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Sentiment analysis (SA) is the study of people's emotions and attitudes toward a particular topic. It is beneficial for monitoring and analyzing social media text in order to gather public opinion. Despite the fact that there are SA applications for monolingual text such as English and non-English languages like Hindi, Chinese and French, the Malay language has far fewer works, not to mention the mixed language such as Malay-English (also known as Manglish). Other than comments and posts from websites and social media, the emoji used by internet users can also help to provide better insights into how they truly feel about a particular topic. Our work focuses on Malay-English mixed language comments and posts on how Malaysians feel about daily new cases of Covid-19 in Malaysia. We proposed a neural network framework to perform SA on languages spoken by Malaysians, namely Malay, English, and Malay-English, by also taking into account the emoji used by internet users. The data was pre-processed to remove noises and then transformed into word vector representation using word embedding technique. Then we propose a framework that involves training and testing mixed language textual data along with emoji analysis by using bidirectional Long Short Term Memory (biLSTM) neural network. To compare with the proposed method, several machine learning models and Long Short Term Memory (LSTM) with word vectorization was used. Finally, compared to the machine learning model such as Naïve Bayes and Logistic Regression, neural networks such as LSTM, the proposed method; biLSTM with tuned hyper-parameter for Malay-English mixed language achieved the highest accuracy of 76.6%, and macro F1-score of 69.6%. [ABSTRACT FROM AUTHOR]

Relatively more smartphones developed nowadays come with GPS location software. GPS information is fetched from the satellites and can be retrieved from Bluetooth, wi-fi, cell-id, and a combination of those tools. GPS-based mobile apps have the features of fetching the location of a device using geo-coordinates and some measurement which is an added value in developing various software. Due to the pandemic disease of Covid-19, the world is now aiming for global digitalization. Countries such as Singapore, Chine, and even Malaysia developed applications that can help the citizen in obeying the SOP and receiving current and reliable information regarding the Covid-19 issue. Thus, a real-time monitoring application for PUS s beneficial to break the chain and contain the disease. This research has been done to develop a localization application for Person Under Surveillance using GPS mobile apps. The application implemented the Firebase Authentication in the sign-up and sign-in process and is developed using Android Studio IDE 4.1 integrated with cloud-hosted database Firebase Realtime Database. Android version 6.0 is used which is API level 23. Application is developed to have the functionalities of user registration, fetching coordinates, setting a boundary, and in-app alert. The application was installed and tested on the Pixel 3 API 23 emulator. [ABSTRACT FROM AUTHOR]