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Emotions hold a paramount role in the conversation, as it expresses context to the conversation. Text/word in conversation consists of lexical and contextual meanings. Extracting emotions from text has been an interesting work recent thees years. With the advancement of machine learning techniques and hardware to support the machine learning process, recognising emotions from a text with machine learning provides promising and significant results. This research aims to explore several popular machine learning to recognise emotions from a conversation in social media. The algorithms proposed in this research are ranged from traditional machine learning to deep learning techniques. The dataset used in this paper is provided by AffectiveTweets, with a baseline of F1Score of 0.71 with word N-grams and SentiStrength. The research contributes extensive explorations in a number of machine learning algorithms, resulting in a total of 2302 features sets were explored, where each features sets has 100-1000 features extracted from the text. The results demonstrate Generalised Linear Model provides the best Accuracy score (0.92), Recall (0.902), Precision (0.902), F1 score (0.901) with standard deviation of accuracy of ±1,2%. [ABSTRACT FROM AUTHOR]

This research was conducted with the aim of utilizing the human hands as an object to operate computers. It is intended to support and use technologies especially in the field of education for helping teachers to do presentation with ease. The program is developed by using a science field of computer vision, as well as additional libraries which are: FLTK, OpenGL, and OpenCV. In order to use this program, the presenters need to have a webcam and a projector. The webcam will be used to recognize the shape and the pattern of the presenters’ hands. Furthermore, the program will send signals to the computers based on the recognized pattern. The result of this research is a program that can improve the teaching process of teaching/presentation. [ABSTRACT FROM AUTHOR]

When we play tower defence game, generally we repeat the same stages several times with the same enemies. Moreover, when the players play a stage that is ridiculously hard or way too easy, they would probably quit the game because it ismoderately frustrating or boring. The purpose of this research is to createa game that can adapt to the players’ ability so the difficulty of the game becomes dynamic. In other words, the game will have different difficultiesof levels according to the players’ ability. High difficulty levels will be set if the players use good strategy and low difficulty levels will be set if the players use bad strategy. In this work, we determine the difficulties based on players’ lives, enemies’ health, and passive skills (skill points) that are chosen by the player. With three of these factors, players will have varies experience of playing tower defence because different combination will give different results to the system and difficulties of the games will be different for each gameplay. The result of this research is a dynamic difficulty tower defence game, dynamic difficulty adjustment (DDA) document, and gameplay outputs for best, average, and worst strategy cases. [ABSTRACT FROM AUTHOR]