PERANCANGAN DATA MART PEMBELIAN BAHAN MAKANAN PADA RESTORAN DISTRICT 9 MENGGUNAKAN METODE NINE-STEP KIMBALL
DOI:
https://doi.org/10.31539/h2jyvz90Abstract
Perkembangan teknologi saat ini telah mengubah kehidupan di berbagai sektor industri. Dengan itu, dapat mendorong suatu organisasi untuk memanfaatkan datanya sebagai dasar pengambilan keputusan. Ketika suatu organisasi memiliki data, tidak hanya dijadikan sebagai catatan pasif yang tidak bernilai. Data sebagai sumber informasi yang bernilai dapat menjaga efisiensi organisasi. Salah satu yang terkena dampak nyata adalah industri kuliner Kesalahan rekap, dan masalah duplikasi data sering menjadi masalah dalam manajemen operasional restoran. Penelitian ini bertujuan untuk merancang data mart pembelian bahan makanan di restoran District 9 menggunakan metode Nine-Step Kimball. Tahapan penelitian meliputi kebutuhan informasi, model data dengan star schema, proses ETL (Extract, Transform, Load). Empat dimensi utama yaitu waktu, bahan, pemasok, dan kategori, yang dihubungkan dengan tabel fakta pembelian.
Kata Kunci: Data Mart, Nine-Step Kimball, ETL, Pembelian Bahan Makanan.
References
Amin, M. M., Sutrisman, A., & Dwitayanti, Y. (2021). Development of Star-Schema Model for Lecturer Performance in Research Activities.
Anshari, S. F., & Retno, S. (2023). Penerapan Metode Nine-Step Kimball Dalam Pengolahan Data History Menggunakan Data Warehouse dan Business Intelligence. Jurnal Ilmu Komputer, 16(1), 69. https://doi.org/10.24843/JIK.2023.v16.i01.p07
Beng, J. T., Tiatri, S., Zheng, M., Nurkholiza, R., Dinatha, V., & Salsabila, T. M. (2025). Development of a Training Model on the Use of Laser Engraving Technology for Vocational High School Female Students in Semi-Urban Areas: Gender Equality in Education. TEM Journal, 14(2), 1860–1866. https://doi.org/10.18421/TEM142-82
Carlos, P. V., & Patricia, C. O. (2025). Kimball data warehouse for the sales analysis process in a manufacturing business in Perú. Indonesian Journal of Electrical Engineering and Computer Science, 37(2), 1093–1101. https://doi.org/10.11591/ijeecs.v37.i2.pp1093-1101
International Journal of Advanced Computer Science and Applications, 12(9). https://doi.org/10.14569/IJACSA.2021.0120909
Castillo-Cordero, L., Contreras-Chihuán, M., & Meneses-Claudio, B. (2024). Datamart for the analysis of information in the sales process of the company WC HVAC Engineering. Data and Metadata, 3, 184. https://doi.org/10.56294/dm2024184
Cekuls, A. (2023). Business intelligence factors for decision making. Journal of Intelligence Studies in Business, 12(2), 4–5. https://doi.org/10.37380/jisib.v12i2.950
Ciccullo, F., Fabbri, M., Abdelkafi, N., & Pero, M. (2022). Exploring the potential of business models for sustainability and big data for food waste reduction. Journal of Cleaner Production, 340, 130673. https://doi.org/10.1016/j.jclepro.2022.130673
Helminski, D., Kurlander, J. E., Renji, A. D., Sussman, J. B., Pfeiffer, P. N., Conte, M. L., Gadabu, O. J., Kokaly, A. N., Goldberg, R., Ranusch, A., Damschroder, L. J., & Landis-Lewis, Z. (2022). Dashboards in Health Care Settings: Protocol for a Scoping Review. In JMIR Research Protocols (Vol. 11, Issue 3). JMIR Publications Inc. https://doi.org/10.2196/34894
Hurbean, L., Militaru, F., Muntean, M., & Danaiata, D. (2023). The Impact of Business Intelligence and Analytics Adoption on Decision Making Effectiveness and Managerial Work Performance. Scientific Annals of Economics and Business, 70(SI), 43–54. https://doi.org/10.47743/saeb-2023-0012
Inmon, W. H. (2002). The data warehouse and design. In Building the data warehouse (3rd ed., pp. 81–145). Wiley.
