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Klasterisasi Pahlawan Nasional Indonesia Berdasarkan Daerah Asal Menggunakan Algoritma Community Detection
Abstract
Abstrak
Kisah sejarah sampai kemerdekaan Indonesia tak pernah lepas dari kurikulum pendidikan wajib belajar 12 tahun. Namun seiring dengan perkembangan zaman, hipotesis-hipotesis sejarah Indonesia juga ikut berkembang. hipotesis-hipotesis juga membantu sejarawan untuk melihat sejarah Indonesia menjadi lebih utuh. Untuk mendukung perkembangan tersebut, penulis mencoba mengimplementasikan algoritma Community Detection untuk mencari keterkaitan antar pahlawan nasional. Sumber data pahlawan nasional yang digunakan pada penelitian ini adalah id.dbpedia. Namun akibat inkonsistensi dan sedikitnya data yang disimpan di id.dbpedia, maka pengumpulan data tidak berhenti di id.dbpedia saja. Salah satu sumber data yang jumlah datanya lebih banyak dari id.dbpedia adalah Wikipedia. Data yang diperoleh kemudian dilakukan standarisasi secara manual, dan dibuatkan graf di Jupyter. Algoritma Community Detection digunakan untuk menemukan klaster-klaster komunitas pahlawan nasional Indonesia berdasarkan data daerah dan tahun. Community Detection menunjukkan bahwa data yang diolah dengan menggunakan Algoritma Greedy Modularity memiliki 16 komunitas atau kelompok. Selain itu, sebuah pola ditemukan pada 6 komunitas yang ada berdasarkan Algoritma Girvan Newman Sehingga dapat disimpulkan bahwa Algoritma Greedy Modularity menghasilkan lebih banyak klasifikasi komunitas.
Kata Kunci: algoritma, klasterisasi, deteksi komunitas, pahlawan nasional indonesia, daerah, tahun
Abstract
The story of history Indonesia until Indonesia's independence has never been separated from the 12-year compulsory education curriculum. However, along with the times, historical hypotheses in Indonesia also developed. Hypotheses also help historians to see Indonesian history more fully. To support this development, the author tries to implement the Community Detection algorithm to find linkages between national heroes. The data source for the national heroes used in this study is id.dbpedia. However, due to inconsistencies and the lack of data stored on id.dbpedia, data collection does not stop at id.dbpedia only. One of the data sources with more data than id.dbpedia is Wikipedia. After the data was obtained, then it standardized manually, and graphs were made in Jupyter. The Community Detection algorithm is used to find clusters of Indonesian national hero communities based on regional and year data Community Detection shows that the data processed using the Greedy Modularity Algorithm generates 16 communities or groups. In addition, a pattern was found in 6 existing communities based on the Newman Girvan Algorithm. So it can be concluded that the Greedy Modularity Algorithm produces more community classifications.
Keywords: algorithm, clustering, community detection, indonesian national heroes, region, years
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DOI: https://doi.org/10.32520/stmsi.v10i1.1068
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