METODE SAMPLE BOOSTRAPING PADA K-NEAREST NEIGHBOR UNTUK KLASIFIKASI STATUS DESA - Eko Siswanto; Suprapedi Suprapedi; Purwanto Purwanto (Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro, 2019)
The Ministry of Rural Area, Remote Area Development and Transmigration divides village itself into five villages, such as, Independent Village, Advance Village, Developing Village, Remote Village and Very Extremely Remote Village. The data are based on Village Potency (Podes) in 2014 by the Ministry of Rural Area, Remote Area Development and Transmigration. It is necessary to know that the data of The Ministry of Rural Area, Remote Area Development and Transmigration can be used to predict the relationship between village development indicators and the status of villages. In this case, it means whether the indicators, which are built, can influence the status of villages or not and whether they can make the status of villages become better than before. k-Nearest Neighbor (k-NN) algorithm is a method which is used to classify toward new object based on k as the nearest neighbor. k-Nearest Neighbor (k-NN) algorithm has the strength as the effective and simple algorithm and it has been used by many problem classifications. However, it has weakness if it is used for the big dataset. It can happen because it needs higher computation time. In this research, Bootstrapping Sample method is proposed to increase the optimalization of computation accuracy and time on Boostraping Sample method. Based on this research, by using the integration of k-Nearest Neighbor (k-NN) algorithm with Bootstrapping Sample method on IPD dataset on Jepara in 2014, apparently it can increase the accuracy until 5.41 % (91.89%-97.30%) than using standard k-NN algorithm. The last, from the result of this research it can be inferred that by using the integration of K-Nearest Neighbor (k-NN) algorithm with Boostraping Sample method shows the better accuracy than using standard k-NN algorithm