scholarly journals PREDIKSI PENGADAAN DAN PENGELOLAAN INVENTORI JARINGAN SYARAF TIRUAN ALGORITMA BACKPROPAGATION PADA PERUM BULOG

KOMPUTEK ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Ethan Mahesa Murty
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Food Availability ◽  
Public Company ◽  
The Future ◽  
Rice Consumption ◽  
Food Stability ◽  
Artificial Neural ◽  
Prediction Techniques

Perum Bulog is a state-owned public company in food logistics field. Perum Bulog has a duty to stabilize food availability in Indonesia. The most consumed food by Indonesians is rice. It is estimated that the total national rice consumption reaches 30.25 million tons of rice. In this way, Perum Bulog must be able to meet their rice stock to maintain national food stability. However, in fact, in 2019 as many as 20 thousand tons of domestic rice had gone bad and caused the company to lose up to 167 billion. Thus, it is important to make predictions to determine the amount of rice stock in the future. One of the prediction techniques that can be used is prediction using Artificial Neural Networks. This study aims to determine the future rice stock of Perum Bulog using Artificial Neural Networks. Perum Bulog merupakan perusahaan umum milik negara yang bergerak di  bidang logistik pangan.  Perum Bulog memiliki tugas untuk menstabilkan ketersediaan pangan di Indonesia. makanan pokok yang paling sering dikonsumsi masyarakat Indonesia adalah beras. Diperkirakan jumlah konsumsi beras nasional mencapai 30,25 juta ton beras. Dengan begitu Perum Bulog harus dapat memenuhi stok beras mereka untuk menjaga kestabilan pangan nasional. Namun, nyatanya dilapan pada tahun 2019  sebanyak 20 ribu ton beras dalam negeri mengalami pembusukan dan membuat perusahaan rugi hingga 167 miliar. Dengan begitu pentingnya melakukan prediksi  untuk mengetahui jumlah stok beras dimasa depan. Salah satu teknik prediksi yang dapa digunakan adalah prediksi menggunakan jaringan syaraf tiruan. Penelitian ini bertujuan untuk mengetahui stok beras masa depan Perum Bulog menggunakan jaringan syaraf tiruan.

Applications of Artificial Neural Networks in Economics and Finance

2018 ◽  
pp. 6631-6641
Author(s):  
Iva Mihaylova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Trends ◽  
Dependence Analysis ◽  
Future Trends ◽  
Powerful Technique ◽  
Analytic Tool ◽  
The Future ◽  
Multivariate Dependence ◽  
Artificial Neural

Artificial neural Networks (ANNs) are a powerful technique for multivariate dependence analysis. Originally inspired by neuroscience, ANNs are becoming an increasingly attractive analytic tool for applications in the area of economics and finance due to the flexible solutions they offer. The purpose of this article is to present such important applications with an emphasis on recent research trends. The contributions are grouped as follows: ANNs (1) for prediction, (2) for classification and (3) for modelling. The chapter concludes with the future trends in the ANNs research in economics and finance.

Applications of Artificial Neural Networks in Economics and Finance

2019 ◽  
pp. 997-1008 ◽  
Author(s):  
Iva Mihaylova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Trends ◽  
Dependence Analysis ◽  
Future Trends ◽  
Powerful Technique ◽  
Analytic Tool ◽  
The Future ◽  
Multivariate Dependence ◽  
Artificial Neural

Artificial neural networks (ANNs) are a powerful technique for multivariate dependence analysis. Originally inspired by neuroscience, ANNs are becoming an increasingly attractive analytic tool for applications in the area of economics and finance due to the flexible solutions they offer. The purpose of this chapter is to present such important applications with an emphasis on recent research trends. The contributions are grouped as follows: ANNs (1) for prediction, (2) for classification, and (3) for modelling. The chapter concludes with the future trends in the ANNs research in economics and finance.

scholarly journals Jaringan Syaraf Tiruan Untuk Memprediksi Nilai Siswa SMA Menggunakan Backpropagation

2021 ◽  
Vol 4 (2) ◽  
pp. 1-7
Author(s):  
Evan Hikler Damanik ◽  
Eka Irawan ◽  
Fitri Rizki
Keyword(s):  
High School ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rapid Development ◽  
Processing Data ◽  
The Future ◽  
Artificial Neural ◽  
Exam Scores ◽  
Student Grades

A student's mastery of a subject greatly influences the marking given by the teacher / teacher concerned. The need for instructors or teachers to monitor every value of students who are taught science in their respective fields. With the rapid development of technology, it is very helpful for teachers in knowing or predicting the value that students will get related. This study aims to apply the performance of backpropagation artificial neural networks in predicting the value of students of SMA N 1 Sidamanik with various models and minimizing their errors. In this study the authors used data on student grades from SMA N 1 Sidamanik. In processing data values, the authors use artificial neural networks with backpropagation algorithms as logical steps to predict student National Exam Scores in SMA N 1 Sidamanik. The main problem in this study is the decline in student grades in some subjects, in the future students will experience difficulties in reaching the desired university or high school.

Artificial Neural Networks in Medicine

2021 ◽  
pp. 1901-1918
Author(s):  
Steven Walczak
Keyword(s):  
Neural Networks ◽  
Resource Allocation ◽  
Artificial Neural Networks ◽  
Cost Reduction ◽  
Decision Problems ◽  
The Future ◽  
Recent Trends ◽  
Artificial Neural ◽  
Medical Domain ◽  
Future Direction

Artificial neural networks (ANNs) have proven to be efficacious for modeling decision problems in medicine, including diagnosis, prognosis, resource allocation, and cost reduction problems. Research using ANNs to solve medical domain problems has been increasing regularly and is continuing to grow dramatically. This chapter examines recent trends and advances in ANNs and provides references to a large portion of recent research, as well as looking at the future direction of research for ANN in medicine.

