Analisis Faktor Yang Mempengaruhi Penumpang Angkutan Umum Beralih Ke Transportasi Online Go-Jek Menggunakan Metode K-Means Clustering

Abstract

The objectives of this research are; 1) Can find the results of the analysis of switching factors, namely the age data, and 2) Can find the results of the analysis of switching factors, namely the data on the travel time of public transportation, 3) Can find the results of the analysis of switching factors, namely the data on the travel time of Go-jek 4) Can find the results of the analysis of switching factors, namely the tariff data, and 5) Can find the results of the analysis of switching factors, namely the data that is easy to obtain. This study was designed to determine the results of the analysis of factors that influence the shifting of public transportation to Go-jek online transportation using the K-means clustering algorithm. The data collection technique in this study was by means of a questionnaire through the Go-jek community in Indonesia and secondary data taken from the internet media. Based on the results of the analysis that has been carried out on the analysis of factors that influence public transport passengers to switch to Go-jek online transportation using the K-means clustering algorithm, it is hoped that further researchers will test with other clustering algorithms, and the rapidminer software used as research material can be developed further. become more other features.

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

“…For example, Khairul et al [11] applied this approach in detecting spinal disorders using X-ray images, while Lompoliuw and Purnomo [12] applied the K-Means algorithm in grouping Covid-19 patients based on the length of recovery. Sahputra et al [13] analyzed the factors that influence public transport passengers to switch to Go-Jek online transportation using the K-Means Clustering method. The application of case-based reasoning and k-means clustering in e-commerce product recommendations is also supported by various other studies such as Aldayel and Benhidour [14], Xiao et al [15], Kumar et al [16], Bandyopadhyay et al [17], Xian et al [18], Andra [19], Nainggolan and Purba [20], and Mulyawan et al [21].…”

“…On the other hand, K-Means Clustering is a data clustering method that groups data into groups based on feature similarities [11] [12]. In recommendations, K-Means can be used to group products or users with similar preferences [13].…”

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

“…For example, Khairul et al [11] applied this approach in detecting spinal disorders using X-ray images, while Lompoliuw and Purnomo [12] applied the K-Means algorithm in grouping Covid-19 patients based on the length of recovery. Sahputra et al [13] analyzed the factors that influence public transport passengers to switch to Go-Jek online transportation using the K-Means Clustering method. The application of case-based reasoning and k-means clustering in e-commerce product recommendations is also supported by various other studies such as Aldayel and Benhidour [14], Xiao et al [15], Kumar et al [16], Bandyopadhyay et al [17], Xian et al [18], Andra [19], Nainggolan and Purba [20], and Mulyawan et al [21].…”

“…On the other hand, K-Means Clustering is a data clustering method that groups data into groups based on feature similarities [11] [12]. In recommendations, K-Means can be used to group products or users with similar preferences [13].…”

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

“…For example, Khairul et al [11] applied this approach in detecting spinal disorders using X-ray images, while Lompoliuw and Purnomo [12] applied the K-Means algorithm in grouping Covid-19 patients based on the length of recovery. Sahputra et al [13] analyzed the factors that influence public transport passengers to switch to Go-Jek online transportation using the K-Means Clustering method. The application of case-based reasoning and k-means clustering in e-commerce product recommendations is also supported by various other studies such as Aldayel and Benhidour [14], Xiao et al [15], Kumar et al [16], Bandyopadhyay et al [17], Xian et al [18], Andra [19], Nainggolan and Purba [20], and Mulyawan et al [21].…”

“…On the other hand, K-Means Clustering is a data clustering method that groups data into groups based on feature similarities [11] [12]. In recommendations, K-Means can be used to group products or users with similar preferences [13].…”

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

“…For example, Khairul et al [11] applied this approach in detecting spinal disorders using X-ray images, while Lompoliuw and Purnomo [12] applied the K-Means algorithm in grouping Covid-19 patients based on the length of recovery. Sahputra et al [13] analyzed the factors that influence public transport passengers to switch to Go-Jek online transportation using the K-Means Clustering method. The application of case-based reasoning and k-means clustering in e-commerce product recommendations is also supported by various other studies such as Aldayel and Benhidour [14], Xiao et al [15], Kumar et al [16], Bandyopadhyay et al [17], Xian et al [18], Andra [19], Nainggolan and Purba [20], and Mulyawan et al [21].…”

“…On the other hand, K-Means Clustering is a data clustering method that groups data into groups based on feature similarities [11] [12]. In recommendations, K-Means can be used to group products or users with similar preferences [13].…”