Inmon, W. H. (2005). The data warehouse and design. In Building the data warehouse (4rd ed., pp. 159–191). Wiley.
Jap, T., & Tiatri, S. (2024). Cross-disciplinary curricula in Bachelor of Information Systems education: a case study in Indonesia. In Teaching Information Systems (pp. 68–86). Edward Elgar Publishing. https://doi.org/10.4337/9781802205794.00010
Kayikci, Y., Demir, S., Mangla, S. K., Subramanian, N., & Koc, B. (2022). Data-driven optimal dynamic pricing strategy for reducing perishable food waste at retailers. Journal of Cleaner Production, 344, 131068. https://doi.org/10.1016/j.jclepro.2022.131068
Leovin, A., Beng, J. T., & Dewayani, E. (2020, December). Business to business e-commerce sales system using web-based quotation: A case study on company x. In IOP Conference Series: Materials Science and Engineering (Vol. 1007, No. 1, p. 012156). IOP Publishing.
Merseedi, K. J., Yazdeen, A. A., Abdulrazzaq, M. B., Mahmood, M. R., Merceedi, K. J., Ibrahim, A. K., & Abdulrazzaq, M. B. (2022). Analyses the Performance of Data Warehouse Architecture Types Journal of Soft Computing and Data Mining Analyses the Performance of Data Warehouse Architecture Types. JOURNAL OF SOFT COMPUTING AND DATA MINING, 3(1), 45–57. https://doi.org/10.30880/jscdm
Nugroho, G., Tedjakusuma, F., Lo, D., Romulo, A., Pamungkas, D. H., & Kinardi, S. A. (2023). Review of The Application of Digital Transformation in Food Industry. Journal of Current Science and Technology, 13(3), 774–790. https://doi.org/10.59796/jcst.V13N3.2023.1285
Panca, A., & Trisnawarman, D. (2024). PERANCANGAN DASHBOARD PENJUALAN PRODUK BIJI KOPI “AGROASTERY” MENGGUNAKAN METODE WATERFALL. In Computatio: Journal of Computer Science and Information Systems (Vol. 8, Issue 2).
Rahayu, V. (2021). Analisis Algoritma Apriori dan FP-Growth Dalam Menemukan Pola Frequent Item Data Association Rule Pada Supermarket. EXPLORE, 11(2), 20. https://doi.org/10.35200/explore.v11i2.436
Ralph Kimball, & Margy Ross. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling Third Edition. Wiley.
Roy, D., Spiliotopoulou, E., & de Vries, J. (2022). Restaurant analytics: Emerging practice and research opportunities. Production and Operations Management, 31(10), 3687–3709. https://doi.org/10.1111/poms.13809
Salsabila, T. M., Caroline, A., Marcydiaz, A. H., Trisnawarman, D., & Beng, J. T. (2024). Perancangan Data Mart Untuk Manajemen Data Penjualan Pada Kedai Kopi X Di Jakarta. INTECOMS: Journal of Information Technology and Computer Science, 7(6), 1872–1880. https://doi.org/10.31539/intecoms.v7i6.12875
Sharma, S., Gahlawat, V. K., Rahul, K., Mor, R. S., & Malik, M. (2021). Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics. Logistics, 5(4), 66. https://doi.org/10.3390/logistics5040066
Soni, P., de Runz, C., Bouali, F., & Venturini, G. (2024). A survey on automatic dashboard recommendation systems. Visual Informatics, 8(1), 67–79. https://doi.org/10.1016/j.visinf.2024.01.002
Sugiarto, D., & Leslie Hendric Spits Warnars, H. (2020). PERANCANGAN DATA WAREHOUSE PENJUALAN (STUDI KASUS PT. SUBAFOOD PANGAN JAYA).
Tripathi, A., Bagga, T., Vishnoi, S. K., Senathirajah, A. R. bin S., & Haque, R. (2024). Business intelligence solution implementation challenges: A comparative analysis of service based start-ups, small & medium and large enterprises. Environment and Social Psychology, 9(9). https://doi.org/10.59429/esp.v9i9.2864
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