Determining The Senior High School Major Using Agglomerative Hierarchial Clustering Algorithm

Determining the senior high school major is still a dilemma for some junior high school students. The selection of high school majors must be tailored to the interests, talents and academic skills of students so that later students can develop a better competencies, attitudes and academic skills in...

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Bibliographic Details
Published in:2018 International Seminar on Application for Technology of Information and Communication pp. 225 - 228
Main Authors: Nugraha, Adhitya, Arista Harum Perdana, Mahendra, Agus Santoso, Heru, Zeniarja, Junta, Luthfiarta, Ardytha, Pertiwi, Ayu
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2018
Subjects:
Agglomerative Hierarchical Clustering Algorithm
Clustering
Clustering algorithms
Clustering methods
Computer science
Couplings
Data mining
Major Determination in Senior High School
Seminars
Testing
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Summary:Determining the senior high school major is still a dilemma for some junior high school students. The selection of high school majors must be tailored to the interests, talents and academic skills of students so that later students can develop a better competencies, attitudes and academic skills in the new environment. The selection of the appropriate high scholl major will influence students' interests and abilities in exploring a field of science so that later it will be easier for students to go to university which is expected and in accordance with their current interests and abilities. This will obviously be very beneficial for the student in preparing for his future. Clustering is one technique known in the data mining process. The core concept of clustering is to group a number of data or objects into a group or several groups where each group contains data that has similarities that are very close to other data. There are two types of grouping methods known as hierarchical clustering and partitioning. The hierarchical clustering method consists of several types, namely complete linkage clustering, single linkage clustering, average linkage clustering and centroid linkage clustering. While the partitioning method itself consists of the following types namely k-means clustering and k-means fuzzy clustering. In this study, the authors have applied and analyzed the Agglomerative Hierarchical Clustering technique in the data of students of SMP Negeri 2 Purwodadi to classify students based on their respective interests and skills to fit the selection of high school majors. In the implementation, the author uses 5 attributes of pre-processing results which are used as experimental data processing variables. The results of this study succeeded in developing a prototype application that has implemented the Agglomerative Hierarchical Clustering algorithm which is used to visualize data processing so that it can help students determine high school majors. From the various experiments that have been carried out, this application has shown good resultsl.
DOI:10.1109/ISEMANTIC.2018.8549834