Prediksi Kualitas Air Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA)
Abstract
Water conservation is very necessary to support the creation of clean water quality that is free from harmful substances that can disturb the environment. So a system is needed to monitor water quality to determine the level of pollution that occurs. This system will work to see water quality in real time with several quality parameters such as pH, temperature, and water turbidity. The purpose of this research is to produce a predictive model and find out the prediction results of a data mining-based system. The method used to predict water quality uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, because the water quality data is thought to contain seasonal patterns. The results of this study indicate that the SARIMA model can be applied to the dataset used and obtain the accuracy of the forecasting results on each of the tested parameter data. The results of water quality forecasting with this parameter are the result data for testing at a dataset of a depth of 30 cm and a depth of 60 cm for temperature parameters, namely MSE<0.1, and RMSE<0.02. For pH parameters, MSE<0.1, and RMSE<0.1. As well as the turbidity parameter, the results of MSE<0.02, and RMSE<0.13. From these results indicate that this system can predict water quality with past data.
References
S. R. Fauzia and S. H. Suseno, “Resirkulasi Air Untuk Optimalisasi Kualitas Air Budidaya Ikan Nila Nirwana (Oreochromis niloticus),” Jurnal Pusat Inovasi Masyarakat (PIM), vol. 2, no. 5, Art. no. 5, Jul. 2020.
D. Azhari and A. M. Tomasoa, “Kajian Kualitas Air dan Pertumbuhan Ikan Nila (Oreochromis niloticus) yang Dibudidayakan dengan Sistem Akuaponik,” Akuatika Indonesia, vol. 3, no. 2, Art. no. 2, Sep. 2018, doi: 10.24198/jaki.v3i2.23392.
S. Hanifi, X. Liu, Z. Lin, and S. Lotfian, “A Critical Review of Wind Power Forecasting Methods—Past, Present and Future,” Energies, vol. 13, no. 15, Art. no. 15, Jan. 2020, doi: 10.3390/en13153764.
T. Nyoni, “Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe,” Jul. 2018, Accessed: Sep. 19, 2022. [Online]. Available: https://mpra.ub.uni-muenchen.de/87737/
“Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke Bali | Jurnal Varian”, Accessed: Sep. 19, 2022. [Online]. Available: https://journal.universitasbumigora.ac.id/index.php/Varian/article/view/668
S. S. Aruan, “Perbandingan Metode Arima dan Sarima Dalam Peramalan Penjualan Kelapa,” JAMI: Jurnal Ahli Muda Indonesia, vol. 2, no. 2, Art. no. 2, Dec. 2021, doi: 10.46510/jami.v2i2.82.
N. S. R. Ahmad, S. Martha, and N. Imro’ah, “PREDIKSI PRODUKSI KELAPA SAWIT DI PTPN XIII DENGAN ADDITIVE OUTLIER PADA MODEL SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA),” Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya, vol. 8, no. 4, Art. no. 4, Oct. 2019, doi: 10.26418/bbimst.v8i4.36562.
K. He, L. Ji, C. W. D. Wu, and K. F. G. Tso, “Using SARIMA–CNN–LSTM approach to forecast daily tourism demand,” Journal of Hospitality and Tourism Management, vol. 49, pp. 25–33, Dec. 2021, doi: 10.1016/j.jhtm.2021.08.022.
B. Y. Yohannes, S. W. Utomo, and H. Agustina, “Kajian Kualitas Air Sungai dan Upaya Pengendalian Pencemaran Air:,” IJEEM - Indonesian Journal of Environmental Education and Management, vol. 4, no. 2, Art. no. 2, Jul. 2019, doi: 10.21009/IJEEM.042.05.
W. H. Siegers and Y. Prayitno, “PENGARUH KUALITAS AIR TERHADAP PERTUMBUHAN IKAN NILA NIRWANA (Oreochromis sp.) PADA TAMBAK PAYAU,” vol. 3, no. 11, p. 10, 2019.
P. E. Wulandari, O. R. Pinontoan, and H. B. Boky, “KUALITAS AIR SUMUR BERDASARKAN PARAMETER FLUORIDA DAN PARAMETER PH DI KELURAHAN SUMOMPO KECAMATAN TUMINTING KOTA MANADO,” KESMAS, vol. 8, no. 6, Art. no. 6, 2019, Accessed: Oct. 18, 2022. [Online]. Available: https://ejournal.unsrat.ac.id/index.php/kesmas/article/view/25337
H. Harianti and N. Nurasia, “ANALISIS WARNA, SUHU, pH DAN SALINITAS AIR SUMUR BOR DI KOTA PALOPO,” Prosiding, vol. 2, no. 1, Art. no. 1, May 2016, Accessed: Oct. 18, 2022. [Online]. Available: http://journal.uncp.ac.id/index.php/proceding/article/view/520
H. Said, N. H. Matondang, and H. N. Irmanda, “Perancangan Sistem Prediksi Kualitas Air Yang Dapat Dikonsumsi Dengan Menerapkan Algoritma K-Nearest Neighbor,” Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya, vol. 3, no. 1, Art. no. 1, Oct. 2022.
