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

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.496 KB) | DOI: 10.17977/um018v1i22018p74-78

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1276.877 KB) | DOI: 10.17977/um018v2i12019p41-46

Abstract

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.
Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare Amelia Ritahani Ismail; Nadzurah Zainal Abidin; Mhd Khaled Maen
Journal of Robotics and Control (JRC) Vol 3, No 2 (2022): March
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i2.13133

Abstract

Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human errors, instrumental failures, and adverse death. Therefore, to accurately deal with incomplete data, a sophisticated algorithm is proposed to impute those missing values. Many machine learning algorithms have been applied to impute missing data with plausible values. However, among all machine learning imputation algorithms, KNN algorithm has been widely adopted as an imputation for missing data due to its robustness and simplicity and it is also a promising method to outperform other machine learning methods. This paper provides a comprehensive review of different imputation techniques used to replace the missing data. The goal of the review paper is to bring specific attention to potential improvements to existing methods and provide readers with a better grasps of imputation technique trends.
Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem Samar Bashath; Amelia Ritahani Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp300-307

Abstract

High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selecting one algorithm among others for solving the high dimensional optimization problem is not an easily accomplished task. This paper presents a comprehensive study of two swarm intelligence based algorithms: 1-particle swarm optimization (PSO), 2-cuckoo search (CS).The two algorithms are analyzed and compared for problems consisting of high dimensions in respect of solution accuracy, and runtime performance by various classes of benchmark functions. 
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

Abstract

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