Prediksi Pendapatan Kargo Menggunakan Arsitektur Long Short Term Memory
DOI:
https://doi.org/10.35143/jkt.v6i2.3621Keywords:
Long Short Term Memory (LSTM), Prediksi, Deep LearningAbstract
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.Downloads
References
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.
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.
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.
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.
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.
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.
M. A. D. Suyudi, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network,†Wikipedia, vol. 052, no. 735, pp. 3–6, 2015.
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.
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.
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.
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.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Jurnal Komputer TerapanÂ
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright info for authors
1. Authors hold the copyright in any process, procedure, or article described in the work and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors retain publishing rights to re-use all or portion of the work in different work but can not granting third-party requests for reprinting and republishing the work.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License 
