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All Journal TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika
De Fitrah, Figo Azzam
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Electrical & Electronics Engineering

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ANALISIS PERFORMANSI ALGORITMA SVM, CNN, DAN LSTM UNTUK PENGENALAN KEGIATAN MANUSIA DENGAN URAD FMCW RADAR Ramadhan, Azhar Yunda; De Fitrah, Figo Azzam; Nurhidayat, Muhammad Adi; Suratman, Fiky Yosep; Istiqomah, Istiqomah
TEKTRIKA Vol 8 No 1 (2023): TEKTRIKA Vol.8 No.1 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i1.6312

Abstract

In this study, we compare Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long ShortTerm Memory (LSTM) algorithms as commonly used machine learning algorithms based on FMCW Radar data for Human Activity Recognition (HAR). The comparison is conducted by evaluating the models on test data and considering the fitting time and the number of parameters required by each model to achieve the desired results, to find the most efficient model that provides the best results. We discovered that the LSTM 01 model with one layer of 16 unit-LSTM produces the best result based on the scoring of several tested models. The model demonstrated an ability to achieve accuracy up to 86% on the test data with a relatively small number of parameters, i.e., 294,725. Key Words: Radar, computation, FMCW, SVM, CNN, LSTM.
PERANCANGAN WEBSITE BERBASIS APLIKASI DENGAN FITUR NOTIFIKASI WHATSAPP UNTUK SISTEM DETEKSI JATUH Nurhidayat, Muhammad Adi; Y. R, Azhar; De Fitrah, Figo Azzam; Suratman, Fiky Y.; Istiqomah, Istiqomah
TEKTRIKA Vol 8 No 2 (2023): TEKTRIKA Vol.8 No.2 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i2.6535

Abstract

This journal discusses the development of a fall detection system using radar as a sensor. The purpose of this system goes beyond technological advancement; it carries significant social impact. It aims to detect fall accidents early and provide prompt intervention to individuals, especially those living alone. In an effort to save lives and reduce the burden on caregivers. The system is connected to an IoT platform and a web-based application that allows users to view real-time detection results. With a notification feature that sends alerts to users and hospitals in the event of a fatal fall accident, this system plays a crucial role in improving the safety and quality of life for those in need. Through testing using the Blackbox Testing method with various scenarios, the system ensures it meets the set requirements and objectives, providing a positive user experience that aligns with expectations, and contributing to ongoing technological development. Key Words: web-based application, fall detection, notifications.
Co-Authors Fiky Y. Suratman istiqomah istiqomah Nurhidayat, Muhammad Adi Ramadhan, Azhar Yunda Y. R, Azhar