Article Per Year (5 Year)
p-Index From 2021 - 2026
0.835
P-Index
This Author published in this journals
All Journal Journal Of Informatics And Busisnes Jurnal Sains dan Teknologi
Andri Wijaya
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 : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Perancangan Data Warehouse Menggunakan Metode Nine Step Pada PT. XYZ Angelus Galang Shevchenko; Wianti Maharani; 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.v3i3.3796

Abstract

PT. XYZ operates in the food distribution sector and handles a large number of inbound and outbound goods transactions each day. The high transaction volume creates challengess in managing inventory effectively, highlighting the need for an integrated data management solution. This research aims to develop a data warehouse for PT. XYZ by applying the Kimball Nine-Step methodology. Data were collected through interviews, field observations, and a review of relevant literature. The data warehouse design process includes identifying fact tables and dimension tables related to receiving and shipping activities, followed by the implementation of the Extract, Transform, Load (ETL) process using SQL Server Management Studio 19. The findings indicate that the proposed data warehouse is capable of integrating operational data and presenting inventory information through reports generated using Microsoft Excel. The system supports improved stock control and enables management to make faster and more accurate decisions.
Perancangan Data Warehouse Untuk Analisis Peminjaman Dan Pengembalian Buku Di Perpustakaan Bintang Jelita Nasrani Silaban; Indri Feni Asih Zalukhu; 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.v3i3.3837

Abstract

The current library environment is experiencing rapid data collection, particulary from the daily activity of borrowing and returning books. This vast amount of data could potentially be utilized for analysis, but in practice, library information systems are often used solely for operational purposes. Consequently, opportunities to extract valuable insights from this data are limited. Given the situation, this study attempts to design and implement a data warehouse focused on analyzing book borrowing and returning patterns, utilizing PostgreSQL as the primary platform. The research process involved several stages, starting with data collection, needs analysis, data warehouse model design using the star schema, and implementation into PostgreSQL. Afterward, an ETL (Extraction, Transformation, and Loading) process was performed to able to combine library transaction data into a more structured and ready for analysis. From this data, the system was able to generate various insights, such as patterns of books that were borrowed most, medim, and least.
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.
Penerapan Data Mining Untuk Klasterisasi Buku Di Perpustakaan Menggunakan Algoritma K-Means Indri Feni Asih Zalukhu; Bintang Jelita Nasrani Silaban; 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.v3i3.3904

Abstract

Libraries expand their book collections every year, making managing and organizing shelves increasingly challenging. This makes finding and grouping relevant books quite time – consuming, especially when the data is already quite large. Therfore, this study attemps to utilize data mining methods, specifically the K-Means algorithm, to help group books based on certain similarities, such as category and borrowing. Before the grouping process is carried out, the book data first goes through preprocessing and normalization stages to make data look neat and ready to be processed. Furthermore, the K-Means algorithm is used to generate several groups of books with similar characteristics. From the data processing results, K-Means has been proven to be able to form several fairly clear clusters, this sifnificantly assisting libraries in organizing books, providing reading recommendations, and improving the quality of service for students and lecturers. Overall, the implementations of the K-Means algorithm in this library can accelerate collection management work and support a more data – driven decision – making process.
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 Angelus Galang Shevchenko Bintang Jelita Nasrani Silaban Bintang Jelita Nasrani Silaban Indri Feni Asih Zalukhu Nova Magdalena Br Hombing Welmi Simanjuntak Welmi Simanjuntak Wianti Maharani