LONG-SHORT TERM MEMORY (LSTM) FOR PREDICTING VELOCITY AND DIRECTION SEA SURFACE CURRENT ON BALI STRAIT
Abstract
The strategic role of the Bali Strait as a connection between the islands of Java and Bali is growing in line with the increase in the economy and tourism of the two islands. Therefore, it is necessary to have a further understanding of the condition of the waters in the Bali strait, one of which is ocean currents. This study aims to predict future ocean currents based on 30-minute data in the Bali Strait in the range of 16 May 2021 to 9 June 2021 obtained from the Perak II Surabaya Maritime Meteorological Station. In this study, the Long Short Term Memory method was used. The parameters used are hidden layer, batch size, and learn rate drop. Based on the parameters used, the results showed that the smallest MAPE value was 18.64% for U ocean current velocity data and 5.29% for V ocean current velocity data.
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A. Listiani, D. Wijayanto, and B. B. Jayanto, “Analisis CPUE (Catch Per Unit Effort) dan Tingkat Pemanfaatan Sumberdaya Perikanan Lemuru (Sardinella lemuru) di Perairan Selat Bali,” J. Perikan. Tangkap Indones. J. Capture Fish., vol. 1, no. 01, pp. 1–9, 2017.
F. Setiawan, “Pergerakan Arus Permukaan Laut Selat Bali Berdasarkan Parameter Angin Dan Cuaca,” J. Ris. Kelaut. Trop. (Journal Trop. Mar. Res., vol. 1, no. 2, p. 1, 2020, doi: 10.30649/jrkt.v1i2.25.
H. P. Sihombing, I. G. Hendrawan, and Y. Suteja, “Analisis Hubungan Kelimpahan Plankton di Permukaan Terhadap Hasil Tangkapan Ikan Lemuru (Sardinella lemuru) di Selat Bali,” J. Mar. Aquat. Sci., vol. 4, no. 1, p. 151, 2017, doi: 10.24843/jmas.2018.v4.i01.151-161.
E. Usman, F. Novico, K. Budiono, and P. Raharjo, “Analisis Geoteknik Kelautan Pada Sisi Ketapang (Selat Bali) Untuk Pengembangan Penghubung Jawa - Bali,” J. Geol. Kelaut., vol. 2, no. 2, 2016, doi: 10.32693/jgk.2.2.2004.113.
E. Ropika, R. R. Atmawidjaja, M. Mahfudz, A. Arus, and K. Arus, “Pemodelan Pola Arus Laut Permukaan Diperairan Selat Badung , Bali Untuk Kepentingan Navigasi Dan Pelayaran,” pp. 1–10, 2019.
Try Al Tanto, Ulung Jantama Wisha, Gunardi Kusumah, Widodo S Pranowo, Semeidi Husrin, and Aprizon Putra, “Characteristics of Sea Current in Benoa Bay Waters – Bali,” Ilm. Geomatika, vol. 23, no. 1, pp. 37–48, 2017.
E. Supriyadi, “Prediksi Parameter Cuaca Menggunakan Deep Learning Long-Short Term Memory (Lstm),” J. Meteorol. dan Geofis., vol. 21, no. 2, p. 55, 2021, doi: 10.31172/jmg.v21i2.619.
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
F. S. Dilla Dwi Kartika, Dian Candra Rini Novitasari, “Prediksi Kecepatan Arus Laut di Perairan Selat Bali menggunakan Metode Exponential Smoothing Holt-Winters,” vol. 02, no. 01, pp. 12–17, 2020.
Laily Jumhuriyah, Dian C. Rini Novitasari, and Fajar Setiawan, “Prediksi Kecepatan Arus Laut dengan Menggunkan Metode Backpropagation (Studi Kasus: Labuhan Bajo),” J. Algebr., vol. 1, no. 1 SE-Articles, pp. 99–108, 2020.
M. A. Dwiyanto, C. E. Djamal, and A. Maspupah, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network,” Semin. Nas. Apl. Teknol. Inf., pp. 33–38, 2019.
G. A. Novi Yanti, Eka Pandu Cynthia, Yelfi Vitriani, Yusra, “Prediksi Radiasi Matahari Dengan Penerapan Metode Elman Recurrent Neural Network,” Semin. Nas. Teknol. Informasi, Komun. dan Ind., no. November, pp. 22–29, 2019.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.
M. W. P. Aldi, Jondri, and A. Aditsania, “Analsis dan Implementasi Long Short Term Memory Neural Network Untuk Prediksi Harga Bekicot,” J. Inform., vol. 5, No, no. 2, p. 3548, 2018, [Online]. Available: http://openlibrarypublications.telkomniversity.ac.id.
A. S. B. Karno, “Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory),” J. Inform. Inf. Secur., vol. 1, no. 1, pp. 1–8, 2020, doi: 10.31599/jiforty.v1i1.133.
R. Julian and M. R. Pribadi, “Peramalan Harga Saham Pertambangan Pada Bursa Efek Indonesia (BEI) Menggunakan Long Short Term Memory (LSTM),” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 3, pp. 1570–1580, 2021, doi: 10.35957/jatisi.v8i3.1159.
P. A. Riyantoko and T. M. Fahruddin, “Analisis Prediksi Harga Saham Sektor Perbankan Menggunakan Algoritma Long-Short Terms Memory (Lstm),” Semin. Nas. …, vol. 2020, no. Semnasif, pp. 427–435, 2020, [Online]. Available: http://www.jurnal.upnyk.ac.id/index.php/semnasif/article/view/4135.
