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Pamungkas, Wahyu Widji

Program Studi TIP, FATETA IPB

Author-ID : 702628

Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Engineering Environmental Science Industrial & Manufacturing Engineering

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PEMODELAN KERANGKA ADAPTIVE THRESHOLD UNTUK MEMONITOR PRODUKSI MINYAK SAWIT NASIONAL BERBASIS STATISTICAL PROCESS CONTROL DAN ARTIFICIAL NEURAL NETWORK-BACKPROPAGATION Pamungkas, Wahyu Widji; Maarif, Syamsul; Irawadi, Tun Tedja; Arkeman, Yandra
Jurnal Industri Hasil Perkebunan Vol 11, No 2 (2016)
Publisher : Balai Besar Industri Hasil Perkebunan

Indonesia is the largest exporter of palm oil in the world, as the largest producer Indonesia still havemany problems. The problem caused by incomparable between the growth of upstream and downstreampalm oil industries. This impact to low added value of palm oil, then Indonesia exports palm oil in crudeform. On the other hand, On the other hand , orientation export of this commodity is also prone of barrier,because Indonesia was not the price setter of this commodity in the international market. Therefore it isimportant to monitor and predict the development of national palm oil production volume in order to takegood anticipation. This research develop a framework model adaptive threshold to monitor the growing ofnational palm oil production volume with techniques of statistical process control (SPC) and back propagationartificial neural network (ANN - BP) methods. Historical data production volume period from 1967 to 2015was used as a base of the behavior as data to determine the threshold and prediction volume for nextperiods. The formation of the threshold value was based on the behavior of the historical data, which areoriented by the epicenter of the average value in the last two periods .Through mapping of data historicalperiod values, existing and forecast values with adaptive threshold can show tolerant level for the threshold.Furthermore, based on the analysis, it is known that the prediction of 2016 to 2018 period, there will behappen the dynamics production volume of national palm oil within tolerance threshold. The values of thesepredictions generated from the simulation model predictions of ANN-BP with the level very good of validationmodel, demonstrated the level of squared errors is very small1 in the MSE = 0.00021136 with a degree ofoutput correlation and the target is very strong2 with R Validation is 99.98 percent.Keywords: adaptive threshold, statistical process control, artificial neural network, national palm oilproduction.

PEMODELAN STATISTICAL CONTROL DETECTION ADAPTIVE (SCDA) UNTUK MONITORING DAN PREDIKSI VOLUME PRODUKSI CRUDE PALM OIL (CPO) NASIONAL Wahyu Widji Pamungkas, M. Syamsul Ma'arif, Tun Teja Irawadi, Yandra Arkeman
Jurnal Teknologi Industri Pertanian Vol. 27 No. 1 (2017): Jurnal Teknologi Industri Pertanian
Publisher : Department of Agroindustrial Technology, Bogor Agricultural University

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Achievement of national palm oil industry as a producer and exporter of crude palm oil (CPO) in the world, it is now giving birth insecurity issues. This is because the growth of upstream and downstream industries of national palm oil that has not been balanced, which in turn encourages the national palm oil industry players to be oriented to the export of CPO which eliminates the added value in the country. On the other hand, though bring in foreign exchange for the country, but is prone commodity export orientation encountered a barriers problem in the international market. It is therefore important to provide a means of monitoring, prediction and assessment to facilitate the formulation of policies more about the marketing of national CPO industry. This research proposed the development of a model framework called adaptive threshold statistical control detection adaptive (SCDA) as a means of monitoring, prediction, and assessment of the movement of national CPO production volume. SCDA idea is to determine the dynamic threshold based mapping pattern historical data and predictions from the aspect of the frequency and trends. SCDA model adapted the techniques of statistical process control (SPC), while the values of the predictions generated from the simulation prediction model developed using the techniques of artificial neural network back propagation (ANN-BP) based on historical data of the national CPO production volume. The data used was the average volume of annual national CPO production period 1967 to 2015. The simulation results showed that the prediction model of national CPO production volume in 2016 until 2018 predicted were31.025 million, 32.214 million, and 34.504 million tons, respectively, while the values of maximum and minimum threshold that was formed in the model predictions SCDA for the period 2016-2018 each sequence were 33,322,065 and 29,246,547, respectively. As far as the literature search results, modeling SCDA has never been done in the research included for monitoring and prediction of national production volume of CPO. Therefore, research on the modeling of SCDA was contributing both to the development of knowledge about modeling as well as in the management of the national supply of CPO.Keywords: adaptive threshold, modelling, artificial neural network, palm oil

PEMODELAN KERANGKA ADAPTIVE THRESHOLD UNTUK MEMONITOR PRODUKSI MINYAK SAWIT NASIONAL BERBASIS STATISTICAL PROCESS CONTROL DAN ARTIFICIAL NEURAL NETWORK-BACKPROPAGATION Wahyu Widji Pamungkas; Syamsul Maarif; Tun Tedja Irawadi; Yandra Arkeman
Jurnal Industri Hasil Perkebunan Vol 11, No 2 (2016)
Publisher : Balai Besar Industri Hasil Perkebunan

Indonesia is the largest exporter of palm oil in the world, as the largest producer Indonesia still havemany problems. The problem caused by incomparable between the growth of upstream and downstreampalm oil industries. This impact to low added value of palm oil, then Indonesia exports palm oil in crudeform. On the other hand, On the other hand , orientation export of this commodity is also prone of barrier,because Indonesia was not the price setter of this commodity in the international market. Therefore it isimportant to monitor and predict the development of national palm oil production volume in order to takegood anticipation. This research develop a framework model adaptive threshold to monitor the growing ofnational palm oil production volume with techniques of statistical process control (SPC) and back propagationartificial neural network (ANN - BP) methods. Historical data production volume period from 1967 to 2015was used as a base of the behavior as data to determine the threshold and prediction volume for nextperiods. The formation of the threshold value was based on the behavior of the historical data, which areoriented by the epicenter of the average value in the last two periods .Through mapping of data historicalperiod values, existing and forecast values with adaptive threshold can show tolerant level for the threshold.Furthermore, based on the analysis, it is known that the prediction of 2016 to 2018 period, there will behappen the dynamics production volume of national palm oil within tolerance threshold. The values of thesepredictions generated from the simulation model predictions of ANN-BP with the level very good of validationmodel, demonstrated the level of squared errors is very small1 in the MSE = 0.00021136 with a degree ofoutput correlation and the target is very strong2 with R Validation is 99.98 percent.Keywords: adaptive threshold, statistical process control, artificial neural network, national palm oilproduction.

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