K-affinity propagation clustering algorithm for the classification of part-time workers using the internet

Abstract

There has been a significant increase in the number of part-time workers in the last 3 years. Data collected from sakernas BPS showed that the number of part-time workers was 125,443,748 in the second period of 2016. This number rapidly increased in 2017, 2018 and 2019 in the same period, by 128,062,746, 131,005,641, and 133,560,880 workers. Based on the increase in the last 3 years, East Java province has the highest number of part-time workers that use the internet. This research aims to determine the number of part-time workers that use the internet by using the k-affinity propagation (K-AP) clustering. This method is used to produce the optimal number of cluster points (exemplar) is the affinity propagation (AP). Three clusters were used to determine the sum of the smallest value ratio. The result showed that clusters 1, 2, and 3 have 3, 23, and 5 members in Bondowoso, Jombang, and Surabaya districts.

Penerapan Metode Clustering Self Organizing Maps (Som) Dan K-Affinity Propagation (K-Ap) Dalam Mengelompokkan Nilai Tukar Petani Di Indonesia 2022

“…Penelitian ini bertujuan untuk melihat gambaran umum NTP di Indonesia, yang kedua yakni menguraikan hasil clustering metode SOM dengan K-AP, dan terakhir untuk mengetahui hasil pengelompokan NTP terbaik antarat metode SOM dengan K-AP. Penelitian terkait metode SOM serta K-AP sudah diadakan oleh [11] dengan membandingkan metode clustering K-Medoids dengan SOM. Hasil cluster terbaik diperoleh dengan menggunakan metode SOM dengan nilai rasio standar deviasi yang lebih kecil daripada nilai rasio standar deviasi pada metode K-Medoids.…”

MSME Sales Clustering Based on Business Aid Distribution Priority Using K-Affinity Propagation

“…The absence of reference variables in clustering process makes it can be used to assign labels to data group whose class is not known beforehand [9]. The distance between objects is carrying out the grouping and the shape is only affected by the size of the distances [10]. This process is calculated using the Euclidean distance, as follows.…”

“…The three clustering methods produced significantly different standard deviation values, with the order from smallest to largest being k-affinity propagation, k-medoids, and k-means. It is concluded that the k-affinity propagation method proved to be the best with the smallest standard deviation [10].…”

“…In previous studies, there are also comparison of the k clustering method, namely between kmeans, k-medoids, and k-affinity propagation [10]. The standard deviation was used to determine the ideal number of clusters.…”

“…The method used for grouping each subdistrict in Sleman Regency is clustering using k-affinity propagation, this method is a modification of affinity propagation to optimize the k value to obtain optimal copies. This method is used because it has a low error rate, and can find clusters fairly quickly [10]. Cluster method identifies copies among all data points and then forms a cluster of data points around the copies.…”