Michael Yoseph Ricky

Mobile Application Software Defect Prediction

The increasing number of user of mobile application, it is needed to check mobile applications that contains defect or not. I proposed a SVM method in comparison with CART and Test Metrics to classify classes in application. It shows that SVM method has better result in terms of precision and accuracy. SVM accuracy reaches 83% compared with CART and Test Metrics method in mobile apps defect prediction.

Smart city's context awareness using social media

The increasing number of inhabitants of a city, there will be more challenges in the management of the city. Many events that can not be controlled by either causing the slow response of the relevant institutions. Sensing the smart city through social media is offered for such a solution. Text mining is done to analyze the social media posts based on events that occurred and the emotion that follows is based on text, hashtag and geo-tagging. Methodology used is text mining approach kernel methods, particularly the support vector machine (SVM). Results are expected with this concept is the city that can listen to the aspirations and desires of the population quickly and accurately.