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All Journal Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Dhovan Damara Santoso
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

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Jaringan Syaraf Tiruan Memprediksi Jumlah Kebutuhan Semen pada Toko Bangunan Bintang Makmur Menggunakan Metode Backpropagation Dhovan Damara Santoso; Relita Buaton; Mili Alfhi Syari
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 5 (2024): September : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i5.559

Abstract

Every company is required to plan the need for goods as effectively as possible in order to maximize profits. Bintang Makmur Building Shop is a building shop that provides building materials, especially cement. Cement is one of the basic materials for buildings. The need for cement has recently continued to increase due to the large number of developments, both housing projects and road construction. In addition to the increasing demand for cement, cement prices also experienced price volatility which tended to fluctuate. This is done so that there is no stockpiling or even a shortage of cement. With prices that tend to go up and down if there is too much stock, it will cause losses if there is a price decrease. Vice versa if there is a shortage of cement stock, it can cause disappointment to customers. To deal with the above, it is necessary to build a prediction system that can predict cement needs in prosperous shops. The system that will be built uses an Artificial Neural Network (Artificial Neural Network) which is part of the science of artificial intelligence which has been widely used to solve various kinds of problems related to prediction or forecasting by utilizing the Backpropagation Method. The system is designed with the MATLAB programming application. From the results of the research that has been carried out, it was found that the total demand for Andalas cement for January of the following year is 0.2532 or 2532, thus the predicted total demand for Andalas cement is 2532 sacks.
Co-Authors Buaton, Relita Mili Alfhi Syari