A. I. Arafat, T. Akter, Md. F. Ahammed, Md. Y. Ali, and A.-A. Nahid, “A dataset for internet of things based fish farm monitoring and notification system,” Data in Brief, vol. 33, p. 106457, Dec. 2020, doi: 10.1016/j.dib.2020.106457.
Y. Muharni, K. Kulsum, and A. Denisa, “Prediksi Kualitas Air Baku dengan Pendekatan Adaptive Neuro Fuzzy Inference System,” Mar. 2018, Accessed: Oct. 18, 2022. [Online]. Available: http://publikasiilmiah.ums.ac.id/handle/11617/9832
H. Said, N. H. Matondang, and H. N. Irmanda, “Penerapan Algoritma K-Nearest Neighbor Untuk Memprediksi Kualitas Air Yang Dapat Dikonsumsi,” Techno.Com, vol. 21, no. 2, Art. no. 2, May 2022, doi: 10.33633/tc.v21i2.5901.
D. Ruhiat and A. Effendi, “PENGARUH FAKTOR MUSIMAN PADA PEMODELAN DERET WAKTU UNTUK PERAMALAN DEBIT SUNGAI DENGAN METODE SARIMA,” Teorema: Teori dan Riset Matematika, vol. 2, no. 2, Art. no. 2, Mar. 2018, doi: 10.25157/teorema.v2i2.1075.
E. Supriyadi, “PREDIKSI PARAMETER CUACA MENGGUNAKAN DEEP LEARNING LONG-SHORT TERM MEMORY (LSTM),” Jurnal Meteorologi dan Geofisika, vol. 21, no. 2, Art. no. 2, Jan. 2021, doi: 10.31172/jmg.v21i2.619.
S. K. Agus Sasmito Aribowo, “Text-Preprocessing Model Youtube Comments in Indonesian,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 4, pp. 648–654, Aug. 2020.
E. T. McGonigle, R. Killick, and M. A. Nunes, “Modelling time-varying first and second-order structure of time series via wavelets and differencing,” Electronic Journal of Statistics, vol. 16, no. 2, pp. 4398–4448, Jan. 2022, doi: 10.1214/22-EJS2044.
M. Tiyani and A. I. Achmad, “Uji Stasioneritas Dickey Fuller Generalized Least Square pada Data Inflasi Indonesia dari Bulan Januari 2006 Hingga Bulan Mei 2020,” Prosiding Statistika, vol. 6, no. 2, Art. no. 2, Aug. 2020, doi: 10.29313/.v6i2.22587.
E. A. Damanhuri, Y. I. Siregar, and E. Elfizar, “Penerapan Model Berbasis Artificial Neural Network Untuk Memprediksi Kualitas Air di Sungai Subayang Kabupaten Kampar,” Jurnal Ilmu Lingkungan, vol. 14, no. 1, Art. no. 1, Mar. 2020, doi: 10.31258/jil.14.1.p.18-28.
R. Rajan, S. Rajest, and B. Singh, “Spatial Data Mining Methods Databases and Statistics Point of Views,” in Innovations in Information and Communication Technology Series, 2021. doi: 10.46532/978-81-950008-7-6_010.
G. N. Ayuni and D. Fitrianah, “Penerapan Metode Regresi Linear Untuk Prediksi Penjualan Properti pada PT XYZ,” Jurnal Telematika, vol. 14, no. 2, Art. no. 2, 2019.
A. Khumaidi, R. Raafi’udin, and I. P. Solihin, “Pengujian Algoritma Long Short Term Memory untuk Prediksi Kualitas Udara dan Suhu Kota Bandung,” Jurnal Telematika, vol. 15, no. 1, Art. no. 1, Dec. 2020.
Susanto, “Model Perbandingan Metode Prediksi Jumlah Penjualan Produk Aplikasi HRIS Dengan Algoritma Forecasting Time Series Perusahaan SaaS,” Jurnal Sisfokom (Sistem Informasi dan Komputer, [Online]. Available: http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1387
