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All Journal Knowledge Engineering and Data Science Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science

Amelia Ritahani Ismail

International Islamic University Malaysia

Author-ID : 2635087

Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

Published : 5 Documents Claim Missing Document

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Change Vulnerability Forecasting Using Deep Learning Algorithm for Southeast Asia Amelia Ritahani Ismail; Nur ‘Atikah Binti Mohd Ali; Junaida Sulaiman
Knowledge Engineering and Data Science Vol 1, No 2 (2018)
Publisher : Universitas Negeri Malang

Climate change is expected to change people’s livelihood in significant ways. Several vulnerability factors and readiness factors used for measuring the prediction index of that particular country on how vulnerable of a country towards global change. Primary data was collected from University of Notre Dame Global Adaptation Index (ND-GAIN). The data has been trained for the forecasting purpose with support from the validated statistical analysis. The summary of the predicted index is visualized using machine learning tools. The results developed the correlation between vulnerability and readiness factors and shows the stability of the country towards climate change. The framework is applied to synthesize findings from Prediction index studies in South East Asia in dealing with vulnerability to climate change.

Adam Optimization Algorithm for Wide and Deep Neural Network Imran Khan Mohd Jais; Amelia Ritahani Ismail; Syed Qamrun Nisa
Knowledge Engineering and Data Science Vol 2, No 1 (2019)
Publisher : Universitas Negeri Malang

The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning. Then, the dataset was fed into a conventional neural network for a benchmark test. Afterwards, the dataset was fed into the wide and deep neural network with and without Adam. It was found that there were improvements in the result of the wide and deep network with Adam. In conclusion, Adam is able to improve the performance of a wide and deep neural network.

Social Distancing Monitoring System using Deep Learning Amelia Ritahani Ismail; Nur Shairah Muhd Affendy; Ahsiah Ismail; Asmarani Ahmad Puzi
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p17-26

COVID-19 has been declared a pandemic in the world by 2020. One way to prevent COVID-19 disease, as the World Health Organization (WHO) suggests, is to keep a distance from other people. It is advised to stay at least 1 meter away from others, even if they do not appear to be sick. The reason is that people can also be the virus carrier without having any symptoms. Thus, many countries have enforced the rules of social distancing in their Standard Operating Procedure (SOP) to prevent the virus spread. Monitoring the social distance is challenging as this requires authorities to carefully observe the social distancing of every single person in a surrounding, especially in crowded places. Real-time object detection can be proposed to improve the efficiency in monitoring the social distance SOP inspection. Therefore, in this paper, object detection using a deep neural network is proposed to help the authorities monitor social distancing even in crowded places. The proposed system uses the You Only Look Once (YOLO) v4 object detection models for the detection. The proposed system is tested on the MS COCO image dataset with a total of 330,000 images. The performance of mean average precision (mAP) accuracy and frame per second (FPS) of the proposed object detection is compared with Faster Region-based Convolutional Neural Network (R-CNN) and Multibox Single Shot Detector (SSD) model. Finally, the result is analyzed among all the models.

Co-Authors Ahsiah Ismail Asmarani Ahmad Puzi Imran Khan Mohd Jais Junaida Sulaiman Mhd Khaled Maen Nadzurah Zainal Abidin Nur Shairah Muhd Affendy Nur ‘Atikah Binti Mohd Ali Samar Bashath Syed Qamrun Nisa

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