Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknik Informatika (JUTIF) Journal of Engineering and Technological Sciences
Bayuwindra, Anggera
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Prediction of Turbidity Removal Time in Electrocoagulation Wastewater Using Random Forest, XGBoost, and Others: A Data-Driven Information System Approach Suakanto, Sinung; See, Tan Lian; Shaffiei, Zatul Alwani; Firdaus, Taufiq Maulana; Lubis, Muharman; Bayuwindra, Anggera
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4847

Abstract

Electrocoagulation is an effective and environmentally friendly technology for treating wastewater by removing contaminants such as turbidity, heavy metals, and organic compounds. Accurately predicting turbidity removal time is essential for optimizing treatment performance and operational efficiency. However, this is challenging due to complex, nonlinear relationships between multiple parameters including current, voltage, electrode configuration, conductivity, and turbidity removal rate. This study aims to develop a predictive framework by comparing six supervised regression models, namely Linear Regression, Polynomial Regression, Random Forest, Support Vector Regression (SVR), XGBoost, and Long Short-Term Memory (LSTM), using key electrocoagulation parameters. After extensive data preprocessing, a dataset of 281 samples was used for training and validation. Among them, Random Forest achieved the best performance (R² = 0.876, RMSE = 601.15). A data-driven information system is proposed to integrate these predictive capabilities for real-time monitoring and control. By improving turbidity prediction accuracy, the system enables the sustainable utilization of water as a valuable asset, even in its wastewater form. The approach enhances decision-making by providing intelligent feedback for process optimization. This research contributes to the advancement of intelligent, sustainable wastewater treatment systems by integrating machine learning prediction models with practical process control applications in informatics.
Longitudinal Train Dynamics Model for CC203/CC206 Locomotive Simulator Hindersah, Hilwadi; Rohman, Arief Syaichu; Bayuwindra, Anggera; Rusmin, Pranoto H.; Kinasih, Fabiola M.T.R.; Machbub, Carmadi
Journal of Engineering and Technological Sciences Vol. 56 No. 3 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.3.10

Abstract

This paper presents train modeling used in a simulator platform for driver training. It was developed for the CC203/CC204 locomotive. The driver will gain experience as in a real locomotive from the perceived platform movements if the movements match real conditions as accurately as possible, including the distance travelled. To this aim, a longitudinal model of the train was developed based on measurement data obtained from the Argo Parahyangan train traveling from Bandung to Jakarta. A second-order linear time invariant model was obtained by a black box identification approach, in which the input and the output of the model are the resultant force (a traction and a slope-friction force) and the train’s position, respectively. While the speed is directly obtained from measurement data, the traction force of the locomotive is predicted using the traction characteristic of the locomotive, train’s measured speed, and latitude time history during a train trip. The model is then validated by running a simulation for one complete trip of the train. In the simulation, the same input as in the model identification is applied and the mileage obtained from simulation result is compared to data of the real train trip with a fitness level of 94.09%.
Co-Authors Arief Syaichu Rohman, Arief Syaichu Carmadi Machbub Firdaus, Taufiq Maulana Hindersah, Hilwadi Kinasih, Fabiola M.T.R. Muharman Lubis Rusmin, Pranoto H. See, Tan Lian Shaffiei, Zatul Alwani Sinung Suakanto