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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Adam Qowiy, Okghi
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

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Trend analysis of machine learning techniques for traffic control based on bibliometrics Luthfiyah, Hilda; Syamsuddin Hasrito, Eko; Widodo, Tri; Hidayat, Sofwan; Adam Qowiy, Okghi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2402-2411

Abstract

Machine learning in traffic control for intelligent transportation systems (ML-ITSTC) aims to enhance user coordination and safety within transportation networks, ultimately improving overall traffic system performance. ML-ITSTC is achieved by leveraging data to execute machine learning algorithms in intelligent transportation management and optimizing traffic flow to prevent or reduce congestion. This paper conducts bibliometric analysis to explain the research status, development trajectory, and challenges of ML-ITSTC, drawing insights from literature in the Scopus database literature covering 2013 to November 2023. The bibliometric analysis of ML-ITSTC includes: performance analysis, science mapping analysis, and citation analysis. The evaluation of ML algorithm trends over the 10-year span indicates that traffic prediction (TP), neural networks, and deep learning are frequently used keywords. Further, an examination of keywords used over the entire period and in 2023 (up to November) shows that reinforcement learning (RL) is the latest popular approach for traffic control in transportation. The results provide a comprehensive view of the opportunities and challenges in ML-ITSTC, covering data, models, and applications, offering researchers insights into the current and future directions of ML-ITSTC research.
Determination analysis of main dimensions of induction motors for railway propulsion system Kamar, Syamsul; Lestari, Meiyanne; Luthfiyah, Hilda; Adam Qowiy, Okghi; Syamsuddin Hasrito, Eko; Hidayat, Sofwan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8554

Abstract

Induction motors are used in industrial production processes. As for its use as a traction motor, it requires special design and manufacture. The type of induction motor that is widely chosen as a traction motor for railways is a squirrel-cage three-phase induction motor. The main consideration for the selection or design of an induction motor as a railway traction motor is the torque requirement to drive the train. Other parameters that are considered in the selection of an induction motor as a traction motor include available spaces for installation. This research is using a three-phase, 2,300 VAC, 480 kW, and 50 Hz induction motor. By using the application program for determining the parameters of the induction motor, it shows that the motor produces a moderate output coefficient (between maximum and minimum) and produces a torque greater than induction motor torque in general. As a result of the analysis, this induction motor is suitable to be used as a motor for the railway, where greater torque is required.
Co-Authors Hidayat, Sofwan Kamar, Syamsul Lestari, Meiyanne Luthfiyah, Hilda Syamsuddin Hasrito, Eko Tri Widodo