Artificial Neural Networks in Medicine

2022 ◽  
pp. 1491-1509
Author(s):  
Steven Walczak
Keyword(s):  
Neural Networks ◽  
Resource Allocation ◽  
Artificial Neural Networks ◽  
Cost Reduction ◽  
Decision Problems ◽  
The Future ◽  
Recent Trends ◽  
Artificial Neural ◽  
Medical Domain ◽  
Future Direction

Artificial neural networks (ANNs) have proven to be efficacious for modeling decision problems in medicine, including diagnosis, prognosis, resource allocation, and cost reduction problems. Research using ANNs to solve medical domain problems has been increasing regularly and is continuing to grow dramatically. This chapter examines recent trends and advances in ANNs and provides references to a large portion of recent research, as well as looking at the future direction of research for ANN in medicine.

Optimization of Material Selection for Whitening Cream: Artificial Neural Networks and Genetic Algorithm Approach

2017 ◽  
Vol 728 ◽  
pp. 416-421
Author(s):  
Chayathach Phuaksaman ◽  
Worrapon Wangkananon
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Material Selection ◽  
Early Stage ◽  
Performance Testing ◽  
Practice Performance ◽  
Wide Range ◽  
The Future ◽  
Artificial Neural

The materials in the skin care products have many types such as the whitening agent in the whitening cream. In general, researchers have conducted experiments to determine the effectiveness of such substances in the form of a single material in vitro. In practice, performance testing of material has conducted in Vivo that it is more than acceptable. In addition to use the materials altogether (synergistic) make higher performance or jump higher in a non-linear manner. The amount of these materials should be used in the right proportions. But to test the performance of materials in the real trial, each time it has a very high cost from volunteer, tracking, and duration of the experiment. We were unable to test the performance in several formulations. For this reason, the research objective is to develop algorithms to solve the problem of restrictions on such experiments to reduce time and cost. With a wide range of experimental design and use techniques to predict the outcome in early stage, then those results to design experiments and collect data. In this work, the effects of artificial neural networks (ANNs) to predict the effectiveness of the materials in whitening cream products were used incorporate with Genetic Algorithm (GA). In this article will experiment with simulated data from the experts as close to real data. To test the performance of the algorithm was developed and will extend the experimental results in the future. The algorithm of ANNs developed a multi-layer feed forward structure (4-13-1) with the lowest MSE is 6.00895e-4 and largest R2 is 0.979164. The best materials formulations optimized by GA were Arbutin=3%, Aloesin=0.658%, Niacinamide=0.007%, and Oxyresveratrol=0.993% that conduct lowest of 0.0823817 melanin value. Therefore, the algorithm developed in this study can be applied to develop the reality experiment in the future.

scholarly journals Avaliação de Uma Rede Neural Artificial Como Modelo Regressor Para Séries Temporais

2021 ◽  
Vol 6 (1) ◽  
pp. 33
Author(s):  
João Vitor Alves Da Cruz ◽  
Bruno Alberto Soares Oliveira
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Customer Service ◽  
Time Intervals ◽  
Demand Prediction ◽  
Artificial Neural ◽  
Prediction Techniques ◽  
Improve Service Quality ◽  
Industry Sectors

<p>Técnicas de predição de demanda são utilizadas em inúmeros ramos da indústria, com o objetivo de agregar valor ao negócio das empresas, especialmente por meio da busca pelo dimensionamento ótimo dos recursos de produção. A predição de demanda em refeitórios, com o intuito de balancear a quantidade de alimento produzido, buscando um melhor aproveitamento dos ingredientes, é um desafio, pois fatores como a quantidade de usuários, o tempo de atendimento e o tipo de alimento utilizado podem ser bastante variáveis neste tipo de problema. O estudo das filas, neste contexto, é de primordial importância, dado que, conhecendo suas características, podem-se estimar, por meio de previsão, informações que podem melhorar a qualidade de atendimento. O presente trabalho teve por finalidade utilizar modelos baseados em Redes Neurais Artificiais (RNA) para realizar regressões em uma série temporal personalizada, gerada por meio de metodologia própria, mediantes os dados coletados in loco no restaurante do IFMG - Campus Bambuí. Teve-se por principal objetivo desenvolver um modelo computacional que fosse capaz de descrever o comportamento para os intervalos de tempo no atendimento dos usuários. Por meio deste recurso, pôde-se gerar informações importantes para a tomada de decisão, como os horários de maior e menor pico de atendimento.</p><p><strong>Palavras-chave</strong>: Redes neurais artificiais, regressão, séries temporais.</p><p>==================================================================================</p><p>Demand prediction techniques are used in numerous industry sectors with the aim of adding value to the business of the companies, especially through the search for optimal sizing of production resources. The prediction of demand in restaurants with the intention of balancing the quantity food produced looking for better use of ingredients is a challenge, since factors like the quantity of users, the time of service and the kind of food can be quite variable in this type of problem. The study of queue, in this context, is of paramount importance, given that, knowing its characteristics, it is possible to estimate, by means of prediction, information that can improve service quality. Present work had the purpose of using models based on Artificial Neural Networks (ANN) to perform regressions in a personalized time series, generated through its own methodology with data collected in the restaurant of the IFMG - Campus Bambuí. The main objective was to develop a computational model that would be able to describe the behavior for the time intervals in the restaurant customer service. Through this resource, it was possible to generate important information for decision making, such as the peak times of higher and lower demands.</p><p><strong>Keywords</strong>: Artificial neural networks, regression, time series.</p>

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