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All Journal Journal of Information Systems and Business Technology
Aldy Bifal Pratama
Universitas Pamulang
Computer Science & IT Library & Information Science

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Analisis dan Perancangan Sistem Informasi Manajemen BUMDes Berbasis Website Menggunakan Standar ISO/IEC 25010 Aldy Bifal Pratama; Muhamad Andri Rian Riyadi; Chairul Anwar
Journal of Information Systems and Business Technology Vol 2 No 3 (2026): Journal of Information Systems and Business Technology
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

The village economy is strategically driven by village-owned enterprises (BUMDes), but accountability and transparency are frequently hampered by manual data administration. The analysis and construction of a web-based BUMDes Management Information System are the goals of this project. Digitising the company's inventory, financial reporting, and administrative procedures is the aim. Because it enables BUMDes developers and managers to work closely together to identify user demands by gradually iterating the interface and functionality design, the prototype system development method was used for this study. By doing this, the likelihood of system incompatibility during the last step of implementation is decreased.The international standard ISO/IEC 25010 is used to guarantee the quality of the software produced. The primary subjects of testing are functional suitability, performance efficiency, ease of use, and reliability. A systematic and scalable information system model that may enhance BUMDes operations' efficiency and support data-driven decision-making is anticipated as a result of this design.
Segmentasi Pelanggan Menggunakan Algoritma K-Means untuk Customer Intelligence dan Strategi Pemasaran pada Perusahaan Retail Online (Studi Kasus: Dataset Online Retail II) Muhammad Naufal Skha Yusfa; Ahyat Musyawwa; Aldy Bifal Pratama
Journal of Information Systems and Business Technology Vol 2 No 3 (2026): Journal of Information Systems and Business Technology
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

Customer intelligence is crucial for online retail companies to design targeted marketing strategies and increase customer retention. This study aims to segment customers based on purchasing behavior using RFM analysis and K-Means clustering. The Online Retail II dataset containing more than 525,000 transactions from December 2010 to December 2011 was used. After data cleaning, outlier handling, and RFM feature engineering, the Elbow Method and Silhouette Score determined the optimal number of clusters (K=3). The results produced three distinct customer segments: Lost/At Risk (25.17%), Regular Customers (74.32%), and High-Value Loyal (0.51%). Actionable business recommendations were formulated for each segment. This segmentation provides deeper customer intelligence and supports more effective marketing strategies for online retail businesses. 
Co-Authors Ahyat Musyawwa Chairul Anwar Muhamad Andri Rian Riyadi Muhammad Naufal Skha Yusfa