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All Journal Jurnal Sains dan Teknologi
Juni Lapita Hasugian
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Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Physics Transportation

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Perancangan Data Warehouse Untuk Penentuan Rencana Strategis Penjualan Motor Hans Rafael Gabriel Turnip; Juni Lapita Hasugian; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Motorcycle sales generate large volumes of transactional data that require systematic management and analysis to support business decision making. This study aims to design and implement a motorcycle sales data warehouse using the Kimball Nine Steps methodology and to perform data analysis and visualization using RapidMiner. The dataset consists of motorcycle sales data including production year, price, model, type, and technical specifications. The results show that the implementation of a star schema successfully integrates data in a structured manner and supports multidimensional analysis. Three-dimensional scatter plot visualizations reveal sales patterns concentrated on mid-aged motorcycles within the medium price range, as well as the dominance of specific models. This study concludes that the integration of data warehousing and data visualization effectively improves information quality and supports data-driven decision making.
Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Menentukan Stok Smartphone Berdasarkan Pola Harga Penjualanan Juni Lapita Hasugian; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Intensifying competition in the smartphone retail sector encourages businesses to utilize sales data more effectively to support strategic decision-making, especially in inventory planning. In many cases,sales records are primarily used for routine administrative purposes and are not thoroughly analayzed to uncover sales trends that can guide stock prioritization.This research focuses on the application of data mining techniques to determine smartphone stock priorities by analyzing sales patterns. The dataset used in this study consists of smartphone sales records obtained from a retail store, incorporating attributes such as product pricing and sales volume. The research process includes data preprocessing stages, namely data cleaning and normalization, followed by the implementation of the K-Means clustering algorithm. Through the clustering process, smartphone products are categorized into several groups that reflect high, moderate, and low sales performance. The findings indicate that the K-Means-based data mining approach is effective in identifying sales patterns and classifying products according to their sales levels. The resulting clusters serve as a valuable reference for establishing stock priorities, improving inventory management efficiency, and supporting strategic decision-making in smartphone retail operations. Consequently, the application of data mining techniques offers an effective approach to enhancing inventory control in smartphone retail businesses.
Co-Authors Andri Wijaya Hans Rafael Gabriel Turnip