Penggunaan Algoritma SLIQ untuk Pengklasifikasian Kinerja Akademik Mahasiswa

Abstract

Academic data increases every year in line with the increase of students. Abundant data store is also
an abundance of information. Data mining technology is a tool for extracting information on large
databases and has been widely used in many domains. Predicting student performance (study evaluation) is
an activity to determine a future state based on existing data. Data in the field of academic research has
been done with various methods and algorithms, but the use of algorithm SLIQ (Supervised Learning In
Quest) has not been done.
SLIQ is an algorithm developed by the IBM's Quest project team in 1996 for mining large datasets.
SLIQ algorithm classify and predict the students performance, beginning with the data cleaning, conducted
election training and testing data. By calculating gini index of each attribute and then selecting the
smallest gini index data table is split according to the criteria until find the same class. From the results of
the calculation process can produce a set of rules that can be used to predict student performance.
From the experiment it can be concluded that the algorithm SLIQ with decision tree technique can
be used as an alternative in designing a system datamining applications. Tests conducted system showed
that the constructed model can be used to predict the performance of new students. The resulting accuracy
of the model system in fact has a lower score than the accuracy of other applications that are used as a
comparison of Tanagra. Advantages of the proposed system is in its design does not need complex
calculations in obtaining the gini index attributes.