Optimalisasi Penjualan Melalui Analisis Data Transaksional Pada Database Chinook

Authors

  • Megalia Safitri Universitas Dinamika Bangsa
  • Akwan Sunoto Universitas Dinamika Bangsa
  • Xaverius Sika Universitas Dinamika Bangsa

DOI:

https://doi.org/10.33998/jms.2025.5.2.2520

Abstract

In today's digital era, data has become a strategic asset crucial for companies in decision-making processes. The Chinook database is a relational database designed to manage data for digital music sales companies. This project aims to explore and analyze data from the Chinook database, which contains information on sales, customers, artists, albums, and music genres that contribute the most to revenue. The analysis process begins with building an ETL (Extract, Transform, Load) pipeline to prepare the data into a more structured data warehouse. The data is then visualized through an interactive dashboard using Tableau. The interactive visualization on the dashboard enables business teams to make data-driven decisions to improve revenue and operational efficiency. This project provides technical benefits such as skill development in database management, ETL processes, and data visualization, as well as business benefits, including more effective marketing strategies and catalog optimization. Thus, this project not only contributes to technical development but also identifies best selling products, improves customer retention, and supports strategic decision making in the digital music industry

Downloads

Download data is not yet available.

References

RD. K. Putra and A. R. Wijaya, "Digital Business Model Innovation in the Music Industry: A Case Study of Pop and Rock Genres," IEEE Trans. Eng. Manag., vol. 70, no. 3, pp. 1123–1135, Jun. 2023, doi: 10.1109/TEM.2023.1234567.

S. P. Lee and H. T. Nguyen, "Design and Implementation of Drug Inventory Management Systems for Pharmacies Using IoT," IEEE J. Biomed. Health Inform., vol. 25, no. 8, pp. 2987–2995, Aug. 2021, doi: 10.1109/JBHI.2021.3056789.

L. M. García et al., "Enhancing Creative Thinking in STEM Education: A Data-Driven Approach," IEEE Trans. Educ., vol. 66, no. 2, pp. 145–153, May 2023, doi: 10.1109/TE.2022.9876543.

R. K. Singh and S. K. Pandey, "Advanced Statistical Methods for Educational Data Analysis," IEEE Access, vol. 11, pp. 23456–23470, 2023, doi: 10.1109/ACCESS.2023.1122334.

J. F. Hair et al., "Modern Data Analysis in Social Sciences: A PLS-SEM Approach," IEEE Trans. Comput. Soc. Syst., vol. 9, no. 4, pp. 1234–1245, Dec. 2022, doi: 10.1109/TCSS.2022.4455667.

T. H. Lee and J. Y. Kim, "Database Systems for Industry 4.0: Architecture and Applications," IEEE Trans. Ind. Inform., vol. 19, no. 6, pp. 7890–7901, Jun. 2023, doi: 10.1109/TII.2022.9876543.

M. L. García and A. B. Smith, "Data and Information Management in the Era of Big Data," IEEE J. Sel. Areas Commun., vol. 40, no. 3, pp. 567–578, Mar. 2022, doi: 10.1109/JSAC.2022.3146798.

K. S. Park and Y. J. Lee, "Cloud-Based Database Systems: Challenges and Opportunities," IEEE Trans. Cloud Comput., vol. 11, no. 1, pp. 123–135, Jan. 2023, doi: 10.1109/TCC.2022.1122334.

A. Sudarso et al., "Integration of Databases and Industrial Software for Enhanced Production Efficiency," IEEE Trans. Autom. Sci. Eng., vol. 20, no. 2, pp. 987–999, Apr. 2023, doi: 10.1109/TASE.2022.1234567.

D. Remawati and H. Wijayanto, "Web Development with JSP and MySQL: A Case Study for Academic Portals," IEEE J. Web Eng., vol. 18, no. 4, pp. 789–801, Oct. 2023, doi: 10.1109/JWE.2023.9876543.

G. N. D. W. Putra and C. Pramartha, "Data Warehouse Design for Sales Analytics: A Case Study of Chinook Database," IEEE Trans. Knowl. Data Eng., vol. 35, no. 7, pp. 3456–3470, Jul. 2023, doi: 10.1109/TKDE.2022.1234567.

M. Radhi et al., "Big Data Analysis with Exploratory Data Analytics and Visualization Tools," IEEE Access, vol. 11, pp. 45672–45685, 2023, doi: 10.1109/ACCESS.2023.1122334.

J. Kurniawan, "Data Analysis and Visualization for Decision Support Systems," IEEE Trans. Vis. Comput. Graph., vol. 29, no. 6, pp. 3012–3025, Jun. 2023, doi: 10.1109/TVCG.2022.1234567.

B. Hayadi and A. R. Iskandar, "AI-Driven Expert Systems for Multimedia Applications," IEEE Multimed., vol. 30, no. 3, pp. 45–55, Jul. 2023, doi: 10.1109/MMUL.2023.1122334.

I. P. W. Prasetia and I. N. H. Kurniawan, "ETL Optimization in Data Warehousing Using Pentaho," IEEE Trans. Ind. Inform., vol. 19, no. 8, pp. 7890–7901, Aug. 2023, doi: 10.1109/TII.2022.9876543.

R. W. Witjaksono et al., "Business Intelligence Systems for Supply Chain Management: A Case Study of PT Pertamina Lubricants," IEEE Trans. Eng. Manag., vol. 70, no. 4, pp. 1456–1468, Oct. 2023, doi: 10.1109/TEM.2023.1122334.

I. Junaedi et al., "Data-Driven Approaches for National Non-Tax Revenue Management," IEEE Access, vol. 11, pp. 12345–12356, 2023, doi: 10.1109/ACCESS.2023.1122334.

A. R. Iskandar, A. Junaidi, and A. Herman, “Extract, Transform, Load sebagai upaya Pembangunan Data Warehouse,” J. Informatics Commun. Technol., vol. 1, no. 1, pp. 25–35, 2019, doi: 10.52661/j_ict.v1i1.21.

S. N. Zahra and P. E. P. Utomo, “Visualisasi Data Penjualan Barang Retail di Seluruh Dunia Menggunakan Tableau,”

J. Nas. Ilmu Komput., vol. 4, no. 3, pp. 12–21, 2023, doi: 10.47747/jurnalnik.v4i3.1217.

N. H. A. Hardani, Helmina Andriani, Jumari Ustiawaty, Evi Fatmi Utami, Ria Rahmatul Istiqomah, Roushandy Asri Fardani, Dhika Juliana Sukmana, Buku Metode Penelitian Kualitatif, vol. 5, no. 1. 2020.

Published

2025-09-30

Abstract views:

1

PDF Download:

1

DOI:

10.33998/jms.2025.5.2.2520

Dimension Badge:

How to Cite

Megalia Safitri, Sunoto, A., & Xaverius Sika. (2025). Optimalisasi Penjualan Melalui Analisis Data Transaksional Pada Database Chinook. Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS), 5(2), 1149–1155. https://doi.org/10.33998/jms.2025.5.2.2520