Paper Title
Predicting A Service Requestforecasting of Regional Standards & Testing Laboratory for Food and Quality Assurance

Abstract
Demand forecasting are crucial for managing supply and when properly applied will help address the surpluses or shortage of materials requested for a certain period of time. This paper explores and addresses the problems of forecasting the services requested of RSTL in the government agency in one of the province of the Philippines to correctly determine the number of materials needed to conduct a test. This involves the activity of generating a forecasting models for each test services to determine the number of supplies and materials that RSTL will purchase. In this research, the researcher used data mining techniques to forecast the number of supplies needed based on the customer’s laboratory services request. The researcher also discussed each food test request to provide the reader an insight of the test and why it was needed to test. Database record from year 2014 to 2016 were extracted, group, summarized and presented applying Knowledge Discovery for Data (KDD) methodology to evaluate them. Result of the evaluation shows that the data mining techniques were effectively forecast the trend especially in a seasonal pattern. It was also noticeable that test service harmful to human health when consume and those test that concerned with the shelf life of the product have a high request while low demand for the test request for healthy product contents. Keywords - Kdd, Trend And Seasonality, Data Mining, Test Services, Shelf Life