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All Journal G-Tech : Jurnal Teknologi Terapan
Edrick Hansel Limantoro
Sampoerna University, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Enhancing Respiratory Disease Diagnosis through FMCW Radar and Machine Learning Techniques Ariana Tulus Purnomo; Raffy Frandito; Edrick Hansel Limantoro; Rafie Djajasoepena; Muhammad Agni Catur Bhakti; Ding-Bing Lin
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3693

Abstract

This study addresses the urgent need for early diagnosis and continuous monitoring of respiratory diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and infectious diseases. We have created a system that combines state-of-the-art machine learning algorithms with frequency-modulated continuous wave (FMCW) radar technology. FMCW radar technology is sensitive to small respiratory movements, allowing real-time monitoring without physical contact. Machine learning algorithms, including Decision Trees, Random Forest, Naïve Bayes, Gradient Boosting, and Support Vector Machines, are employed to classify these waveforms. The Random Forest classifier achieved the highest accuracy score of 94.6%, with Naïve Bayes exhibiting the shortest processing time at 0.055 seconds. We explored various cross-validation methods such as Shuffle Split, K-fold, and Stratified K-fold, with the Shuffle Split method performing best overall in terms of accuracy and time. Our study introduces an integrated system that could revolutionize the early detection, response, and tracking of respiratory diseases and emergencies over time.
Unleashing the Power of Deep Learning: Revolutionizing Facial Recognition with GhostFaceNets Ariana Tulus Purnomo; Edrick Hansel Limantoro; Muhammad Nafis Aimanurrohman
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6459

Abstract

Facial recognition technology has advanced significantly due to the development of deep learning algorithms. This paper explores deep learning, a branch of machine learning that employs multi-layered neural networks to simulate human decision-making processes in facial recognition. It provides a brief literature review of significant works in various deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The core of the study is the implementation of the GhostFaceNets model, an enhancement of GhostNets, which is specifically designed for efficient and accurate facial recognition. By using Ghost Modules, this model reduces computational redundancy in generating additional feature maps through linear operations. An integrated attention mechanism is used in this study to emphasize critical facial features. Additionally, this study also employs the ArcFace loss function to improve class separation accuracy. The VGG2-FP dataset was used to train and evaluate this model and achieved an accuracy of 94.45%. This study contributes to the evolution of facial recognition systems, particularly in constrained computational environments.
Co-Authors Ariana Tulus Purnomo Ding-Bing Lin Muhammad Agni Catur Bhakti Muhammad Nafis Aimanurrohman Raffy Frandito Rafie Djajasoepena