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p-Index From 2021 - 2026
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All Journal International Journal of Management Science and Information Technology (IJMSIT) Journal of Computer Science Advancements
Parini Parini
Universitas Royal
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Consumer Segmentation With K-Means at Lucky Shop Tanjungbalai Reza Ahmad Fauzi; Masitah Handayani; Parini Parini
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.7213

Abstract

Consumer segmentation is an important strategy for improving marketing effectiveness and inventory management in retail businesses. Lucky Shop Tanjungbalai faces challenges in understanding diverse customer purchasing patterns, making it difficult to develop targeted marketing strategies. This study aims to apply the K-Means Clustering method to classify consumers based on purchasing behavior patterns. The data used consisted of 15 customer transaction records collected from Lucky Shop Tanjungbalai, with attributes including purchase frequency, quantity of purchased products, and product categories. This research adopted a qualitative approach combined with data mining techniques using the CRISP-DM framework, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The system was developed using PHP and MySQL. The results indicate that K-Means Clustering successfully segmented customers into Loyal Customers and Occasional Customers based on their purchasing characteristics. These segmentation results provide practical benefits for Lucky Shop by enabling more targeted promotional programs, improving customer relationship strategies, optimizing inventory planning, and supporting data-driven business decision-making. Therefore, the implementation of K-Means Clustering can serve as an effective solution for customer segmentation in local retail businesses.
Implementation of the Apriori Algorithm for Product Recommendation Analysis at Asyifa Serba 35.000 Retail Store in Kisaran Ardiansyah Putra Tambunan; Adi Prijuna Lubis; Parini Parini
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.7289

Abstract

This study aims to implement the Apriori algorithm to analyze sales transaction data and generate product recommendations at Toko Asyifa Serba 35.000. The research addresses the problem of underutilized transaction data, where sales records are only used for administrative purposes without further analysis to support marketing strategies and decision-making. The significance of this study lies in its contribution to enhancing data-driven decision-making in retail businesses, particularly in improving product promotion strategies, inventory management, and customer satisfaction. The research adopts an applied quantitative approach with an experimental design. Data were collected through observations, interviews, and documentation of sales transactions, and analyzed using data mining techniques, specifically the Apriori algorithm, to identify frequent itemsets and association rules based on support and confidence values. The results indicate that the implementation of the Apriori algorithm successfully uncovers patterns of consumer purchasing behavior, revealing combinations of products frequently bought together. The generated recommendations provide practical benefits for retail management, including more effective product bundling strategies, optimized shelf arrangement, targeted promotional campaigns, and improved inventory planning. These improvements can contribute to increased sales opportunities and better customer shopping experiences. These findings enable the development of a recommendation system that provides accurate and relevant product suggestions. The study concludes that the application of Apriori-based recommendation systems improves sales effectiveness, optimizes product placement, and enhances customer satisfaction. It is recommended that retail businesses adopt data mining techniques to maximize the value of transaction data and further develop integrated recommendation systems for better decision support.
Implementation of a Cloud-Based E-Learning System for Integrated Learning in Higher Education Parini Parini; Sri Nur Rahmi; Fransiskus Ghunu Bili; Ahmya Ayaka
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1625

Abstract

The integration of technology in higher education has gained significant momentum, with cloud-based e-learning systems emerging as a transformative approach to support integrated and flexible learning environments. Traditional learning systems often face limitations in scalability, accessibility, and resource-sharing, prompting the need for innovative solutions. Cloud-based e-learning systems offer a centralized platform that enhances collaboration, resource management, and learning continuity. This research explores the implementation of a cloud-based e-learning system in higher education institutions, focusing on its impact on learning outcomes and system efficiency. The study employs a mixed-method approach, combining quantitative surveys and qualitative interviews. Data were collected from 300 students and 50 faculty members across three universities that recently adopted cloud-based e-learning platforms. The research assessed system usability, learner engagement, and academic performance, alongside implementation challenges and benefits. The findings reveal that cloud-based e-learning systems significantly improve accessibility, resource-sharing, and collaboration among students and educators. Survey results indicated a 40% increase in learner engagement and a 35% improvement in resource utilization. Faculty interviews highlighted reduced administrative burdens and enhanced flexibility in course delivery. However, challenges such as data security concerns and the need for technical support were noted. The study concludes that cloud-based e-learning systems are a valuable tool for modernizing higher education. Addressing implementation challenges and ensuring continuous technical support are critical for maximizing their potential. Future research should explore long-term impacts and integration with emerging technologies to further enhance learning experiences.
Co-Authors Adi Prijuna Lubis Ahmya Ayaka Ardiansyah Putra Tambunan Fransiskus Ghunu Bili Masitah Handayani Reza Ahmad Fauzi Sri Nur Rahmi