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All Journal JURNAL ELEKTRO Metris: Jurnal Sains dan Teknologi
Sachi, Haenuki
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Klasifikasi Gender Berdasarkan Gambar Menggunakan Metode Deep Learning Pada MATLAB Sachi, Haenuki; Wijayanti, Linda; Octaviani, Sandra
Jurnal Elektro Vol 16 No 2 (2023): Vol.16 No.2 Oktober 2023: Jurnal Elektro
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v16i2.5135

Abstract

In the present era, machine intelligence, also known as Artificial Intelligence (AI), is demanded not only to execute specific commands but also to recognize, analyze, or even make decisions, thereby providing desired outputs. By harnessing the power of AI, it is anticipated that desired outcomes will be more accurate and goal achievement will be optimized, minimizing losses. With the capabilities of AI in mind, a research study has been conducted on AI's ability to analyze and make decisions based on specific data. In this study, data in the form of images of men and women were utilized. The objective of this research is to analyze the ability of AI, particularly in gender classification. The method employed in designing this system is Deep Learning, with GoogLeNet as the Convolutional Neural Network utilized. In testing, the data accuracy ranged from 61.8% to 100% for the system without training algorithm options and from 97.5% to 100% for the system with training algorithm options. Testing was also carried out on a smaller set of training data and grayscale images, yielding lower accuracy ranges. From this research, it can be concluded that the quantity of training data, image preprocessing, and training algorithm options are crucial indicators for enhancing prediction accuracy.
Simulasi Pengendalian Kecepatan Putar Motor DC Menggunakan Metode Logika Fuzzy Hutapea, Duma Kristina Yanti; Sachi, Haenuki; Kusmanto, Willy; Herianto, David; Fernanto, Nico; Bachri, Karel Octavianus
Metris: Jurnal Sains dan Teknologi Vol. 24 No. 02 (2023): Desember
Publisher : Prodi Teknik Industri, Fakultas Teknik - Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/metris.v24i02.4725

Abstract

This paper discusses DC motor rotational speed control using fuzzy logic method. The system works by utilizing ultrasonic sensors to detect distance. The detected distance affects the rotational speed of the DC motor which is controlled by the Arduino UNO microcontroller. Fuzzy logic is applied to the Arduino UNO microcontroller in which distance as the input, and the rotational speed of the DC motor in the form of Pulse Width Modulation as the output. There are several classifications of distance: very close, close, normal, far and very far. In addition, there are several classifications in the motor rotational speed response: slow, very slow, medium, fast, and very fast. The design of the fuzzy logic system is carried out in the Arduino IDE application and MATLAB using the Mamdani method at the inference stage. Fuzzy logic system is run on the Arduino UNO microcontroller in which the simulation is carried outthrough Proteus application. The test results show that the largest percentage of errors/discrepancies between the Proteus system design and the MATLAB is 1.55%. In the future, this simulation can be applied to systems such as self-driving cars.
Co-Authors Bachri, Karel Octavianus Duma Kristina Yanti Fernanto, Nico Herianto, David Kusmanto, Willy Sandra Octaviani Wijayanti, Linda