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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Informatika Upgris RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTECOMS: Journal of Information Technology and Computer Science MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JURNAL MAHAJANA INFORMASI Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal Software Engineering and Computer Science (IJSECS) SENTRI: Jurnal Riset Ilmiah Duta Abdimas: Jurnal Pengabdian Masyarakat Jurnal Informatika Dan Tekonologi Komputer Indonesian Journal on Learning and Advanced Education (IJOLAE)
Edy Supriyanto
Universitas Stikubank
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Penerapan Social Network Analysis dengan Network X untuk Melihat Derajat Sentralitas pada Dataset Jaringan Sosial Widianto Tri Handoko; Sariyun Naja Anwar; Edy Supriyanto; Endang Lestariningsih
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.8768

Abstract

Social Network Analysis (SNA) is a crucial quantitative methodology for mapping relationships and identifying connectivity structures within a group. This research specifically explores the use of the NetworkX library in Python as an effective tool for analyzing social networks. The primary objective of this study is to apply the Degree Centrality method to measure the level of connectivity and identify the most popular actors in a social network. The methodology employed is the quantitative analysis of an undirected graph modeled from the us_edgelist.csv dataset, which contains a list of relationships among political figures in an edge list format. Data processing utilized pandas, and the graph object was constructed using NetworkX. Degree Centrality was calculated for each node, with the results being normalized to provide a relative value. This normalization allows for a direct comparison of how active each actor is within the network. The centrality results were then visualized, with node sizes adjusted based on their Degree Centrality score. The results of the analysis indicate that figures like Bush and Obama possess the highest Degree Centrality score, 0.25, suggesting they have the greatest number of direct connections in this network. This high value confirms their role as the most active or central actors in the exchange and interaction within the political network studied. This finding validates the effectiveness of Degree Centrality as an indicator of high involvement. The study concludes that the implementation of Social Network Analysis using NetworkX provides a robust framework for understanding political relationship structures. Therefore, Degree Centrality is a reliable metric for quantifying actor activity and accurately identifying individuals who form the center of connections within the network.
Co-Authors Agus Ariyanto Alifan Purnomo Aji Budiningsih, Iffah Cahyono Rahadiyanto CANDRA KITSI AWALUDDIN Eka Ardhianto Eko Nur Wahyudi Endang Lestaringsih Endang Lestariningsih Fakhri Zufar Abhirama Fitika Andraini Fitri Indahsari Dewi Susanti Galih Yunus Al Anas Handoko Handoko Hari Murti Harlan Yudha Pratama Heni Candra Kirana Irfan Mahdi Jeffri Alfa Razaq Muhammad Alif Samudera Murti, Hari Nova Fadhilah Rahmadian Purwatiningtyas Purwatiningtyas R. Soelistijadi Rara Sri Artati Redjeki Rara Sri Artati Redjeki Sariyun Naja Anwar Suluh Hidayat Sutrasno Andre Wibowo* Theresia Dwiati Wismarini Tjiptogoro Dinarjo Soehari W. T. Handoko W. T. Handoko Wakhid Rokhayadi Wawan Septiana Widianto Tri Handoko Widiyanto Tri Handoko