Penerapan Data Mining Untuk Prediksi Jumlah Total Porduksi Bakpao Pada PT. Estetika Tata Tiara Menggunakan Algoritma Regresi Linier Berganda

Prasetya Wicaksana(1*), Magdalena A. Ineke Pakereng(2),

(1) FTI UKSW, Salatiga, Indonesia
(2) FTI UKSW, Salatiga, Indonesia
(*) Corresponding Author

Abstract


Production objective planning is the process of identifying the product to be produced, the required quantity, the completion deadline, and what sources will be needed. The research objective is to determine the total production of Bakpao PT. Estetika Tata Tiara uses multiple linear regression algorithm. At this stage, the data mining technique uses multiple linear regression algorithms. This research was conducted at PT. Aesthetics of Tata Tiara which is a bakpao production company. The results show that the regression equation obtained from the results of multiple linear regression analysis is for the prediction of Bakpao in April 2022 as follows: Y = 473.531 + 0.56 X1 + 0.043 X2. After the analysis, it can be concluded that the variables X1 and X1 affect the prediction of the amount of Bakpao production in 2022. The relationship between sales (X1) and stock (X2), and Bakpao production has a strong positive and unidirectional relationship.

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References


Berry, Michael J.A & Linoff, Gordon S. (2004). Data Mining Techniques For Marketing, Sales, Customer Relationship Management Second Edition. United States of America: Wiley Publishing, Inc.

Connolly, T & Begg, C. (2010). Database Systems: A Practical Approach to Design, Implementation, and Management, Fifth Edition. Pearson Education, Boston

David, O & Yong, S. (2011). Introduction to Business Data Mining. International Edition: Mc Graw Hill

Han. J, Kamber M. (2011). Data Mining: Concepts and Techniques, Waltham: Elsevier Inc.

Han. J, Kamber M., 2006, Data Mining: Concepts and Techniques, Second Edition. Morgan Kaufman. California

Jamner R. Lawendatu, John S. Kekenusa, Djoni Hatidja, (2014). Regresi Linier Berganda Untuk Menganalisis Pendapatan Petani Pala. JdC, Vol. 3, No. 1. Pp.66-72

Khafiizh, H, (2012). Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Mahasiswa Non Aktif. Seminar Nasional Teknologi Informasi & Komunikasi Terapan 2012 (Semantik 2012). Pp.241-249

Kutner, M.H., C.J. Nachtsheim., & J. Neter. 2004. Applied Linear Regression Models. 4th ed. New York: McGraw-Hill Companies, Inc

Syafitri, N. Sutardi & Tajidun, L.M, (2018). Aplikasi Forecasting Mengenai Angka Kelahiran Di Kota Kendari Menggunakan Metode Regresi Linear Berganda (Studi Kasus : Dinas Kesehatan Kota Kendari). semanTIK, Vol.4, No.1. Pp.31-38

Vercellis (2009), Data Warehousing, in Business Intelligence, onlinelibrary.wiley.com




DOI: http://dx.doi.org/10.30645/j-sakti.v7i1.598

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