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All Journal International Journal of Electrical and Computer Engineering Jurnal Elektronika dan Telekomunikasi
Andri Fachrur Rozie
National Research and Innovation Agency
Computer Science & IT Electrical & Electronics Engineering Engineering

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SIMONIC: IoT Based Quarantine Monitoring System for Covid-19 Vita Awalia Mardiana; Mochamad Mardi Martadinata; Galih Nugraha Nurkahfi; Arumjeni Mitayani; Dayat Kurniawan; Nasrullah Armi; Budi Prawara; Sudirja Sudirja; Andria Arisal; Rendra Dwi Firmansyah; Andri Fachrur Rozie; Sulaksono Priyo; Sopyan Setiana; Asih Setiarini
Jurnal Elektronika dan Telekomunikasi Vol 21, No 2 (2021)
Publisher : LIPI Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v21.112-121

Abstract

COVID-19, which has become a global pandemic since March 2020, has tremendously affected human life globally. The negative impact of COVID-19 affects societies in almost all aspects. Implementing quarantine monitoring, also social distancing, and contact tracing are a series of processes that can suppress the new infected COVID-19 cases in various countries. Prior works have proposed different monitoring systems to assist the monitoring of individuals in quarantines, as well as many methods are offered for social distancing and contact tracing. These methods focus on one function to provide a reliable system. In this paper, we propose IoT-based quarantine monitoring by implementing a geofence equipped with social distancing features to offer an integrated system that provides more benefits than one system carrying one particular function. We propose a system consisting of a low cost, low complexity, and reusable wristband design and mobile apps to support the quarantine monitoring system. For the geofencing, we propose a GPS-based geofence system that was developed by taking advantage of the convenience offered by the Traccar application. Meanwhile, we add the notification for social distancing feature with adaptive distance measurement RSSI-based set up in the android application. Based on the experiment we did to validate the system, in terms of wristband-to-smartphone communication, scanning interval in smartphone and advertising interval in wristband is best to set in 7 s for both. For social distancing notification and geofence, we measure the system performance through precision, recall, accuracy, and F-measure.
Monitoring Indonesian online news for COVID-19 event detection using deep learning Purnomo Husnul Khotimah; Andria Arisal; Andri Fachrur Rozie; Ekasari Nugraheni; Dianadewi Riswantini; Wiwin Suwarningsih; Devi Munandar; Ayu Purwarianti
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp957-971

Abstract

Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.
Co-Authors Andria Arisal Arumjeni Mitayani Asih Setiarini Ayu Purwarianti Budi Prawara Dayat Kurniawan Devi Munandar Dianadewi Riswantini Ekasari Nugraheni Galih Nugraha Nurkahfi Mochamad Mardi Martadinata Nasrullah Armi Purnomo Husnul Khotimah Rendra Dwi Firmansyah Sopyan Setiana Sudirja Sudirja Sulaksono Priyo Vita Awalia Mardiana Wiwin Suwarningsih