• Kode Repository : SKF62/ADE/19
• NPM : 065114185
• Nama : Ade Muchlis Maulana Anwar
• Pembimbing 1 : -Prihastuti Harsani, Msi.
• Pembimbing 2 : -Aries Maesya M.Kom
• Abstrak : -PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS Ade Muchlis Maulana Anwar1), Prihastuti harsani2), Aries Maesya3) 1, 2 & 3)Program Studi Ilmu Komputer, FMIPA, Universitas Pakuan Bogor 1)ades.muchlis@gmail.com, 2) prihastuti.harsani@unpak.ac.id , 3) a.maesya@unpak.ac.id Abstract Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. k-nearest neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. This study discusses the k-nearest neighbor method to predict late reporting of birth certificates, which then the data will be grouped with the k-means method to classify priority areas of service. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179. Keywords : k-nearest neighbor, k-means, population, clustering, data mining
• Program Studi : Ilmu Komputer