Optimalisasi Penjualan Melalui Analisis Data Transaksional Pada Database Chinook
DOI:
https://doi.org/10.33998/jms.2025.5.2.2520Abstrak
Dalam era digital saat ini, data menjadi asset strategis yang sangat penting bagi perusahaan dalam mengambil keputusan. Database Chinook adalah database rasional yang dirancang untuk mengelola data perusahaan penjualan musik digital. Proyek ini bertujuan untuk mengeksplorasi dan menganalisis data dari database Chinook yang berisi informasi penjualan, pelanggan, artist, album maupun genre musik yang memberikan kontribusi terbesar terhadap penjualan. Proses analisis diawali dengan membangun pipeline ETL (Extract, Transform, Load) untuk mempersiapkan data kedalam data warehouse menjadi lebih terstruktur. Selanjutnya data tersebut akan divisualisasikan dalam bentuk dashboard interaktif menggunakan tableau.Visualisasi interaktif pada dashboard memungkinkan tim bisnis untuk mengambil keputusan berdasarkan data guna meningkatkan pendapatan dan efesiensi operasional. Proyek ini memberikan manfaat teknis seperti pengembangan keahlian dalam pengolahan database, proses ETL dan visualisasi data,pemanfaatan bisnis berupa strategi pemasaran yang lebih efektif dan optimalisasi katalog musik. Dengan demikian, proyek ini tidak hanya berkontribusi pada pengembangan teknis, tetapi juga mendukung pengambilan keputusan strategis di sektor industri musik digital.
Unduhan
Referensi
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.
