Article Per Year (5 Year)
p-Index From 2019 - 2024
0.882
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Aceh International Journal of Science and Technology
Monna Rozana
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Aceh International Journal of Science and Technology

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

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). 
ZnO Production from EAF Solid Waste Using Hydrothermal Methods via Oxalate Precipitation Lukman Nulhakim; Imam Prasetyo; Monna Rozana; Widi Astuti
Aceh International Journal of Science and Technology Vol 12, No 1 (2023): April 2023
Publisher : Graduate Program of Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.1.29579

Abstract

Electric Arc Furnace (EAF) waste contains 50-60% Zn. EAF waste synthesis has the potential to produce ZnO, which can be used in a variety of applications. The hydrothermal method is used in the synthesis, with time variations of 3 and 6 hours and temperature variations of 120 °C, 150 °C and 200 °C using precipitating reagents in the form of sodium hydroxide (NaOH) and oxalic acid (C2H2O4). UV-Vis spectrophotometer characterization result shows the absorbance value at 365 nm, which is the absorbance characteristic of ZnO material. The result of increasing the Zn element was revealed by XRF characterization. The XRD characterization revealed zinc oxalate dehydrates. This result depicts the hydrothermal with the oxalic acid solvent used to produce zinc oxalate dehydration at various temperatures and time variations.
Comparison Study the Modeling of Limiting Current in the Magneto Electrodeposition of Vanadium using Neural-Wiener Model and Feed Forward Neural Network Lukman Nulhakim; Ismoyo Aji Sasmita; Monna Rozana; Sudibyo Sudibyo
Aceh International Journal of Science and Technology Vol 12, No 1 (2023): April 2023
Publisher : Graduate Program of Syiah Kuala University

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). 
ZnO Production from EAF Solid Waste Using Hydrothermal Methods via Oxalate Precipitation Lukman Nulhakim; Imam Prasetyo; Monna Rozana; Widi Astuti
Aceh International Journal of Science and Technology Vol 12, No 1 (2023): April 2023
Publisher : Graduate Program of Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.1.29579

Abstract

Electric Arc Furnace (EAF) waste contains 50-60% Zn. EAF waste synthesis has the potential to produce ZnO, which can be used in a variety of applications. The hydrothermal method is used in the synthesis, with time variations of 3 and 6 hours and temperature variations of 120 °C, 150 °C and 200 °C using precipitating reagents in the form of sodium hydroxide (NaOH) and oxalic acid (C2H2O4). UV-Vis spectrophotometer characterization result shows the absorbance value at 365 nm, which is the absorbance characteristic of ZnO material. The result of increasing the Zn element was revealed by XRF characterization. The XRD characterization revealed zinc oxalate dehydrates. This result depicts the hydrothermal with the oxalic acid solvent used to produce zinc oxalate dehydration at various temperatures and time variations.
Co-Authors Adi Waskito Anisya Lisdiana Herkuswyna Isnaniyah Wahab Imam Prasetyo Ismoyo Aji Sasmita Lukman Nulhakim Sudibyo Sudibyo Widi Astuti