It is important to every business to know their level of market competition, for example customers demand, customers' pattern of buying and their sales performance. This project proposes a sales analysis using prediction and association technique in sales data. Chan Furniture and Electrical Appliances Company have been selected as a case study. From the preliminary study, the researcher found that the Company is facing fluctuating amount of sales throughout the year. The sales obtained are lower than expected. The prediction model is used to predict the product that will be sold for every month. The association model is used to identify the associated products purchased by the customer. Purchasing transactions data is taken from the sales invoices issued by the company. The result obtained from both is used in developing a business strategy for Chan furniture.

Traffic congestion is one of the main problems in traffic system and it occurs on most cities around the world. This project focuses on solving recurring congestion that occurs in area around Kuala Terengganu city. Traffic routing can be implemented to reduce traffic congestion by finding the best path for a vehicle. Ant Colony Optimization algorithm is applied to optimize traffic routing in this project. Ant Colony Optimization (ACO) takes inspiration from the foraging behavior of some ant species and these ants release pheromone on the ground in order to mark some favorable path that should be followed by other ants. Simulation of Urban Mobility (SUMO) is used for the simulation of traffic congestion and traffic routing. Traffic Control Interface (TraCI) gives the program that is written in Python to access the traffic while the simulation is running and it allows to retrieve values of road travel time and to re-route a vehicle based on the path that is found by ACO. The efficiency of ACO is compared with Dijkstra algorithm. It is found that average travel time for vehicle simulation in ACO perform 30% faster than Dijkstra.