A candidate for legislative member is someone who is sent to represent his political party in a legislative part which will be directly elected by the people through general elections. General election is a means of implementation in a democratic system to elect representatives of the people in the people's representative institutions directly, publicly, freely, confidentially, honestly and fairly. Legislative elections are a five-year democratic agenda that is held simultaneously, one of which is to elect members of the Provincial DPRD. Provincial DPRD is a regional people's representative institution that is domiciled as an element of provincial government administration. The purpose of this study is to understand the application of the K-Nearest Neighbor Algorithm, to design a system that can determine the success of legislative candidates in the general election in the Jambi provincial constituency using Data Mining with the K-Nearest Neighbor Algorithm, to test using Rapidminer supporting software, to test the system. Data Mining that has been built uses test data, and analyzes the output generated from the Data Mining system. The appropriate method for this research is Data Mining. Data mining is a data processing method to find patterns in data, the k-nearest neighbor algorithm is one of the classification algorithms on objects based on learning data that is closest to the object. The data used and processed in this research is the data of the elected DPRD members for the previous period, namely the data of the 2009-2014 DPRD members, the 2014-2019 DPRD members' data, and the 2019-2024 DPRD members' data. The results of this study are a system that can provide recommendations for prospective Provincial DPRD candidates for the 2024-2029 period in choosing electoral districts that match the profile and have a greater potential for victory during the General Election based on profile data of elected DPRD members in the previous period.
CITATION STYLE
Rivatunisa, C. (2022). Potensi Keberhasilan Bakal Calon Legislatif Menggunakan Algoritma K-Nearest Neighbors. Jurnal Sistim Informasi Dan Teknologi, 47–51. https://doi.org/10.37034/jsisfotek.v4i2.122
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