Abstract: The main focus of lower or higher education institutions is to provide the quality education to its students. One way to achieve the highest level of quality education system is through discovering the hidden knowledge that relied in huge educational datasets. There is an increasing trend in using data mining in education. This new emerging field, called Educational Data Mining (EDM), concerns with developing methods that discover knowledge from data originating from educational environments. Examples of educational domain are discovering knowledge for prediction regarding enrollment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the student’s result sheet, prediction about student’s performance and analyzing a pattern of student’s class attendance and student’s class absenteeism. The main objective of this paper is to study and compare the use of several data

• Publication Date: 05-May-2013
• DOI: 10.15224/978-981-07-6261-2-14
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Abstract: one of the most important issues in dairy farm is feed management which aims to manage available and required feed. There are several problems associated with managing cattle feed such as how to best satisfy feed deficits or how to utilize extra feed available. Since early 70s, feed planning decision support systems have been developed to help farmers addressed issues. However, many of those systems have failed because of several reasons such as being designed for specific geographical region, missing dairy farm’s production variables (e.g. milk yield per cow) or using very complex model which is hard to understand and handle. The objective of our research is to proposed an advanced web-based decision support system to address issues of feed management on the basis of stock inputs (required feed estimated based on stock numbers) and basic pasture (available feed for cattle). The system then provides a solution for 12 months feed planning which can be reviewed and improved if required.

• Publication Date: 05-May-2013
• DOI: 10.15224/978-981-07-6261-2-15
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