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All Journal Media Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI)
Khairuddin Omar
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Computer Science & IT

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Genetic Algorithm with Center Based Chromosomal Representation to Solve New Student Allocation Problem Zainudin Zukhri; Khairuddin Omar
Media Informatika Vol. 5 No. 2 (2007)
Publisher : Department of Informatics,Faculty of Industrial Technology,Islamic University of Indonesia

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Abstract

Genetic Algorithm (GA) is one of the most effective approaches for solving optimization problem. We have a problem difficulty for GA in clustering problem. It can be viewed as optimization problem, that is maximization of object similarity in each cluster. The objects must be clustered in this paper are new students. They must be allocated into a few of classes, so that each class contains students with low gap of intelligence and they must not exceed the class capacity. The intelligence gap of each class should be low, because it is very difficult to give good education service for the students in the class whose high diversity of achievements or high variation of skills. We call this problem as New Student Allocation Problem (NSAP). Initially, we apply GA with Partition Based Chromosomal Representation (PBCR). But experiments only provide a small scale case (200 students and 5 classes with same capacities). Then we try to apply GA with Center Based Chromosomal Representation (CBCR) and we evaluate it with the same data. We have successfully improved the performance with this approach. This result indicates that chromosomal representation design is the important step in GA implementation. CBCR is better than PBCR in all aspects. All classes generated by CBCR approach have largest gap of intelligence in each class less than generated by PBCR. CBCR approach can reduce these values almost a half of the values with PBCR approach.
Implementation of Genetic Algorithms to Cluster New Students into Their Classes Zainudin Zukhri; Khairuddin Omar
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2006
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Clustering new students into their classes at random probably make an educational problem, because the smartest student maybe clustered in a same class with the most stupid one. To avoid this problem, we can use sorting-score method which cluster new students based on their achievements. First, we sort the average of their scores, and then make the clusters(classes) base on its. This method is not so worse than the first one, but only the smartest class and the most stupid class that have a low gap. There are high gaps in the middle classes. This research tries to explore Genetic Algorithm (GA) to solve this problem. Experimental studies show that performance of GA is better than sorting-score method. The gap of intelligence in classes clustered by GA are relatively same each other. GA can reduce maximum gap in class that clustered by sorting-score method.Keywords: cluster, Genetic Algorithm, similarity, student.
Comparative Evaluation of Genetic Algorithm and Modification of Agglomerative Method for Allocating New Students Zainudin Zukhri; Khairuddin Omar
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2007
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Allocating new students into their classes is a clustering problem, that is how to cluster new students intotheir classes so that each class contains students in the number that less than or equals to its capacity and hasminimum gap of intelligence. It needs a suitable method to avoid an educational problem. This paper describesthe comparison of Genetic Algorithm (GA) and Modification of Agglomerative Methods (AM) for solving thisproblem. To determine which method is better then the other, the software of each method which can cluster nstudents with m attributes into c classes are evaluated by two-dimensional random data consists of 200 students.Then we compare the results. Comparison of GA and AM for clustering the data sets shows that although the GAcluster the data successfully, the method provides no advantages over AM. Intelligence gap of students in eachclass clustered by GA almost same each other, but the average of this value is greater than by AM. Meanwhile,the intelligence gap of student clustered by AM depend on the clustering sequence. This GA performance may beis caused by unsuitable GA approach, both chromosome representation and GA operators in this research.Better GA approach may enhance the effectiveness of the GA searching.Keywords: Agglomerative Method, cluster, Genetic Algorithm, student.
Co-Authors Zainudin Zukhri