PENDEKATAN ATURAN ASOSIASI UNTUK ANALISIS PERGERAKAN SAHAM - Azhari Azhari; Anshori Anshori (Jurusan Teknik Informatika, 2009)
Financial analysis of listed companies is essential in stock investment management for maximizing investment return with controlled risk. Usually the analysis involves large amount of data and the underlying patterns not easy to identify. With the increasing power of computer, data mining becomes possible in the domain of stock investment. The focus of this research is to extract association rules for stock ratio against certain variables. A data mining application for association rules on Jakarta Stock Exchange stock movement archives has been built and to be accessed through the WEB. Preprocessing has been done to convert the data on financial report to financial ratios to allow for direct comparison. Apriori, an algorithm for association rules mining is implemented for efficiency comparison. Apriori algorithm used support and confidence in searching the rules. Support is the amount of transactions which contain an itemset. Confidence is used in order to measure how often an itemset in Y appear in transactions that contain an itemset X. The prototype has been tested for same data stock exchanges. And the results showed that the prototype can construct same association patterns and display it in a bar chart.