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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Rotasi Jurnal Teknik Elektro
Pane, Ivranza Zuhdi
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Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Pengkondisi Sinyal RTD Presisi pada Terowongan Angin Indonesian Low-Speed Tunnel Muflih, Muhamad; Riyadi, Munawar Agus; Pane, Ivranza Zuhdi; Parulian, Franky Surya
Jurnal Teknik Elektro Vol 14, No 2 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i2.39415

Abstract

The temperature of the Indonesian Low-Speed Tunnel (ILST) wind tunnel test section was measured using a Pt100-type Resistance Temperature Detector (RTD) sensor. With the upgrade of the Indonesian Low-Speed Tunnel - Data Acquisition and Reduction System (ILST-DARS) using Ethernet communication, an integrated RTD linearization circuit was designed with the Conditioning Unit (CU) Mk3 to replace the Newport 267B 16-bit parallel and DAS-Hub as the current RTD interface. In this research, the design of the signal conditioner uses the RTD_Linearization_v7.xls program from Texas Instruments, the LTspice simulator software, and the AMP01E precision instrumentation amplifier. Based on the calibration results in the range of 20 – 50 0C, this signal conditioner has an average deviation value of 0.38 0C (1.31%). In the wind tunnel speed variation testing with a range of 30 – 65 m/s, the RTD signal conditioner had an average deviation of 0.41 K (0.14%). The Repeatability Test procedure was carried out at a wind speed of 65 m/s with an angle of attack for the test model from -90 to 200 and data were collected 10 times at each angle. The average deviation of temperature against variations in the angle of attack of the test model in this procedure is 0.25 K (0.08%) and the average deviation of wind speed against variations in the angle of attack of the test model is 0.03 m/s (0.04%).
Analisis Visualisasi Aliran Pengujian Model Airfoil Menggunakan Fluida Vape dengan Geometri Smoke Line yang berbeda Kooshartoyo, Meddy; Yohana, Eflita; Muchammad, Muchammad; Pane, Ivranza Zuhdi
ROTASI Vol 26, No 2 (2024): VOLUME 26, NOMOR 2, APRIL 2024
Publisher : Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/rotasi.26.2.73-78

Abstract

This research aims to analyze the flow visualization of the design of 2 types of smoke rake with vape fluid. The test model used was a three-dimensional NACA 4412 airfoil profile made from 10 mm thick flexyglass structural material, skin wrapped in epoxy resin with dimensions with chord length (C) = 151.52 mm, span width (S) = 300 mm, maximum thickness (t) = 19.18 mm made on a laboratory scale for wind tunnel testing purposes. Aerodynamic flow research was carried out at angles of attack (α) 5o, 100, 150 and 200 at wind speeds of 5 m/s, 10 m/s, and 15 m/s and 20 m/s. Several important parameters such as lift coefficient (CL), drag coefficient (CD), lift-drag ratio (L/D), in this study visually analyze the flow using the smoke method so that changes can be seen as the angle of attack (α) increases. The research results show that the drag coefficient (CD) and lift coefficient (CL) obtained using a numerical approach and testing in a wind tunnel from previous research data with this flow visualization research, changes in the angle of attack (α) have an effect on the leading edge area on the surface of the airfoil body, which in turn affects the overall aerodynamic characteristics. From the photo results, it can be seen that the difference in flow using the SR1 and SR2 smoke rake is greater.
Enhancing facial recognition accuracy through feature extractions and artificial neural networks Kusnadi, Adhi; Pane, Ivranza Zuhdi; Tobing, Fenina Adline Twince
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1056-1066

Abstract

Facial recognition is a biometric system used to identify individuals through faces. Although this technology has many advantages, it still faces several challenges. One of the main challenges is that the level of accuracy has yet to reach its maximum potential. This research aims to improve facial recognition performance by applying the discrete cosine transform (DCT) and Gaussian mixture model (GMM), which are then trained with backward propagation of errors (backpropagation) and convolutional neural networks (CNN). The research results show low DCT and GMM feature extraction accuracy with backpropagation of 4.88%. However, the combination of DCT, GMM, and CNN feature extraction produces an accuracy of up to 98.2% and a training time of 360 seconds on the Olivetti Research Laboratory (ORL) dataset, an accuracy of 98.9% and a training time of 1210 seconds on the Yale dataset, and 100% accuracy and training time 1749 seconds on the Japanese female facial expression (JAFFE) dataset. This improvement is due to the combination of DCT, GMM, and CNN's ability to remove noise and study images accurately. This research is expected to significantly contribute to overcoming accuracy challenges and increasing the flexibility of facial recognition systems in various practical situations, as well as the potential to improve security and reliability in security and biometrics.
Co-Authors Eflita Yohana Kooshartoyo, Meddy kusnadi, adhi Muchammad Muchammad Muflih, Muhamad Munawar Agus Riyadi Parulian, Franky Surya Tobing, Fenina Adline Twince