. Hasil visual pemetaan heatmap kerumunan pengunjung dibentuk dengan warna hitam dan kuning berdasarkan kepadatan arus pengunjung yang melalui suatu lokasi. Warna kuning pada peta di suatu lokasi menandakan pada jalur tersebut terdapat tingkat lalu-lintas manusia yang padat.
Co-Authors Drantantiyas, Nike Dwi Grevika Harahap, Al Barra Nulhakim, Lukman Rozana, Monna Sudibyo Sudibyo