The Implementation of K-Means Clustering Method in Classifying Undergraduate Thesis Titles

摘要

One of graduation requirements at university is completing undergraduate thesis. At Industrial Engineering Universitas Ahmad Dahlan, undergraduate thesis titles are documented by thesis coordinator. The problem is that students are less knowledgeable on thesis topics, so they do not really know the previous students' thesis topics. Based on the problem, this research aims at developing a program to classify thesis title so the knowledge on the trend of thesis title topic can be got The method used in this research was K-Means clustering, while range measurement method used was cosine similarity. The testing used Silhouette Coefficient method. The phases from text mining were tokenizing, filtering, stemming, similarity, classifying, testing. The result of this research is a program that can process the title data into trend group pattern of thesis title topic. From 138 data obtained, there are three clusters arranged based on the field on Industrial Engineering study program. Silhouette Coefficient testing shows score of 0.5674 that shows the clustering result is classified low. It occurs since the textual data of the thesis title is too widely distributed, so the title has relatively low similarity score.