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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Ekasari Nugraheni
National Research and Innovation Agency
Computer Science & IT Electrical & Electronics Engineering

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Machine learning in handling disease outbreaks: a comprehensive review Dianadewi Riswantini; Ekasari Nugraheni
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3612

Abstract

The changes in the global environment have made impact on the evolution of infectious diseases, virus mutations, or new diseases which are challenging to be tackled with new technological advances. This work aims to identify and analyze previous studies on machine learning applications in handling disease outbreaks. Bibliometric analysis was conducted on 3,447 scientific articles selected from the Scopus database. Further, latent dirichlet analysis (LDA) method was applied to identify the topic hotspots in attempting to deepen the analysis. The LDA results identified twelve topic hotspots that can be classified into three themes: COVID-19 disease, miscellaneous diseases, and public opinion on disease outbreaks for discussion. The study reveals that the scientific structure of this domain is dominated by machine learning research on COVID-19 diseases and miscellaneous diseases caused by pathogens or some genetic factors. A huge amount of multimodal medical data was used by previous studies for prediction, forecasting, classification, or screening purposes to resolve many problems of diseases, including epidemiological surveillance, diagnosis, treatment, health monitoring, epidemic management, viral infection, and pathogenesis. Public opinions toward new diseases are also an interesting topic in addition to the public perceptions in response to the health protocol and policies.
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.
The multi-tenancy queueing system “QuAntri” for public service mall Wiwin Suwarningsih; Ana Heryana; Dianadewi Riswantini; Ekasari Nugraheni; Dikdik Krisnandi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4348

Abstract

In the new-normal era, public services must make various adjustments to keep the community safe during the COVID-19 pandemic. The Public Service Mall is an initiative to put several public services offices in a centralized location. However, it will create a crowd of people who want access to public service. This paper evaluates multi-tenant models with the rapid adaptation of cloud computing technology for all organizations' shapes and sizes, focusing on multi-tenants and multi-services, where each tenant might have multiple services to offer. We also proposed a multi-tenant architecture that can serve queues in several places to prevent the spread of COVID-19 due to the crowd of people in public places. The design of multi-tenants and multi-services applications should consider various aspects such as security, database, data communication, and user interface. We designed and built the "QuAntri'' business logic to simplify the process for multi-services in each tenant. The developed system is expected to improve tenants' performance and reduce the crowd in the public service. We compared our agile method for system development with some of the previous multi-tenant architectures. Our experiments showed that our method overall is better than the referenced model-view-controller (MVC), model-view-presenter (MVP), and model-model-view-presenter (M-MVP).
Co-Authors Ana Heryana Andri Fachrur Rozie Andria Arisal Ayu Purwarianti Devi Munandar Dianadewi Riswantini Dikdik Krisnandi Purnomo Husnul Khotimah Wiwin Suwarningsih