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

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Ide-cabe: chili varieties identification and classification system based leaf Wiwin Suwarningsih; Purnomo Husnul Khotimah; Andri Fachrur Rozie; Andria Arisal; Dianadewi Riswantini; Ekasari Nugraheni; Devi Munandar; Rinda Kirana
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3276

Abstract

Identifying good quality chili varieties can be done by observing their leaves. It is required for seed testing and certification processes. Currently, a manual leaf identification method is used in which human experts inspect a wide range of leaves every one to two months. An automatic method could increase the identification process. Deep learning has proven to be a prominent method for image classification. We investigate the performance of deep CNN models, as: AlexNet, VGG16, Inception-v3 and DenseNet-121; to classify chili variety. In this paper, we took images of leaves aged 10 days. A preprocessing strategy was taken to enrich the dataset and to boost the classification performance and to assess the proposed models’ quality. From this study, we acquired 12 classes of chili leaves dataset. We acquired performance accuracy ranging from 70.18% to 78.37%. Further, the classification results by DenseNet-121 obtained the highest accuracy of 78.37% and recall of 74.83%. The classifiers investigated in this study perform well despite the relatively small number of our dataset. These results encourage the application of such an approach in practice.
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 Andri Fachrur Rozie Andria Arisal Andria Arisal Ayu Purwarianti Devi Munandar Devi Munandar Dianadewi Riswantini Dianadewi Riswantini Dikdik Krisnandi Ekasari Nugraheni Ekasari Nugraheni Purnomo Husnul Khotimah Purnomo Husnul Khotimah Rinda Kirana