Implementation of Backpropagation Artificial Neural Networks to Predict Palm Oil Price Fresh Fruit Bunches

Ismanto, Edi; Effendi, Noverta; Cynthia, Eka Pandu

doi:10.30645/ijistech.v2i1.17

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…[2] Metode JST yang sering digunakan dalam studi kasus prediksi adalah, Backpropagation. Backpropagation merupakan algoritma pembelajaran yang terawasi dan serta umumnya dipakai oleh Perceptron dengan banyak lapisan untuk mengganti bobot-bobot yang terhubung dengan neuron-neuron yang terdapat pada lapisan tersembunyinya [3]. Backpropagation menggunakan model pembelajaran terbimbing yang dapat meminimalisir kesalahan pada output yang dihasilkan oleh jaringan karena menggunakan multilayer konsep [4].…”

Section: Pendahuluanunclassified

Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation

Thoriq¹

2022

jidt

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Artificial Neural Network (ANN) technique has developed rapidly in the field of estimation. ANN can predict based on data on events and related factors that existed in the past. ANN has advantages in parallel computing in classifying patterns. ANN is also capable of self-regulating the data to be processed without requiring an explicit function specification. The advantage of using ANN is the elimination of complex analytical and numerical iterative computations. The ANN method that is often used in prediction case studies is the Backpropagation Algorithm. This algorithm has the ability to solve problems in the real world by building trained methods that show good performance on large data scales and are able to overcome complex pattern recognition. This study aims to predict the demand for salt optimally using the ANN Method with the Backprogation Algorithm at PT. Kurnia Garam Prosperous Padang City. This forecasting is needed because of the high cost of production with the large number of requests that occur to be more effective. Proper forecasting will be able to optimize production so that it can reduce the required production costs. The data processed is salt production data from 2016 to 2018 at PT. Kurnia Garam Prosperous. The momentum results obtained are 3-9-1 for dividing the data into 2, namely 24 training data and 12 test data. The optimal prediction result is 0.98946, so this research is very helpful in forecasting optimal and efficient production costs.

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Section: Pendahuluanunclassified

Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation

Thoriq¹

2022

jidt

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“…Backpropagation merupakan algoritma pembelajaran yang terawasi dan serta umumnya dipakai oleh Perceptron dengan banyak lapisan untuk mengganti bobot-bobot yang terhubung dengan neuron-neuron yang terdapat pada lapisan tersembunyinya [4]. Backpropagation menggunakan model pembelajaran terbimbing yang dapat meminimalisir kesalahan pada output yang dihasilkan oleh jaringan karena menggunakan multilayer konsep [5].…”

Section: Pendahuluanunclassified

Prediksi dan Klasifikasi Buku Menggunakan Metode Backpropagation

Rahmiyanti

Defit

Yunus

2021

jidt

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Students of SMP Negeri 2 Lengayang have different interests in determining the books they are interested in, so that the library often has difficulty determining the books that are most entered by students, this is because they have not used the right system in determining the type and number of books, only based on the estimated number. Students and subjects only, as a result school students stock books of the books they want to borrow. Based on the above, a method is needed to predict and classify the amount of book stock in the future. The data used is a recap of monthly book lending, from 2018 to 2020 in the third month, with a total of 1653 transactions and 5 types of books processed, then the data is analyzed using the Backpropogation method. The results obtained are using a 5-3-1 pattern with a learning rate of 0.01, a goal of 0.01, the number of input units for the Weapon layer 5, the number of units in the hidden layer and the number of output layer units that are placed on 1 layer, and to carry out training using two phases namely feedforward and backpropagation phases. It is removed from this research that the backpropagation method can provide a classification prediction of the number of books that must be provided in the following year based on the number of data entered or the number of data entered.

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“…The Backpropagation algorithm has applied to predict rice production; thus, it can be anticipated in advance so that rice production can be obtained become self-sufficient in food [10]. The backpropagation Algorithm can produce information systems that can predict the oil palm commodity price as an essential reference in taking company policy [11]. Algorithm Backpropagation has been applied to predict rice productivity outcomes to optimize agricultural techniques [12].…”

Section: Previousresearchmentioning

confidence: 99%

Backpropagation Neural Network for Predict Sugarcane Stock Availability

2020

IJATCSE

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National sugar consumption from 2013 to 2016 experienced an upward trend of 6.14%, especially for 2016, demand increased by 5.08% from the previous year [1]. Increment consumption of this sugar is inversely proportional to production results domestic sugarcane. Domestic sugarcane yields each year has decreased due to the problem of sugarcane on-farm or off the farm. Hence the government is trying to import sugarcane raw materials to fulfill national sugar needs. In importing raw materials sugarcane, the government needs predictive data for sugarcane production and the national sugar needs that will come used as a reference for the amount of raw sugarcane that needs to be imported. This study aims to estimate the results of sugarcane production based on historical data from 2017 to 2019. After making further predictions, it is necessary to evaluate the prediction model used. Method research used to predict production yield sugarcane, namely the Backpropagation Neural Network.Furthermore, the evaluation of the model used uses the Mean Absolute Percentage Error (MAPE). MAPE delivers a hint of how large the forecast error is compared with the series's real value. The following result 97% predictable production and 3% MAPE yield, then it can be said that the result of the method Backpropagation Neural Network has modeling capabilities decent forecast.

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Implementation of Backpropagation Artificial Neural Networks to Predict Palm Oil Price Fresh Fruit Bunches

Cited by 3 publications

References 7 publications

Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation

Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation

Prediksi dan Klasifikasi Buku Menggunakan Metode Backpropagation

Backpropagation Neural Network for Predict Sugarcane Stock Availability

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