K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor

Abstract

Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of a healthy Indonesian card (KIS) required a method of optimal accuracy. The data used in this study is KIS data which amounts to 200 data records with 15 determinants of feasibility in 2017 taken at the Social Service of Pekalongan Regency. The data were processed using the K-Nearest Neighbor algorithm and the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm. This can be seen from the accuracy of determining the feasibility of K-Nearest Neighbor algorithm of 64%, while the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is 96%, so the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is the optimal algorithm in determining the feasibility of healthy Indonesian card recipients with an increase of 32% accuracy. This study shows that the accuracy of the results of determining feasibility using a combination of K-Nearest Neighbor-Naive Bayes Classifier algorithms is better than the K-Nearest Neighbor algorithm.

The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

“…After the classification results are known, then determine the accuracy used confusion matrix [15]. The Confussion Matrix is used to display the number of correct and false predictions made by the model compared to the actual classification in the test data [16], the use of confussion matrix allows better analysis of various types of errors [17].…”

Implementasi K-Nearest Neighbor pada Decission Support System Pemilihan Satuan Pengamanan Event Perguruan Tinggi

“…Target hasil dicari dengan mencari pada sekelompok data sample terhadap objek baru berdasarkan susunan atribut yang mempengaruhi. Rumus yang digunakan dalam metode K-Nearest Neighbor adalah (Safri, 2018):…”

Klasifikasi Tingkat Kemurnian Bahan Bakar Minyak Berdasarkan Cepat Rambat Gelombang Menggunakan Algoritma K-Nearest Neighbor

“…Dalam KNN perlu menetapkan nilai k yaitu jumlah tetangga terdekat , kemudian menghitung jarak ketetanggaan (similarity measure) masing-masing objek terhadap data uji yang diberikan [5], [11], [14]. Tahap selanjutnya adalah mengurutkan objek-objek tersebut kedalam kelompok yang mempunyai jarak terkecil sampai terbesar, kemudian mengumpulkan kategori klasifikasi [15]. Label suatu data baru dapat diprediksi dengan menggunakan kategori K-Nearest Neighbor yang paling mayoritas [4], [11], [15].…”

“…Tahap selanjutnya adalah mengurutkan objek-objek tersebut kedalam kelompok yang mempunyai jarak terkecil sampai terbesar, kemudian mengumpulkan kategori klasifikasi [15]. Label suatu data baru dapat diprediksi dengan menggunakan kategori K-Nearest Neighbor yang paling mayoritas [4], [11], [15].…”