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All Journal Bulletin of Computer Science Research Journal of Computer Science Artificial Intelligence and Communications
Yessi Fitri Annisah Lubis
Universitas Harapan Medan, Medan
Computer Science & IT Earth & Planetary Sciences Engineering

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Pemanfaatan Algoritma K-Means Clustering Pada Sistem Rental Mobil Sri Wulandari Maesaroh; T M Diansyah; Risko Liza; Yessi Fitri Annisah Lubis
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.
Impact of Mobile Technology Use on Knowledge Management in the Education Sector Muhammad Eka; Yessi Fitri Annisah Lubis; Divi Handoko; Rismayanti Rismayanti; Supiyandi Supiyandi
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 1 (2025): May 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i1.21

Abstract

The integration of mobile technology into knowledge management (KM) practices has reshaped the landscape of information sharing and learning in the education sector. This study explores how mobile devices and applications contribute to the efficiency, accessibility, and effectiveness of KM processes among educators, students, and administrators. With the growing adoption of smartphones, tablets, and mobile learning platforms, educational institutions are experiencing a shift from traditional knowledge repositories to dynamic, real-time knowledge exchange environments. The research employs a mixed-method approach involving surveys and in-depth interviews with teachers, students, and IT staff across several secondary and higher education institutions. The findings reveal that mobile technology enhances knowledge acquisition and dissemination by enabling anytime-anywhere access to learning materials, collaborative tools, and institutional knowledge databases. However, challenges such as data security, digital literacy gaps, and resistance to change remain significant barriers to optimal utilization. Furthermore, the study highlights the role of institutional policies and support systems in facilitating effective mobile-based KM adoption. The results indicate that institutions with clear mobile technology strategies and investments in user training are more likely to achieve improved knowledge-sharing outcomes. This research provides practical insights into leveraging mobile technology to strengthen KM frameworks in education and emphasizes the need for continuous adaptation to technological advancements to sustain knowledge-based performance improvements.
The Use of Knowledge Management Systems to Improve Decision-Making in Local Government Rusmin Saragih; Yuyun Dwi Lestari; Yessi Fitri Annisah Lubis; Divi Handoko
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 1 (2025): May 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i1.22

Abstract

Effective and data-driven decision-making has become an urgent need for local governments in facing the challenges of public service complexity, socio-economic dynamics, and demands for transparency and accountability. One of the strategic approaches to support this process is through the implementation of a Knowledge Management System (KMS). This research aims to explore the role and impact of KMS implementation on the improvement of decision-making quality in regional government organizations. A qualitative approach is used in this study with a case study method on several regional government agencies in Indonesia that have implemented KMS, combined with an analysis of related academic literature. Research results show that KMS is capable of improving the efficiency of storage, distribution, and access to organizational knowledge, both tacit and explicit. KMS supports faster, more accurate, and participatory decision-making because strategic information can be obtained and used promptly by policymakers. The findings also indicate that the success of KMS implementation is greatly influenced by organizational culture, leadership support, and the capacity of human resources in managing and sharing knowledge. This study recommends the comprehensive integration of KMS into the government work system, with an emphasis on training aspects, digital infrastructure, and internal policies that support the knowledge-sharing process. The theoretical and practical implications of these findings are an important contribution to the development of knowledge-based governance at the regional level.
Co-Authors Divi Handoko Divi Handoko Muhammad Eka Risko Liza Rismayanti Rismayanti Rusmin Saragih, Rusmin Sri Wulandari Maesaroh Supiyandi Supiyandi T M Diansyah Yuyun Dwi Lestari