Hafiz Mohd Sarim

Water flow algorithm decision support tool for travelling salesman problem

This paper discuss about the role of Decision Support Tool in Travelling Salesman Problem (TSP) for helping the researchers who doing research in same area will get the better result from the proposed algorithm. A study has been conducted and Rapid Application Development (RAD) model has been use as a methodology which includes requirement planning, user design, construction and cutover. Water Flow Algorithm (WFA) with initialization technique improvement is used as the proposed algorithm in this study for evaluating effectiveness against TSP cases. For DST evaluation will go through usability testing conducted on system use, quality of information, quality of interface and overall satisfaction. Evaluation is needed for determine whether this tool can assists user in making a decision to solve TSP problems with the proposed algorithm or not. Some statistical result shown the ability of this tool in term of helping researchers to conduct the experiments on the WFA with improvements TSP initialization.

MYNDA: an intelligent data mining application generator

Development of a Decision Support System (DSS) based on data mining is expensive. It consists of three main phases: produce quality input data, develop quality knowledge models and developed an application based on the model, which needs experts in the domain, data mining and software development respectively. Current commercial data mining tools, such as Insightful miner, aims for the development of quality knowledge models which are conducted by data mining expert. The knowledge model is not meaningful to the end user without the development of a DSS application based on the knowledge model. Mynda is a web-based data mining tool for domain expert users to generate knowledge models from clients data (model generator) and also generate a data mining application from the knowledge model (application generator). The user only provides input data sets (for example in Excel format) and set the mining technique profile. Mynda will automatically develop the knowledge model and generate an executable data mining application based on the profile. The data mining application can be run independently as a stand alone application. Mynda has reduced the complexity of the development of data mining based DSS applications.

Solving the viva presentations timetabling problem: A case study at FTSM-UKM

There is a growing need to automatically timetable viva presentations for postgraduate candidates due to the increasing number of students enrolled each year, and hence, requiring additional personnel effort. The automatic timetabling process involves the assignment of the people involved in the viva timetable into a limited number of timeslots and rooms. In order to produce a feasible timetable, we must satisfy some regulations (hard constraints), while attempting to accommodate as much as possible some preferences (soft constraints). In this work, we tackle the problem of scheduling viva presentations for the Masters degree students at FTSM-UKM as a case study. Each presentation must be attended by a chair of the school (or representative), a chair of the viva presentation, a technical committee member, a student (presenter), an internal examiner and supervisor(s). The presentation must be scheduled into a room and timeslot. In this work, we propose a new objective function to model the problem and to evaluate the quality of the timetable (schedule). We also introduce a greedy constructive heuristic to construct a valid timetable that satisfies all of the hard constraints and tries to satisfy the soft constraints as much as possible. The heuristic will assign the committee and students into an empty timetable based on a pre-ordered list of prioritized elements. These elements are ordered based on the largest enrolment: specifically a technical person who has the largest number of students enrolled under his/her supervision and examination will be ordered first in the list and is first to be assigned into the timetable. Results show that the automated timetabling solver can efficiently produce good quality timetable in reasonable time.