X. Li et al., “Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation,” Environ. Pollut., vol. 231, no. December, pp. 997–1004, 2017, doi: 10.1016/j.envpol.2017.08.114.
C. J. Huang and P. H. Kuo, “A deep cnn-lstm model for particulate matter (Pm2.5) forecasting in smart cities,” Sensors (Switzerland), vol. 18, no. 7, 2018, doi: 10.3390/s18072220.
L. Widyarsi et al., “Prediksi Kasus Covid-19 Melalui Analisis Data Google Trend Di Indonesia : Pendekatan Metode Long Short Term Memory (LSTM),” J. SAINTIKA UNPAM J. Sains dan Mat. Unpam, vol. 3, no. 2, pp. 161–177, 2021.
P. D. Pakan, “Peramalan Kasus Positif Covid 19 Di Indonesia Menggunakan Lstm,” J. Ilm. Flash, vol. 6, no. 1, pp. 12–15, 2020.
F. D. Adhinata and D. P. Rakhmadani, “Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method,” Teknika, vol. 10, no. 1, pp. 62–67, 2021, doi: 10.34148/teknika.v10i1.328.
and H. Z. Ghassen Chniti, Houda Bakir, “E-commerce Time Series Forecasting using LSTM Neural Network and Support Vector Regression. In Proceedings of the International Conference on Big Data and Internet of Thing,” Assoc. Comput. Mach. New York, NY, USA, pp. 80–84, 2017, [Online]. Available: https://doi.org/10.1145/3175684.3175695.
H. Bakir, G. Chniti, and H. Zaher, “E-Commerce price forecasting using LSTM neural networks,” Int. J. Mach. Learn. Comput., vol. 8, no. 2, pp. 169–174, 2018, doi: 10.18178/ijmlc.2018.8.2.682.
I. I. Zulfa, D. C. R. Novitasari, F. Setiawan, A. Fanani, and M. Hafiyusholeh, “Prediction of Sea Surface Current Velocity and Direction Using LSTM,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 11, no. 1, pp. 93–102, 2021, doi: 10.22146/ijeis.63669.
E. S. Putri, M. Sadikin, J. T. Informatika, F. I. Komputer, and U. M. Buana, “Prediksi Penjualan Produk Untuk Mengestimasi Kebutuhan Bahan Baku Menggunakan Perbandingan Algoritma LSTM dan ARIMA,” vol. 10, pp. 162–171, 2021.
A. Arfan and L. ETP, “Perbandingan Algoritma Long Short-Term Memory dengan SVR Pada Prediksi Harga Saham di Indonesia,” Petir, vol. 13, no. 1, pp. 33–43, 2020, doi: 10.33322/petir.v13i1.858.
R. Modalo, R. Rampengan, E. Opa, R. Djamaluddin, H. Manengkey, and N. Bataragoa, “Arah dan kecepatan arus perairan sekitar Pulau Bunaken pada periode umur bulan perbani di musim pancaroba II,” J. Pesisir Dan Laut Trop., vol. 6, no. 1, p. 61, 2018, doi: 10.35800/jplt.6.1.2018.20201.
B. Rochaddi, Ludy Cahya Permadi, Elis Indrayanti, “Studi Arus pada Perairan Laut di sekitar PLTU Sumuradem Kabupaten Indramayu, Provinsi Jawa Barat.,” Oseanografi, vol. 4, pp. 516–523, 2015.
D. Berlianty and T. Yanagi, “Tide and Tidal Current in the Bali Strait, Indonesia,” Mar. Res. Indones., vol. 36, no. 2, pp. 25–36, 2015, doi: 10.14203/mri.v36i2.39.
G. A. B. Suryanegara, Adiwijaya, and M. D. Purbolaksono, “Peningkatan Hasil Klasifikasi pada Algoritma Random Forest untuk Deteksi,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 114–122, 2021.
A. Khoirunnisa, Adiwijaya, and A. A. Rohmawati, “Implementing principal component analysis and multinomial logit for cancer detection based on microarray data classification,” 2019 7th Int. Conf. Inf. Commun. Technol. ICoICT 2019, pp. 1–6, 2019, doi: 10.1109/ICoICT.2019.8835320.
L. Wiranda and M. Sadikin, “Penerapan Long Short Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk Pt. Metiska Farma,” J. Nas. Pendidik. Tek. Inform., vol. 8, no. 3, pp. 184–196, 2019.
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015.
L. A. Andika, H. Pratiwi, and S. S. Handajani, “Lingga Aji Andika 1 , Hasih Pratiwi 2 , and Sri Sulistijowati Handajani 3 1,” Indones. J. Stat. Its Appl., vol. 3, no. 3, pp. 331–340, 2019.
N. M. Ashar, I. Cholissodin, and C. Dewi, “Penerapan Metode Extreme Learning Machine ( ELM ) Untuk Memprediksi Jumlah Produksi Pipa Yang Layak ( Studi Kasus Pada PT . KHI Pipe Industries ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 11, pp. 4621–4628, 2018.
D. Z. Haq et al., “Long Short-Term Memory Algorithm for Rainfall Prediction Based on El-Nino and IOD Data,” Procedia Comput. Sci., vol. 179, no. 2019, pp. 829–837, 2021, doi: 10.1016/j.procs.2021.01.071.
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