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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Majalah Ilmiah Pengkajian Industri
Luthfiyah, Hilda
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Transportation

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CAMERA-BASED DETECTORS AS AN ALTERNATIVE TO DETECTING TRAINS IN A LEVEL CROSSING IMPLEMENTATION Luthfiyah, Hilda; Adam, Okghi; Anugrah, Teddy; Mantara, Gilang
Majalah Ilmiah Pengkajian Industri Vol. 14 No. 2 (2020): Majalah Ilmiah Pengkajian Industri
Publisher : Deputi TIRBR-BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/mipi.v14i2.4077

Abstract

Based on data from Indonesia Directorate General of Railways in 2017, it is mentioned that the problems at the level crossing of railroad tracks are mostly caused by human error factors themselves. The current train headway and the crossing system that is still operated manually can increase the potential for accidents. Therefore, the development of alternative camera-based detectors to support the railroad crossing automation system is needed at this time. The development of this camera-based train detector uses the basic program You Only Look Once (YOLO), where YOLOv3 has proven to be accurate enough to detect moving objects. The development results show promising results for several types of alternative trains. Key Words : Detectors; Train; YOLOv3
Analisis Pengaruh Noise pada Performa K-Nearest Neighbors Algorithm dengan Variasi Jarak untuk klasifikasi Beban Listrik YUNATA, ARIS SURYA; HALIM, ABDUL; LUTHFIYAH, HILDA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 3: Published July 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i3.745

Abstract

ABSTRAK Teknik Non-Intrusive Load Monitoring (NILM) digunakan dalam pemantauan konsumsi energi. Variabel pengukuran yang digunakan yaitu Real Power dan Reactive Power. klasifikasi beban listrik menjadi acuan dalam mengurangi tagihan energi. Namun, data pengukuran sering kali terpengaruh oleh noise. Penelitian ini bertujuan untuk menganalisis pengaruh noise terhadap performa algoritma k-Nearest Neighbors (k-NN) dalam klasifikasi beban listrik. Berbagai tingkat noise secara rundom diberikan pada data pengukuran yang diperoleh. Selanjutnya, model k-NN dilatih dan dievaluasi dengan nilai k = 1 sampai 9 dan 15 tipe jarak. Hasil eksperimen menunjukkan bahwa penambahan noise pada data pengukuran secara signifikan mempengaruhi performa algoritma k-NN dalam mengklasifikasikan beban listrik. Pengaruh ini terlihat pada nilai akurasi tertinggi mayoritas pada k = 3 dan Tipe jarak Cambera menghasilkan nilai akurasi di atas rata-rata. Kata kunci: NILM, Real Power, Reactive Power, noise, k-NN  ABSTRACT The Non-Intrusive Load Monitoring (NILM) technique is used in monitoring energy consumption. The measurement variables used are Real Power and Reactive Power. Electric load classification serves as a reference in reducing energy bills. However, measurement data is often affected by noise. This study aims to analyze the influence of noise on the performance of the k-Nearest Neighbors (k-NN) algorithm in electric load classification. Various levels of noise are randomly added to the obtained measurement data. Subsequently, the k-NN model is trained and evaluated with values of k = 1 to 9 and 15 distance types. The experimental results show that the addition of noise to the measurement data significantly affects the performance of the k-NN algorithm in classifying electric loads. This influence is observed in the highest accuracy values, mostly at k = 3, and the Canberra distance type yields accuracy values above average. Keywords: NILM, Real Power, Reactive Power, noise, k-NN
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 A Halim Adam Qowiy, Okghi Adam, Okghi Anugrah, Teddy Hidayat, Sofwan Kamar, Syamsul Lestari, Meiyanne Mantara, Gilang Syamsuddin Hasrito, Eko Tri Widodo YUNATA, ARIS SURYA