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K.A.F.A. Samah
Universiti Teknologi MARA (Kampus Jasin)

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Business Intelligence for Paintball Tournament Matchmaking Using Particle Swarm Optimization M.T. Mishan; A.F.A. Fadzil; K.A.F.A. Samah; N.F. Baharin; N. Anuar
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp599-606

Abstract

Paintball has gained a huge popularity in Malaysia with growing number of tournaments organized nationwide. Currently, Ideal Pro Event, one of the paintball organizer found difficulties to pair a suitable opponent to against one another in a tournament. This is largely due to the manual matchmaking method that only randomly matches one team with another. Consequently, it is crucial to ensure a balanced tournament bracket where eventual winners and losers not facing one another in the very first round. This study proposes an intelligent matchmaking using Particle Swarm Optimization (PSO) and tournament management system for paintball organizers. PSO is a swarm intelligence algorithm that optimizes problems by gradually improving its current solutions, therefore countenancing the tournament bracket to be continually improved until the best is produced. Indirectly, through the development of the system, it is consider as an intelligence business idea since it able to save time and enhance the company productivity. This algorithm has been tested using 3 size of population; 100, 1000 and 10,000. As a result, the speed of convergence is consistent and has not been affected through big population.
Brute force algorithm implementation for traveljoy travelling recommendation system K.A.F.A. Samah; N. Sabri; R. Hamzah; R. Roslan; N.A. Mangshor; A.A.M. Asri
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp1042-1049

Abstract

This paper presents the Brute Force algorithm implementation for TravelJoy Travelling Recommendation System.  Due to overwhelmed information in the internet, travelers faced difficulties in finding and comparing which places in Melaka that worth to visit. Melaka is a well-known place as one of the most popular tourist spots in Malaysia, famous with historical places. All the mentioned problems were time-consuming and required lots of efforts for manual comparison between places and planning the trip itinerary. An efficient application system is needed to assist travelers in planning their trip itinerary by providing details of interesting place in Melaka, budget estimating and recommendation of sequence places which to visit. The TravelJoy application applied Traveling Salesman Problem (TSP) concept using Brute Force algorithm in determining the least time duration for the selected places and adapting Expected Time Arrival (ETA). It was found through Brute Force algorithm adaptation; the recommendation system is reliable based on the functional and reliability testing with t-test result of 0.00067, indicates the system is accepted.
Co-Authors A.A.M. Asri A.F.A. Fadzil M.T. Mishan N. Anuar N. Sabri N.A. Mangshor N.F. Baharin R. Hamzah R. Roslan