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All Journal Journal of Innovation and Applied Technology JURNAL NASIONAL TEKNIK ELEKTRO
Ming, Eileen Su Lee
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Computer Science & IT Electrical & Electronics Engineering

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Martibo: Smart Tissue Dispenser with Monitoring Dashboard Alfaniza, Izzat Muhammad; Syafiq, Muhammad; Ana Ratna Wati, Dwi; Wisudawan, Hasbi Nur Prasetyo; Ming, Eileen Su Lee; Addi, Mitra Binti Mohd
Journal of Innovation and Applied Technology Vol 11, No 1 (2025)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2024.010.002.03

Abstract

This paper presents Martibo, a smart tissue dispensing management system integrated with a smartphone based on the Internet of Things (IoT), to improve tissue usage efficiency in public spaces and households. The system uses sensors to detect tissue availability and sends notifications to the Blynk application. The system is evaluated based on reliability, accuracy, and user interface responsiveness. The results show high accuracy with an average error of 0.91% in tissue availability detection and an average notification response time of 1.2 seconds. The system transmits data accurately through a Wi-Fi connection, enhancing the convenience and efficiency of tissue usage. The advantages of this system include accurate real-time data, a user-friendly interface, and good integration with mobile devices, making it an ideal solution for efficient tissue management.
Studi Autoencoder Deep Learning pada Sinyal EKG Mochamad Reza, Dandi; Satria Mandala; Zaki, Salim M.; Ming, Eileen Su Lee
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 3: November 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n3.1117.2023

Abstract

Arrhythmia refers to an irregular heart rhythm resulting from disruptions in the heart's electrical activity. To identify arrhythmias, an electrocardiogram (ECG) is commonly employed, as it can record the heart's electrical signals. However, ECGs may encounter interference from sources like electromagnetic waves and electrode motion. Several researchers have investigated the denoising of electrocardiogram signals for arrhythmia detection using deep autoencoder models. Unfortunately, these studies have yielded suboptimal results, indicated by low Signal-to-Noise Ratio (SNR) values and relatively large Root Mean Square Error (RMSE). This study addresses these limitations by proposing the utilization of a Deep LSTM Autoencoder to effectively denoise ECG signals for arrhythmia detection. The model's denoising performance is evaluated based on achieved SNR and RMSE values. The results of the denoising evaluations using the Deep LSTM Autoencoder on the AFDB dataset show SNR and RMSE values of 56.16 and 0.00037, respectively. Meanwhile, for the MITDB dataset, the corresponding values are 65.22 and 0.00018. These findings demonstrate significant improvement compared to previous research. However, it's important to note a limitation in this study—the restricted availability of arrhythmia datasets from MITDB and AFDB. Future researchers are encouraged to explore and acquire a more extensive collection of arrhythmia data to further enhance denoising performance.
Co-Authors Addi, Mitra Binti Mohd Alfaniza, Izzat Muhammad Ana Ratna Wati, Dwi Mochamad Reza, Dandi MUHAMMAD SYAFIQ Satria Mandala Wisudawan, Hasbi Nur Prasetyo Zaki, Salim M.