Visualisasi Pemain Sepak Bola Indonesia pada DBPedia dengan menggunakan Node2Vec dan Closeness Centrality
DOI:
https://doi.org/10.24002/jbi.v11i2.3346Abstract
Abstract. Visualization of Indonesian Football Players on DBpedia through Node2Vec and Closeness Centrality Implementation. Through Semantic Web, data available on the internet are connected in a large graph. Those data are still raw so that they need to be processed to be an information that can help humans. This research aims to process and analyze the Indonesian soccer player graph by implementing node2vec and closeness centrality algorithm. The graph is modeled through a dataset obtained from the DBpedia by performing a SPARQL query on the SPARQL endpoint. The results of the Node2vec algorithm and closeness centrality are visualized for further analysis. Visualization of node2vec shows that the defenders are distributed over the players. Meanwhile, the result of closeness centrality shows that the strikers have the highest centrality score compared to other positions.
Keywords: visualization, node2vec, closeness centrality
Abstrak. Dengan adanya web semantik, data yang tersebar di internet dapat saling terhubung dan membentuk suatu graf. Data yang ada pada graf tersebut masih berupa data mentah sehingga perlu dilakukan pengolahan agar data mentah tersebut dapat menjadi informasi yang dapat membantu manusia. Penelitian ini bertujuan untuk melakukan pengolahan dan analisis terhadap graf pemain sepak bola Indonesia dengan mengimplementasikan algoritma node2vec dan closeness centrality. Graf dimodelkan melalui dataset yang didapat dari website DBpedia dengan cara melakukan query SPARQL pada SPARQL endpoint. Hasil dari algoritma node2vec dan closeness centrality divisualisasikan untuk dianalisis. Visualisasi dari node2vec menunjukkan pemain defender tersebar. Hasil closeness centrality menunjukkan bahwa pemain striker memiliki nilai tertinggi daripada posisi lainnya.
Kata Kunci: visualisasi, node2vec, closeness centrality
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