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首页> 外文会议>International Seminar on Application for Technology of Information and Communication >Determining The Senior High School Major Using Agglomerative Hierarchial Clustering Algorithm
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Determining The Senior High School Major Using Agglomerative Hierarchial Clustering Algorithm

机译：使用凝聚层间聚类算法确定高中专业

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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 school 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.
机译：确定高中专业仍然是一些初中生的困境。选择高中专业的选择必须根据学生的利益，才能和学术技能量身定制，以便后来学生可以在新环境中培养更好的能力，态度和学术技能。选择适当的高中专业将影响学生的兴趣和能力在探索科学领域，以便稍后，学生将更容易参加大学，预计并按照他们目前的利益和能力。这显然对学生在为其未来做准备方面非常有益。聚类是数据挖掘过程中已知的一种技术。群集的核心概念是将许多数据或对象分组到一个组或几个组中，其中每个组包含具有非常接近其他数据的相似性的数据。称为分层群集和分区的分组方法有两种类型的分组方法。分层群集方法由几种类型组成，即完全链接聚类，单链接群集，平均链接聚类和质心链接聚类。虽然分区方法本身由以下类型组成，但是K-means群集和k-means模糊聚类。在这项研究中，作者已经申请并分析了SMP Negeri 2 Purewodadi学生数据中的群集分层聚类技术，以根据他们各自的利益和技能来分类学生，以适应高中专业的兴趣和技能。在实现中，作者使用5个属性的预处理结果，其用作实验数据处理变量。该研究的结果成功开发了开发了一种实现的原型应用，该应用程序已经实现了用于可视化数据处理的聚集分层聚类算法，以便它可以帮助学生确定高中专业。从已经进行的各种实验中，本申请表明了良好的结果。

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