Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal Of Informatics And Busisnes Jurnal Sains dan Teknologi
Nova Magdalena Br Hombing
Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Katolik Musi Charitas Palembang
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Physics Transportation

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

Found 2 Documents
Search

 1 
Perancangan Data Warehouse Untuk Mendukung Keputusan Strategi Pemasaran Dalam Penjualan Nova Magdalena Br Hombing; Welmi Simanjuntak; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 3 No. 3 (2025): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i3.3855

Abstract

Operational sales data is often fragmented, impeding management from developing data driven marketing strategies. This research aims to conceptually design a data warehouse to support sales marketing strategy decision making. The method utilizes a descriptive-conceptual approach employing the Kimball’s Nine-Step methodology on the Superstore Sales Data (2025) dataset from Kaggle. The resulting design is a Star Schema, which integrates historical data (customer, product, region, and time). Via the ETL (Extract, Transform, Load). The derived multidimensional analysis yields critical insights: Furniture products are the primary profit drivers, the Home Office segment demonstrates superior profitability, and the Q4 seasonal pattern (October-December) is the consistent sales speak. This data warehouse model proves effective in providing structured, actionable insights for marketing profit optimization.
Analisis Segmentasi Pelanggan Menggunakan K-Means Untuk Personalisasi Penawaran Produk Dalam Konteks E-Commerce Welmi Simanjuntak; Nova Magdalena Br Hombing; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid growth of e-commerce requires companies to gain deeper insights into customer behavior in order to enhance marketing effectiveness. This study aims to segment e-commerce customers using the Recency, Frequency, and Monetary (RFM) model combined with the K-Means algorithm. The research utilizes an Online Retail Dataset, with stages including preprocessing of 10,001 initial records into 2,311 valid data points, RFM calculation, and Min-Max normalization. The results indicate that the K-Means algorithm successfully groups customers into five clusters with distinct transaction behavior characteristics. Each cluster represents different levels of customer activity, ranging from high-value customers (Cluster 3) to at-risk customers (Clusters 0, 2, and 4). These segments serve as a basis for designing personalized product offerings and improving customer retention strategies in the e-commerce context.
Co-Authors Andri Wijaya Welmi Simanjuntak Welmi Simanjuntak