Livestock feed blend formulation is an important process in livestock industry.  This process will help the livestock industry nowadays to keep providing continuous supply of animal protein food to cater for the expanding and increasing demand as Malaysia is undergoing a rapid growth in economic and human population.  The formulation of feed blend involves multiple objectives to be achieved through the decision making process.  In this project, Goal Programming (GP) method is used to formulate the livestock feed blend for a farm situated in Negeri Sembilan, Malaysia. This method is an approach of assisting the decision makers to solve multiple objectives for livestock feed blend in determining an optimal combination of ingredients to meet the nutritional requirements. This will lead to a rational use of available resources by minimizing the production cost and maximizing the nutritional value required for the growth of livestock. The nutrition for the livestock is dry matter (DM), metabolism energy (ME), crude protein (CP) and crude fiber (CF). Then, the preemptive model is tested using LINGO software and the results have been validated by using Mean Absolute Percentage Error (MAPE).  All of the multiple objectives have been fully achieved which represents the ability of the goal programming model to comply with optimizing the feed blend formulation.

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Consensus method is a means of communication between experts who assist the formation of a group judgment. This technique has great potential to be adopted to provide prediction based on outcomes obtained from several classification algorithms. In this study, data on water consumption was used to induce the classification model that will be used to predict the possibility of the occurrence of water leakage at residential premises. The classification algorithms used in the analysis using consensus method were selected based on their generalization ability on simulation datasets. The prediction outcomes were obtained based on analysis on individual algorithm classification outcomes on validation dataset. The finding showed a very promising result where consensus method produces consistent prediction outcomes.