Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JURNAL ELEKTRO
Linda Wijayanti
Program Studi Teknik Elektro, Fakultas Teknik Universitas Katolik Atma Jaya Indonesia, Jakarta 12930, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Analisis Audio Capture untuk Pengumpulan Data pada Smart Speaker Andre Zenik; Lukas Lukas; Catherine Olivia Sereati; Kumala Indriati; Linda Wijayanti
Jurnal Elektro Vol 16 No 1 (2023): Vol.16 No.1 April 2023: Jurnal Elektro
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v16i1.5128

Abstract

Internet of Things or IoT is a technology that is currently trending, because it can help human in their daily routine. One of the IoT product that used in homes is smart speaker. However, every latest technological innovation does not escape from vulnerabilities and one of them is the microphone on the smart speaker. Several studies and research have found that security vulnerabilities in smart speakers, such as dolphin attacks and audio capture. To find out how this audio capture technique works, an experiment was made to understand how it works and the impact of this technique. This experiment uses a smart speaker with voice assistant Alexa, using the Alexa Skills Kit services and Flask-Ask framework to create an audio capture program. The results of this program testing are expected to be used as a benchmark to prevent smart speakers from becoming the target of this technique anymore.
Klasifikasi Gender Berdasarkan Gambar Menggunakan Metode Deep Learning Pada MATLAB Haenuki Sachi; Linda Wijayanti; Sandra Octaviani
Jurnal Elektro Vol 16 No 2 (2023): Vol.16 No.2 Oktober 2023: Jurnal Elektro
Publisher : Program Studi Teknik Elektro, Fakultas Biosains, Teknologi, dan Inovasi, Universitas Katolik Indonesia Atma Jaya

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.
Co-Authors Andre Zenik Catherine Olivia Sereati Haenuki Sachi Kumala Indriati Lukas Lukas Sandra Octaviani