Fredy Purnomo

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.

The effective noise removal techniques and illumination effect in face recognition using Gabor and Non-Negative Matrix Factorization

There is always noise inside the digital images. Noise is an unwanted component of the image. The existence of noise in a face image can degrade the accuracy of a face recognition. Therefore, we need a proper method that can cope noise or restore the quality of the image. The best method to overcome noise in the image is to use smoothing (filter). In this research, we discuss some techniques to overcome noise in face recognition task using Gabor and Non-Negative Matrix Factorization (NMF), as it is stated in the previous research that it still cannot handle images with noise yet. The noises discussed in this research consist of impulse noise (salt-and-pepper), additive noise (Gaussian) and multiplicative noise (speckle). The experiment was conducted by using two face databases; they were ORL and Extended Yale B. The result said that mean filter is the best coping technique for Gabor and NMF face recognition methods. We used K-Nearest Neighbors (KNN) as the classifier and it achieved 90.83% accuracy rate.

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.