Rancang Bangun Aplikasi Peramalan Jumlah Penumpang Menggunakan Long Short-Term Memory (LSTM)

  • Muhammad Davi Politeknik Negeri Lhokseumawe
  • Edi Winarko Universitas Gadjah Mada
Abstract views: 334 , PDF downloads: 418
Keywords: long short-term memory, exponential smoothing, machine learning, forecasting, time series

Abstract

Public services such as public transportation are closely related to user satisfaction. Busway DKI Jakarta is one of the first public transportation services in Southeast and South Asia. In order to maintain passenger satisfaction, the management of Busway continues to improve services such as adding buses and opening a new line. Opening a new lane or adding buses must necessarily be adjusted also with the increasing number of passengers. So to know the number of passengers in the future, it is necessary to forecast the number of passengers through existing historical data. The historical data used is time series data from January 2015 to January 2016. The method used in forecasting is Long Short-Term Memory (LSTM), one of the machine learning methods. The method is measured in accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). As a comparison of LSTM accuracy in forecasting, we also use the Exponential Smoothing method. Based on the results of forecasting, the most and least dominant method of producing RMSE and MAPE is the LSTM method. Only in corridor 3 LSTM can not provide RMSE and MAPE values below baseline values. While corridor 5 LSTM can give better results after the data transformation process by using exponential smoothing. However, overall the LSTM method provides the best accuracy based on the lowest average RMSE and MAPE values, namely RMSE of 2640.53 and MAPE of 9.14%.

References

Chairunnisa, “Hubungan Kinerja Organisasi dan Kualitas Pelayanan Transjakarta Busway dengan Tingkat Kepuasan Pelanggan Pengguna Jasa Transjakarta-Busway (Studi Kasus pada Transjakarta-Busway Koridor IV Pulo Gadung – Dukuh Atas DKI Jakarta),” Skripsi, Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Diponegoro, Semarang, 2008.

A. Rudi, “Transjakarta Catat Rekor Baru Jumlah Penumpang.” 2016. [Online]. Available: http://megapolitan.kompas.com/read/2016/09/06/07272441/transjakarta.catat.rekor.-baru.jumlah.penumpang

I. Ein, I. Ernawati, and Y. Widiastiwi, “Analisis Sentimen Terhadap Layanan Transjakarta Pada Media Sosial Instagram Menggunakan Naïve Bayes dan Seleksi Fitur Information Gain,” Pros. Semin. Nas. Mhs. Bid. Ilmu Komput. Dan Apl., vol. 3, no. 2, pp. 442–451, 2022.

G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.

B. P. Dewani, S. Suhartono, and M. S. Akbar, “Peramalan Jumlah Penumpang dan Barang di Bandar Udara Internasional Juanda dan Pelabuhan Tanjung Perak Menggunakan Model Hybrid ARIMAX dan Deep Learning Neural Networks,” Inferensi, vol. 2, no. 1, p. 1, Mar. 2019, doi: 10.12962/j27213862.v2i1.6805.

S. Hasanah, “Peramalan Jumlah Penumpang di Bandara Internasional Juanda Menggunakan Metode ARIMA, Regresi Time Series, TBATS,” Justek J. Sains Dan Teknol., vol. 2, no. 1, p. 27, May 2019, doi: 10.31764/justek.v2i1.3720.

K. Albeladi, B. Zafar, and A. Mueen, “Time Series Forecasting using LSTM and ARIMA,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 1, 2023, doi: 10.14569/IJACSA.2023.0140133.

Sofiana, Suparti, A. R. Hakim, and I. T. Utami, “PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDARA INTERNASIONAL AHMAD YANI DENGAN METODE HOLT WINTER’S EXPONENTIAL SMOOTHING DAN METODE EXPONENTIAL SMOOTHING EVENT BASED,” vol. 9, no. 4, 2020.

W. F. Mujtaba, I. G. A. M. Srinadi, and I. W. Sumarjaya, “PERAMALAN JUMLAH PENUMPANG PESAWAT BANDARA I GUSTI NGURAH RAI MENGGUNAKAN EXPONENTIAL SMOOTHING DAN RUEY-CHYN TSAUR,” E-J. Mat., vol. 10, no. 4, p. 222, Nov. 2021, doi: 10.24843/MTK.2021.v10.i04.p346.

R. Aziz and Helma, “Peramalan Jumlah Penumpang Pesawat di Bandar Udara Soekarno-Hatta dengan Pemulusan Eksponensial Tripel,” 2022.

L. Wiranda and M. Sadikin, “PENERAPAN LONG SHORT TERM MEMORY PADA DATA TIME SERIES UNTUK MEMPREDIKSI PENJUALAN PRODUK PT. METISKA FARMA,” vol. 8, 2019.

P. Arsi and J. Prayogi, “Optimasi Prediksi NilaiTukar Rupiah Terhadap Dolar Menggunakan Neural Network Berbasiskan Algoritma Genetika,” J. Inform., vol. 7, no. 1, pp. 8–14, Apr. 2020, doi: 10.31311/ji.v7i1.6793.

S. Elsworth and S. Güttel, “Time Series Forecasting Using LSTM Networks: A Symbolic Approach.” arXiv, Mar. 12, 2020. Accessed: Jul. 04, 2023. [Online]. Available: http://arxiv.org/abs/2003.05672

M. Mukhlis, A. Kustiyo, and A. Suharso, “Peramalan Produksi Pertanian Menggunakan Model Long Short-Term Memory,” BINA INSANI ICT J., vol. 8, no. 1, p. 22, Jun. 2021, doi: 10.51211/biict.v8i1.1492.

Salahuddin, M. Khadafi, Huzaeni, and M. Davi, “Rancang Bangun Aplikasi Machine Learning Prediksi Hasil Panen Buah Pinang (Areca Catechu) Menggunakan Metode Regresi Linier Berganda,” Proceeding Semin. Nas. Politek. Negeri Lhokseumawe, vol. 6, no. 1, pp. 181–186, 2022.

F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to Forget: Continual Prediction with LSTM,” 1999 Ninth Int. Conf. Artif. Neural Netw. ICANN 99 Conf Publ No 470, vol. 2, pp. 850–855, 1999, doi: 10.1049/cp:19991218.

R. Cahyadi, A. Damayanti, and D. Aryadani, “RECURRENT NEURAL NETWORK (RNN) DENGAN LONG SHORT TERM MEMORY (LSTM) UNTUK ANALISIS SENTIMEN DATA INSTAGRAM,” vol. 5, no. 1, 2020.

P. A. Qori, D. S. Oktafani, and I. Kharisudin, “Analisis Peramalan dengan Long Short Term Memory pada Data Kasus Covid-19 di Provinsi Jawa Tengah,” vol. 5, 2022.

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Published
2023-07-31