PERAMALAN PRODUKSI AIR BERSIH OLEH PERUSAHAAN DAERAH AIR MINUM BATULANTEH KABUPATEN SUMBAWA MENGGUNAKAN METODE REGRESI
Keywords:
Forecasting, Linear Regression, Quadratic Regression, Cubic Regression, PDAMAbstract
Water is one of the natural resources which is a basic human need that cannot
separated every day, therefore good quality water that is suitable for use is needed. Company
Drinking Water (PDAM) Sumbawa Besar is the only company that supplies clean water
for the needs of residents of the city of Sumbawa Besar and its surroundings. One of the obstacles for the company
in meeting the need for clean water is the limited debit of the available water supply considering that
The city of Sumbawa Besar is categorized as a drought area. Therefore it is necessary to plan
to manage the available water resources. Based on this, the researchers provide
the future use of clean water produced by PDAM is useful
become material for community planning in using groundwater. In this study, the method
used to determine or predict the use of clean water produced by PDAM
Bantulante is a regression method, namely the linear regression method, the cudratic regression method or the method
cubic regression. Determination of the best method of the three methods is determined by the value of R square
The largest, from the analysis using SPSS, it is found that the cubic regression method has an R square value
the largest compared to the other two methods, where the R square value is 0.572. Forecasting
Using the selected method, the highest water usage was obtained in January, namely
as much as 402,908.60 m3
and the lowest was in December, namely 354,747.60 m3
. Where is the level
the accuracy of the forecasting value is 99.99%.
References
Firmanila, U. D. (2016). Keterkaitan
Karakteristik Wilayah Terhadap
Distribusi Air Bersih Di Perkotaan
Sumbawa Besar [Institut Teknologi
Sepuluh November].
http://repository.its.ac.id/48733/1/3610
-Undergraduate-Theses.pdf
Halin, H., Wijaya, H., & Yusilpi, R. (2017).
Pengaruh Harga Jual Kaca Patri Jenis
Silver Terhadap Nilai Penjualan Pada
Cv. Karunia Kaca Palembang Tahun
-2015. Jurnal Ecoment Global,
(2), 49.
https://doi.org/10.35908/jeg.v2i2.251
Hermanto, K., & Rizqika, F. (2019). Metode
Regresi yang Tepat Untuk Meramalkan
Permintaan Minyak Solar di
Kabupaten Sumbawa. Unisda Journal
of Mathematics and Computer Science,
(1), 17–24.
Hermanto, K., & Utami, S. F. (2019). Peramalan
Kebutuhan Air Untuk Penyiapan Lahan
Menggunakan Metode Siklis (Studi
Kasus Daerah Irigasi Bendungan Batu
Bulan Kec. Moyo Hulu). Unisda
Journal of Mathematics and
Computational Science (UJMC), 5(1),
–34. http://ejurnal.unisda.ac.id/index.php/ujmc/articl
e/view/1480
Hermawan, D. D., Widada, B., & Vulandari, R.
T. (2018). Perbandingan Hasil Panen
Padi Dipengaruhi Rata-Rata Curah
Hujan Atau Irigasi Dengan Model
Regresi Nonlinier Kubik Dikabupaten
Sukoharjo. Jurnal Teknologi Informasi
Dan Komunikasi (TIKomSiN), 6(1), 6–
https://doi.org/10.30646/tikomsin.v6i1.3
Hudaningsih, N., Utami, S. F., & Jabbar, W. A.
A. (2020). Perbandingan Peramalan
Penjualan Produk Aknil PT. Sunthi
Sepuri Menggunakan Metode Single
Moving Average dan Single Exponential
Smoothing. Jinteks, 2(1), 15–22.
Nafidah, Q. A., Astutik, S., Si, S., Si, M.,
Wuryantini, S., Statistika, J., Brawijaya,
U., Tanaman, P., Penelitian, B., & Jeruk,
T. (2020). Penerapan Analisis Regresi
Nonlinear Kuadratik Terhadap
Pengujian Toksisitas ( LD 50 )
Biopestisida Crude Extract Tembakau
Pada Kutu Daun Hijau ( Aphis Gossypii
). Prosiding Seminar Nasional Inegrasi
Matematika Dan Nilai Islmi, 3(1), 430–
Rozi, F., & Sukmana, F. (2016). Metode Siklis
Dan Adaptive Neuro Fuzzy Inference
System Untuk Peramalan Cuaca. JIPI
(Jurnal Ilmiah Penelitian Dan
Pembelajaran Informatika), 1(1), 7–13.
https://doi.org/10.29100/jipi.v1i01.20
Said, N. I., & Widayat, W. (2000).
Pemasyarakatan Unit Pengolahan Air
Siap Minum. Teknologi Lingkungan,
(3), 233–246.
Santosa, R. G. (2013). Aljabar Linear Dasar. In J.
Widiyatmoko (Ed.), ANDI (1st ed.).
Utami, S. F., Arisma, S. Y., Hermanto, K., &
Ruskartina, E. (2020). Peramalan
Jumlah Penjualan Sepeda Motor Menggunakan Metode Time Series
Studi Kasus?: Dealer Motor Nusantara
Surya Sakti (NSS) Sumbawa. Hexagon,
(2), 33–41.