COMPARISON OF SALINITY AND SEAWATER TEMPERATURE PREDICTIONS USING VAR AND BIRESPONSE FOURIER SERIES ESTIMATOR
Abstract
Salinity is the concentration of dissolved salts in water. The salt in question is a variety of ions dissolved in water, including table salt (NaCl). Salinity and seawater temperature are one of the factors that affect salt production. The higher the NaCl content, the better the quality of the salt. Currently, people's salt production is still unable to meet the needs of national salt, especially industrial salt, because most of the quality of people's salt still does not meet the SNI criteria for industrial salt. Thus, it is necessary to predict the salinity and temperature of seawater to help determine the next steps or policies in improving the quality of people's salt. Predictions of salinity and seawater temperature were carried out by applying the Vector Autoregressive (VAR) Analysis method and nonparametric Fourier series regression with primary data of salinity and seawater temperature on the coast of Tlesah Tlanakan Beach, Pamekasan. The best model chosen is the model that has the smallest error size and the highest accuracy measure. The best models are nonparametric regression of the Fourier series of sine and cosine bases with the predicted result obtaining a MAPE value is 0.00496 and coefficient of determination is 100%.
Downloads
References
C. K. Aini, Prediksi Salinitas Menggunakan Fuzzy Sugeno, Pamekasan: Universitas Islam Madura, 2020.
S. N. Putri, Y. I. Satria and N. Hendrianie, "Pra Desain Pabrik Garam Industri dari Garam Rakyat," Jurnal Teknik ITS, vol. 9, no. 2, pp. F151-F156, 2020.
K. D. Maulana, m. m. Jamil, P. E. M. Putra, B. Rohmawati and Rahmawati, "peningkatan Kualitas Garam bledug Kuwu Melalui Proses Rekristalisasi dengan Pengika Pengotor," Journal of Creativity Student, vol. II, no. 1, pp. 42-46, 2017.
Pusat Pengkajian Perdagangan Dalam Negeri, "Analisis Struktur Biaya Produksi Garam Rakyat," in Laporan Akhir, Jakarta, Badan Pengkajian dan Pengembangan Perdagangan Kementerian Perdagangan, 2019.
M. R. A. Pranata, PENERAPAN STRUCTURAL SEASONAL VECTOR AUTOREGRESSIVE (SSVAR) PADA INFLOW-OUTFLOW UANG KARTAL, Malang: Universitas Brawijaya, 2018.
R. Yuriska, A. A. Rohmawati and A. Aditsania, "Forecasting Jumlah Kasus Harian Covid-19Di Provinsi Jawa Barat Menggunakan Model Vector Autoregressive (VAR)," e-Proceeding of Engineering, vol. VIII, no. 5, pp. 11376-11387, 2021.
K. Rajab, F. Kamalov and A. K. Cherukuri, "Forecasting COVID-19: Vector Autoregression-Based Model," Arabian Journal for Science and Engineering, no. 47, p. 6851–6860, 2022.
T. W. Utami and I. M. Nur, "Aplikasi Regresi Nonparametrik Deret Fourier Pada Data High Water Level (HWL) Kota Semarang," Statistika, vol. 5, no. 2, pp. 57-61, 2017.
L. J. Christiano and A. Cristoper, "Sims and Vektor autoregressive," Jurnal of Ekononic, pp. 1082-1104, 2012.
R. Handayani, S. Wahyuningsih and D. Yuniarti, "Modeling Generalized Space Time Autoregressive (GSTAR) on Inflation Data In Samarinda And Balikpapan," Jurnal Eksponensial, vol. IX, no. 2, pp. 153-161, 2018.
B. Junda and Junaidi, Ekonometrika Deret Waktu, Bogor: IPB Press, 2012.
W. W. Utami, Pemodelan Ispa, Faktor Cuaca dan PM10 dengan Mengunakan Vector Autoregressive, Pekanbaru: Universitas Islam Syarif Kasim Riau, 2020.
E. N. Susanti and R. Zamora, "Analisis Kausalitas Pertumbuan Ekonomi Terhadap Indeks Pembangunan Manusia Di Provinsi Kepulauan Riau," Dimensi, vol. VIII, no. 3, pp. 473-484, 2019.
P. R. Hardani, A. Hoyyi and Sudarno, "Peramalan Laju Inflasi , Suku Bunga indonesia dan Indeks Harga Saham Gabungan Menggunkan Metode Vector Autoregressive (VAR)," Gaussian, vol. VI, no. 1, pp. 101-110, 2016.
F. N. Hayati, Peramalan Harga Saham Jakarta Islamic Index Menggunakan Metode Vector Autoregressive, Surabaya: Institut Teknologi Sepuluh Nopember, 2016.
D. R. Hardine, A. Abdullah, M. Ikbal and N. Chamidah, "Pemodelan Kadar Gula Darah dan Tekanan Darah Pada Remaja Penderita Diabetes Miletus Tipe II dengan pendekatan Regresi Nonparametrik Birespon Berdasarkan Estimator Spline," Seminar Nasional Matematika dan Aplikasiny, pp. 308-312, 2017.
M. F. F. Mardianto, E. Tjahjono and M. Rifada, "Semiparametric Regression Based on Three Forms of Trigonometric Function in Fourier Series Estimator," in IOP Publishing, 2019.
R. Kustianingsih, M. F. F. Mardianto, B. A. Ardhani, Kuzairi, A. Thohari, R. Andriawan and T. Yulianto, "Fourier series estimator in semiparametric," AIP Conference Proceedings, vol. 2329, pp. 060023-1-060023-9, 2021.
A. Sholiha, Kuzairi and M. F. F. Mardianto, "Estimator Deret Fourier Dalam Regresi Nonparametrik dengan Pembobot Untuk," Zeta – Math Journal, vol. 4, no. 1, pp. 18-23, 2018.
E. Vivas, H. Allende-Cid and S. Rodrigo, "ASystematic Review of Statistical and Machine Learning Methods for Electrical Power Forcasting with Reported MAPE Score," entropy, vol. XXII, no. 1412, 2020.
Copyright (c) 2022 Faisol Faisol, Putri Ukhrowi, M. Fariz Fadillah Mardianto, Ira Yudistira, Kuzairi Kuzairi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
