Time series based forecasting for crude palm oil price utilizing neural network algorithms

Kanchymalay, Kasturi; Sallehuddin, Roselina; Salim, Naomie; Hashim, Ummi Rabaah

doi:10.1109/ict-ispc.2017.8075334

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…Hal ini dimaksudkan agar produksi dapat sesuai dengan target dan mencegah kerugian dari biaya produksi dan mendukung manajemen dalam pengambilan keputusan (Meryana, 2017). Untuk memudahkan perencanaan produksi di masa depan, proses estimasi produksi harus didasarkan pada data produksi pada tahun-tahun sebelumnya (Gunawan, Khodra, & Harlili, 2013;Haviluddin & Alfred, 2014;Haviluddin & Jawahir, 2015;Kanchymalay, Sallehuddin, Salim, & Hashim, 2017).…”

Section: Pendahuluanunclassified

“…Hal ini ditandai dengan nilai MAPE sebesar 2.10 % dan nilai RMSPE sebesar 2.61 % yang berarti prediksi mendekati nilai data sebenarnya. (Kanchymalay et al, 2017) juga menerapkan metode kecerdasan buatan yaitu Backpropagation Neural Network (BPNN) untuk memprediksi minyak kelapa sawit (CPO) di Malaysia. Hasil penelitian menunjukkan bahwa metode BPNN mampu memprediksi CPO dengan akurat berdasarkan nilai root mean square error (RMSE) dan R2 yang masing-masing menghasilkan nilai 41.884536 dan 0.99600.…”

Section: Pendahuluanunclassified

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Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Aini

Haviluddin

Budiman

et al. 2019

JSAKTI

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Artikel ini bertujuan untuk memprediksi produksi minyak kelapa sawit mentah (CPO) di PT. Perkebunan Nusantara (PTPN) XIII, Desa Long Pinang. Kabupaten Paser, Kalimantan Timur dengan menggunakan algoritma cerdas, jaringan saraf tiruan (JST) yang disebut Backpropagation Neural Network (BPNN). Data penelitian berasal dari produksi CPO periode Januari 2015 hingga Januari 2018. Pengukuran akurasi prediksi algoritma BPNN menggunakan metode mean square error (MSE). Berdasarkan hasil percobaan, metode BPNN dengan parameter arsitektur 5-10-11-12-13-1; fungsi pembelajaran adalah trainlm; fungsi aktivasi adalah logsig dan purelin; laju pembelajaran adalah 0.7 mampu menghasilkan tingkat kesalahan prediksi yang baik dengan nilai MSE sebesar 0.0069. Hasil penelitian menunjukkan bahwa model ini dapat digunakan sebagai alternatif metode dalam memprediksi produksi CPO pada tahun 2019.

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Section: Pendahuluanunclassified

Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Aini

Haviluddin

Budiman

et al. 2019

JSAKTI

View full text Add to dashboard Cite

show abstract

“…In [ 11 ], besides researching the uses of ANN in non-linear models, the authors also highlighted the important developments in real-time data forecasting by ANN. There have been various studies where ANN is applied successfully to analyze the real-time data in domains such as electricity load forecasting [ 12 , 13 , 14 ], stock price prediction [ 15 , 16 ], commodity price prediction [ 17 ], etc., as explained further below.…”

Section: Background and Preliminary Analysismentioning

confidence: 99%

“…In [ 17 ], the price of crude palm oil (Soy bean oil) has been predicted using ANN, where the data was comprised of the historical price of Soy bean oil and the currency exchange rate. Here, the authors have studied the performance of the model by changing the network structure to get the best results.…”

Section: Background and Preliminary Analysismentioning

confidence: 99%

Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home

Khan

Ghani

Haider

2018

Sensors

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IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor’s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

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Time series based forecasting for crude palm oil price utilizing neural network algorithms

Cited by 2 publications

References 12 publications

Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home

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