Abstract: One of the higher education problems is to map out students potential effectively to achieve a more optimal education results. The mapping could be represented by choosing the right department, choosing thesis title, or choosing the right optional subject. Higher education is an institution that resulting many data including the student’s academic profile which can be utilized to fulfil this objective. Data mining is a technology which can interface this need. One of the data mining variant is association analysis and frequent itemset mining which are seeking the connection pattern between one attribute or item and the other. Data attribute that shows up often at the same time means that they have a strong association connection and can make a pattern used as information. Apriori algorithm is a popular algorithm that is used in association data mining. Although this algorithm have several disadvantages, this algorithm still commonly used because it’s easiness to implement and it’s flexibility to improved and adjusted with the purpose. As the addition, several publications have suggested some improvement for this algorithm, such as limiting the number of rules.  In this research, the utilization of Apriori Algorithm to extract knowledge from academic’s profile data could not yet resulted in aimed recommendation due to the lack distribution of optional course which results in the lack of knowledge seeking result variation and only focused on certain optional course as the result of knowledge seeking.
Keywords: Academic’s Profile Data, Data Mining, Apriori Algorithm, Choosing Course, Rule Limitation
Penulis: Andhik Budi Cahyono, Sukrisno Mardiyanto
Kode Jurnal: jptindustridd160146

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