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Indonesian air cargo transportation is currently experiencing quite significant development. One of the cargo services in Indonesia is Garuda Indonesia Cargo and has several branch offices. The existence of a revenue forecast model is expected to provide insight into a branch office. This research proposes an income prediction using the Deep Learning algorithm, Long Short Term Memory (LSTM). LSTM is used because the data to be processed is time series data. The results of testing accuracy are measured using Root Mean Squared Error (RMSE). The data in this study are income from one branch office, the Cargo Service Center (CSC) Tangerang City. Data contains collections of goods delivery transactions every day. The data goes through 4 preprocessing processes, namely subtotal, outlier detection, difference, and scaling. The results of this study show the best prediction results, namely the composition of the 90% train data and 10% test data with RMSE values of train data 641375.70 and test data 594197.70.


Long Short Term Memory (LSTM) Prediksi Deep Learning Long Short Term Memory LSTM Prediksi Deep Learning

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How to Cite
Aprian, B. A., Azhar, Y., & Nastiti, V. R. S. (2020). Prediksi Pendapatan Kargo Menggunakan Arsitektur Long Short Term Memory. Jurnal Komputer Terapan, 6(2), 148–157.


  1. C. Hu, Q. Wu, H. Li, S. Jian, N. Li, and Z. Lou, “Deep learning with a long short-term memory networks approach for rainfall-runoff simulation,†Water (Switzerland), vol. 10, no. 11, pp. 1–16, 2018, doi: 10.3390/w10111543.
  2. J. Schmidhuber, “Deep Learning in neural networks: An overview,†Neural Networks, vol. 61, pp. 85–117, 2015, doi: 10.1016/j.neunet.2014.09.003.
  3. G. Ghazali, Jondri, and M. Si, “Prediksi saham menggunakan dbn ( deep belief network ) stock prediction using dbn ( deep belief network ),†eProceedings Eng., vol. 4, no. 1, pp. 1258–1273, 2017.
  4. W. Bao, J. Yue, and Y. Rao, “A deep learning framework for financial time series using stacked autoencoders and longshort term memory,†J. Cheminform., vol. 24, no. 4, pp. 1–11, 2018, doi: 10.6084/m9.figshare.5028110.
  5. S. Li, H. Fang, and B. Shi, “Multi-Step-Ahead Prediction with Long Short Term Memory Networks and Support Vector Regression,†Chinese Control Conf. CCC, vol. 2018-July, pp. 8104–8109, 2018, doi: 10.23919/ChiCC.2018.8484066.
  6. Z. Chen, Y. Liu, and S. Liu, “Mechanical state prediction based on LSTM neural netwok,†Chinese Control Conf. CCC, pp. 3876–3881, 2017, doi: 10.23919/ChiCC.2017.8027963.
  7. M. A. D. Suyudi, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network,†Wikipedia, vol. 052, no. 735, pp. 3–6, 2015.
  8. M. Rizki, “Implementasi Deep Learning Menggunakan Arsitektur Long Short Term Memory(Lstm) Untuk Prediksi Curah Hujan Kota Malang,†vol. 2, no. 3, pp. 331–338, 2019.
  9. M. Wildan, P. Aldi, and A. Aditsania, “Analisis dan Implementasi Long Short Term Memory Neural Network untuk Prediksi Harga Bitcoin,†e-Proceeding Eng., vol. 5, no. 2, pp. 3548–3555, 2018.
  10. A. Ubrani and S. Motwani, “LSTM- and GRU-based time series models for market clearing price forecasting of Indian deregulated electricity markets,†Adv. Intell. Syst. Comput., vol. 898, pp. 693–700, 2019, doi: 10.1007/978-981-13-3393-4_70.
  11. Q. Wang, Y. Guo, L. Yu, and P. Li, “Earthquake Prediction based on Spatio-Temporal Data Mining: An LSTM Network Approach,†IEEE Trans. Emerg. Top. Comput., vol. 6750, no. c, pp. 1–1, 2017, doi: 10.1109/tetc.2017.2699169.