PREDIKSI DALAM PENGIRIMAN STOK PRODUK PADA PT.PUPUK SRIWIDJAJA MENGGUNAKAN METODE REGRESI LINIER

DEWI, NARWASTU KARTIKA and Heroza, Rahmat Izwan and Bardadi, Ali (2020) PREDIKSI DALAM PENGIRIMAN STOK PRODUK PADA PT.PUPUK SRIWIDJAJA MENGGUNAKAN METODE REGRESI LINIER. Undergraduate thesis, Sriwijaya University.

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Abstract

Agriculture in Indonesia, which is an agricultural country, is used as a major sector in the national economy. Fertilizer itself is a material used to provide nutrients for plants. Production results in the right time and quantity is something that farmers expect. Predictive data mining is needed as a solution to the needs required by PT. Pupuk Sriwidjaja Palembang, to make it easier to determine the amount of stock that will be sent to each region. Linear regression is a method that is quite popular as one of the methods used in predictive time-series have a better level of accuracy and precision than other methods. By using the linear regression method, a correlation will be generated regarding the factors that affect the change in the number of product shipments stock. The data mining process is carried out using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method then be implemented into a web-based system using the PHP programming language.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	prediction, Data Mining, CRISP-DM, linier Regression, product delivery.
Subjects:	T Technology > T Technology (General) > T57-57.97 Applied mathematics. Quantitative methods > T57.5 Data processing Cf. HF5548.125+ Business data processing Operations research. Systems analysis T Technology > T Technology (General) > T58.4 Managerial control systems Information technology. Information systems (General) T Technology > T Technology (General) > T58.6-58.62 Management information systems > T58.62 Decision support systems Cf. HD30.213 Industrial management T Technology > T Technology (General) > T58.7-58.8 Production capacity. Manufacturing capacity > T58.8 Productivity. Efficiency Standardization Cf. TA368 Standards (Collections, indexes, etc.)
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Users 9784 not found.
Date Deposited:	12 Jan 2021 07:56
Last Modified:	12 Jan 2021 07:56
URI:	http://repository.unsri.ac.id/id/eprint/39802

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