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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Aziz Awaludin
Universitas Bina Darma, Palembang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Application of the LSTM Algorithm in Predicting Urea Fertilizer Production at IIB Plant PT. Pupuk Sriwidjaja Palembang Aziz Awaludin; F Ferdiansyah; A Andri; Tri Oktarina
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.704

Abstract

PT. Pupuk Sriwidjaja Palembang is a pioneer of fertilizer manufacturers in Indonesia. One of the plants at PT. Pupuk Sriwidjaja Palembang, namely the IIB urea plant, has been operating normally since 2017, thereby the data of production results has been collected for more than five years (time series data). The collected data can be used to make predictions of future production using the LSTM (Long Short Term Memory) model. LSTM is an artificial neural network architecture that is suitable for processing sequential data. The research objective to be achieved is to produce a production prediction model using LSTM modeling. Data collected over five years was divided into training data and testing data through data composition trials. The LSTM model training was carried out with a training data composition of 70% of the total data, batch size 64, and epoch 200. Then testing was carried out with data testing as much as 30% of the total data using RMSE and MAPE as model quality assessment parameters. Based on test results, the LSTM model is able to predict production with an RMSE of 11.08 and a MAPE of 6.39%.
Application of the LSTM Algorithm in Predicting Urea Fertilizer Production at IIB Plant PT. Pupuk Sriwidjaja Palembang Aziz Awaludin; F Ferdiansyah; A Andri; Tri Oktarina
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.704

Abstract

PT. Pupuk Sriwidjaja Palembang is a pioneer of fertilizer manufacturers in Indonesia. One of the plants at PT. Pupuk Sriwidjaja Palembang, namely the IIB urea plant, has been operating normally since 2017, thereby the data of production results has been collected for more than five years (time series data). The collected data can be used to make predictions of future production using the LSTM (Long Short Term Memory) model. LSTM is an artificial neural network architecture that is suitable for processing sequential data. The research objective to be achieved is to produce a production prediction model using LSTM modeling. Data collected over five years was divided into training data and testing data through data composition trials. The LSTM model training was carried out with a training data composition of 70% of the total data, batch size 64, and epoch 200. Then testing was carried out with data testing as much as 30% of the total data using RMSE and MAPE as model quality assessment parameters. Based on test results, the LSTM model is able to predict production with an RMSE of 11.08 and a MAPE of 6.39%.
Co-Authors A Andri F Ferdiansyah Ferdiansyah Ferdiansyah Tri oktarina