Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation

Thoriq, Muhammad

doi:10.37034/jidt.v4i1.178

Cited by 11 publications

(13 citation statements)

References 9 publications

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“…Jaringan neural merupakan sebuah sistem yang terdiri dari beberapa layer yang masing-masing terdiri dari beberapa unit neuron yang terhubung satu sama lain. Saat melatih jaringan neural, kita memberikan data latih ke jaringan dan kemudian memperkirakan hasil yang diinginkan [17] [18]. Jaringan akan menghitung hasil yang dihasilkan dan kemudian mengukur seberapa jauh hasil tersebut dari target yang diinginkan.…”

Section: Pendahuluanunclassified

Penerapan Metode Neural Network Dengan Struktur Backpropagation Untuk Memprediksi Kebutuhan Stok Pada Toko Umkm Perlengkapan Bayi Babyqu

Mulyana¹,

YkhSanur²,

FikriYadi³

et al. 2023

jimik

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At this time Machine Learning especially Deep Learning is developing very quickly in any field, especially in the fields of business, transactions, stock predictions, sales, and stocks and the like. Machine Learning has become a mainstay for facilitating and helping work. At the BabyQu store, the decisions used to carry out stock inventory still use the manual method, and there are several problems that arise including the occurrence of excess stock which makes other storage areas used for excess stock, especially if there are products that have an expiration date, and if there is a shortage of stock, problems will occur that will make consumers who need products or goods go to other places and several other problems that will arise which will cause losses to BabyQu stores, it is necessary to create a system or software that aims to assist BabyQu stores in forecasting stock availability product to overcomesome of these problems. This research was made using the Artificial Neural Network (ANN) model design and method using Backpropagation as the algorithm because this algorithm can reduce the percentage of errors. The results we got in this study using the 3-3-1 model obtained an accuracy rate of 85.72%, using 550 iterations of epochs, and taking approximately 11.1 seconds.

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Section: Pendahuluanunclassified

Penerapan Metode Neural Network Dengan Struktur Backpropagation Untuk Memprediksi Kebutuhan Stok Pada Toko Umkm Perlengkapan Bayi Babyqu

Mulyana¹,

YkhSanur²,

FikriYadi³

et al. 2023

jimik

View full text Add to dashboard Cite

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“…Langkah-langkah yang dilakukan dalam pengolahan data adalah menentukan distribusi probabilitas, menentukan distribusi komulatif, interval angka acak (random), membangkitkan bilangan acak, dan membuat serangkaian simulasi [14], [15]. [16].…”

Section: Mengolah Data Dengan Metode Simulasi Monte Carlounclassified

Model Simulasi untuk Memperkirakan Tingkat Penjualan Garam Menggunakan Metode Monte Carlo

Thoriq¹,

Syaputra²,

Eirlangga³

2022

jidt

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The increasing need for salt in the West Sumatra area is inversely proportional to the raw material for making salt. So the stock of salt for consumption becomes less. This causes the purchasing power to be cut off for the needs of regional consumers. Based on these problems, a research was conducted by conducting simulations to predict the amount of salt sales in controlling stock. This study aims to predict sales in maintaining service to consumer demand. The method that can be used in making predictions is the Monte Carlo Method by processing Salt sales data in 2019, 2020, and 2021 at PT. Prosperous Grace. The results of the study are able to predict sales of salt in the form of kilograms (kg) in the future. The average accuracy rate in 2020 is 88% and in 2021 is 91%. So that this research can be a reference in decision making by PT. Kurnia Sejahtera to improve services.

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“…Prediksi permintaan garam pada PT. Kurnia Garam Sejahtera Kota Padang, dimana hasil momentum yang didapatkan adalah 3-9-1 dan hasil prediksi yang optimal adalah 0,98946 [18]. Prediksi produksi padi di Sumatera Barat diperoleh berupa rancangan optimal untuk melakukan prediksi menggunakan multilayer dengan tingkat akurasi mencapai 88,14% atau dengan tingkat error 11,86% [19].…”

Section: Pendahuluanunclassified

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Khairati

Putra²

2022

jsisfotek

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Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.

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Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation

Cited by 11 publications

References 9 publications

Penerapan Metode Neural Network Dengan Struktur Backpropagation Untuk Memprediksi Kebutuhan Stok Pada Toko Umkm Perlengkapan Bayi Babyqu

Penerapan Metode Neural Network Dengan Struktur Backpropagation Untuk Memprediksi Kebutuhan Stok Pada Toko Umkm Perlengkapan Bayi Babyqu

Model Simulasi untuk Memperkirakan Tingkat Penjualan Garam Menggunakan Metode Monte Carlo

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

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