Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Bulletin of Computer Science Research Journal of Informatics Management and Information Technology
Maesaroh, Sri Wulandari
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pemanfaatan Algoritma K-Means Clustering Pada Sistem Rental Mobil Maesaroh, Sri Wulandari; Diansyah, T M; Liza, Risko; Lubis, Yessi Fitri Annisah
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.494

Abstract

This research utilizes the K-Means clustering algorithm to analyze car rental data from PT. Station Armada Indonesia, aiming to simplify customer car selection and improve the company's market responsiveness. The study addresses the problem of customer confusion stemming from the wide variety of car types offered by the company. By employing K-Means clustering on August 2023 rental data, the research groups cars based on rental price and mileage. The dataset, initially encompassing four car categories (Minibus MVP, SUV, City Car, and Van/Bus), was further detailed to include individual car models. Three parameters—rental duration, rental price, and mileage—were used for clustering. The K-Means algorithm, chosen for its ease of implementation and speed, was applied iteratively using Euclidean distance to assign data points to the nearest centroid. The study initially defined two clusters. Manual calculations, detailed in the paper, demonstrate the clustering process. These manual results were then compared against results obtained using RapidMiner Studio version 10.1, showcasing the software's efficiency in handling the K-Means process. The RapidMiner output included Data, Statistics, and Annotations views, providing a comprehensive analysis of the clusters. The final clustering, achieved after three iterations, revealed two distinct clusters: one representing less popular car types (Cluster 0), and the other representing the most popular car types (Cluster 1). Cluster 0 contained six car types with average customer mileage ranging from 673 km to 2050 km, while Cluster 1 included 24 car types with average mileage between 270 km and 3388 km. The findings enable PT. Station Armada Indonesia to optimize fleet management and marketing strategies by focusing on the most in-demand car types. The study concludes that K-Means clustering, implemented via RapidMiner, offers a valuable tool for enhancing customer understanding of car selection and improving the company's overall efficiency.
Pemanfaatan Algoritma K-Means Clustering Pada Sistem Rental Mobil Maesaroh, Sri Wulandari; Diansyah, T.M; Liza, Risko; Lubis, Yessi Fitri Annisa
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.391

Abstract

PT. Station Armada Indonesia is one of the companies engaged in the car rental service sector. With the many types of car choices offered, it is not uncommon for many customers to feel confused in choosing what type of car suits their needs. This problem is often experienced by customers who are confused by the many choices of car types available. In this study, the k-means algorithm was used to group cars based on several attributes. The k-means algorithm can be used to group car type data to help provide recommendations for choosing a car type. The purpose of this study is to make it easier for customers to choose the type of car that is most in demand and as material for PT. Station Armada Indonesia to respond better to market changes and achieve better results. Grouping car rental fleets based on rental prices and mileage by utilizing the k-means algorithm can help PT. Station Armada Indonesia group car types. From the grouping results, two cluster groups were obtained with the character of the first cluster being less in demand by customers and the second cluster group being the most in demand by customers. So that the company can easily prepare the type of fleet that is most in demand. In the application of data mining methods using k-means is very helpful and makes it easier for PT. Station Armada Indonesia to develop more effective marketing and offering strategies. By grouping car types with the implementation of k-means can facilitate customer knowledge in choosing car types based on customer needs.
Co-Authors Diansyah, T M Diansyah, T.M Liza, Risko Lubis, Yessi Fitri Annisa Yessi Fitri Annisah Lubis