Browsing by Author "Marini Othman, Ph.D"

The substantial progress of medical imaging technology in the last decade makes it challenging for medical experts and radiologists to analyse and classify them. Medical images contain massive information that can be used for diagnosis, surgical planning, training, and research. The ability to estimate conclusions without direct human input in healthcare systems using computer algorithms is known as Artificial intelligence (AI) in healthcare. Deep Learning (DL) approaches are already being employed or exploited for healthcare purposes. There is, therefore, a need for a technique that can automatically analyze and classify the images based on their respective contents. DL algorithms open a world of opportunities, and it has been recently used for medical images analysis. Although DL techniques have demonstrated a breakthrough in medical images analysis, research still ongoing to improve the accuracy rate. This research focuses on DL in the context of analysing Magnetic Resonance Imaging (MRI) brain medical images. A comprehensive review of the state-of-the-art processing of brain medical images using DL is conducted in this research. The scope of this research is restricted to three digital databases: (1) the Science Direct database, (2) the IEEEXplore Library of Engineering and Technology Technical Literature, and (3) Scopus database. More than 400 publications were evaluated and discussed in this research. The research focus on both binary classification and multi-class classification. For binary classification, the dataset used is from the brain tumor classification project which contains tumorous and non-tumorous images, and it is available for research and development. For multi-class classification, the dataset contains T1-weighted contrast-enhanced MRI medical images from 233 patients with three types of tumours: meningioma, glioma, and pituitary which is also available for research and development. The proposed neural model is fully automatic brain tumour MRI medical images classification model that uses Convolutional Neural Network (BTMIC-CNN). The model's excellent performance was confirmed using the evaluation metrics and reported a total accuracy of 99%. It outperforms existing methods in terms of classification accuracy and is expected to help radiologists and doctors accurately classify brain tumours’ images. This study contributes to goal 3 of the Sustainable Development Goals (SDGs), which involves excellent health and well-being.

The new era of smartphone mobile applications in Indonesia has been started since the formation of the GOJEK application in 2010, where Gojek became the first super app or multiservice platform to provide more than 20 services. GOJEK is one of the applications that utilizes technological and economic advancements in Indonesia. GOJEK is a company that provides multiple online services such as transportation, delivery of goods and foods. Since 2016, GOJEK has significantly improve Aceh economy and hopefully it will fix many problems that Aceh has been facing so far. Gojek has made positive impact since the beginning and has become an important main role in SMEs, especially in 2020 since the Covid-19 pandemic began to emerge and based on the Demographic Institute of the Faculty of Economics and Business, University of Indonesia (LD FEB UI) the Gojek survey helps buffed the economy for those whose affected by Covid-19, as many as 40 %t SMEs joined Go-food (one of GOJEK service) and 90% are micro scale business. as many as 92% of SMEs says they were able to adapt in pandemic due to GOJEK and almost 50% say they would not be able to survive if they were not part of the Gojek ecosystem. With statement above this study aims to analyze and provide information on what factors that actually influencing user to buy through Multi-Service Platform (GO-JEK) in Aceh in order to find what are those factors that helps SMEs and other micro small-scale business to thrive in this pandemic. In this quantitative study the samples of the study are gathered using a non-probability sampling technique with 95% confidence level and 8% error margin. The data is collected using survey questionnaires through Google Form. In this study the scale is measured using Likert scale. To test the effect of the independent variable with the dependent variable regression linear method is used, where this model using 4 steps in building mediation. These finding provides future evidence supporting current economic and technology innovation on how these 4 variables influencing Use of Multi-Service Platform (GO-JEK) in Aceh.