Determining eligible villages for mobile services using k-NN algorithm

Anton Yudhana(1); Imam Riadi(2); M Rosyidi Djou(3*);

(1) Universitas Ahmad Dahlan
(2) Universitas Ahmad Dahlan
(3) Universitas Ahmad Dahlan
(*) Corresponding Author



To maximize and get population document services closer to the community, the Disdukcapil district of Alor provides mobile services by visiting people in remote villages which difficult-to-reach service centres in the city. Due to a large number of villages and limited time and costs, not all villages can be served, so the kNN algorithm is needed to determine which villages are eligible to be served. The criteria used in this determination are village distance, difficulty level, and document ownership (Birth Certificate, KIA, family card, and KTPel). The classes that will be determined are "Very eligible", "Eligible", and "Not eligible". By applying Z-Score normalization with the value of K=5, the classification gets 94.12% accuracy, while non-normalized only gets 88.24% accuracy. Thus, applying normalization to training data can improve the kNN algorithm's accuracy in determining eligible villages for "ball pick-up" or mobile services.


Data Mining; kNN; Determining Villages; Dukcapil Mobile Service; Public Service


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