Journal SINTA 2
Pemodelan Regresi Non Linear Menggunakan Algoritma Genetika Untuk Prediksi Kebutuhan Air PDAM Kota Malang
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 3 No. 1 (2016) · pp. 59–65
- ISSN
- 2528-6579, 2355-7699
- Published
- 2016-03-17
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- en
Abstract
AbstrakSeiring dengan meningkatnya jumlah penduduk di Kota Malang maka meningkat pula kebutuhan konsumen air bersih dari PDAM. Perubahan pemakaian air tersebut jika tidak diolah dengan baik maka akan menyebabkan beberapa persoalan diantaranya apabila PDAM terlalu banyak mendistribusikan air bersih ke konsumen maka akan berakibat pemborosan air dan sebaliknya apabila distribusi air bersih PDAM kurang maka konsumen akan kekurangan air bersih. Oleh karena itu dibutuhkan suatu estimasi untuk memperkirakan dengan tepat seberapa besar volume air yang diperlukan di tahun-tahun berikutnya. Permasalahan tersebut dimodelkan dengan persamaan regresi non linear yang terdiri dari variabel bebas (X) dan variabel terikat (Y). Algoritma Genetika digunakan untuk memilih variabel mana saja yang perlu dilibatkan dalam persamaan regresi. Proses reproduksi menggunakan one-point-crossover dan random mutation, untuk proses seleksinya menggunakan model elitism selection. Dari uji coba didapatkan parameter terbaik yaitu ukuran populasi sebanyak 225, generasi terbaik sebanyak 1750 generasi, kombinasi cr: mr adalah 0,6: 0,4 dengan nilai fitness tertinggi yaitu 107.997. Hasil akhir berupa model regresi dengan melibatkan sesedikit mungkin variable bebas dan mean square error (MSE) terkecil..Kata kunci: Regresi Non Linear, Algoritma Genetika, Prediksi, Pemakaian air PDAM AbstractAlong with the increasing population in Malang the consumer water consumption from PDAM also increase. The change of water consumption if it is not treated properly, it will cause some problems when the PDAM has too many of water to distribute to consumers it will result in wastage of water and otherwise if the distribution of water less than normal, then the consumer will get a shortage of water. Therefore it is necessary to estimate for predict exactly how much the water volume needed in subsequent years. This problem will be modeled with non linear regression that consist of the independent variable (X) and the dependent variable (Y). Genetic Algorithm is applied to determine which variables are involved in the regression model. The reproduction process uses one-point-crossover and random mutation, for the selection process uses a elitism selection models. The numerical experiment obtains the best population size is 225, the best generation as much as 1750 generation, combination of cr: mr is 06: 0.4 with the highest fitness value is 107.997. The final result is a regression model that involves less independent variabels with minimum mean square error (MSE).Keywords: Non Linear Regression, Genetic Algorithm, Predict, Water Consumption
Collaboration network of the authors
40 authors · 68 co-authorship links · 10 across different organisations · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- State University of Malang
- Universitas Merdeka Malang
- Institut Teknologi Sepuluh Nopember
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Yusuf Priyo Anggodo | University of Brawijaya | 3 |
| Asyrofa Rahmi | University of Brawijaya | 2 |
| Syaiful Anam | University of Brawijaya | 2 |
| Adyan Nur Alfiyatin | University of Brawijaya | 2 |
| Firnanda Al Islama Achyunda Putra | University of Brawijaya | 1 |
| Fitri Utaminingrum | University of Brawijaya | 1 |
| Ida Wahyuni | University of Brawijaya | 1 |
| Ervin Yohannes | University of Brawijaya | 1 |
| Hilman Nuril Hadi | University of Brawijaya | 1 |
| Rizal Setya Perdana | University of Brawijaya | 1 |
| Ghenniy Rachmansyah | University of Brawijaya | 1 |
| Ria Febriyana | University of Brawijaya | 1 |
| Luthfi Hidayat | University of Brawijaya | 1 |
| Rifki Setya Armanda | University of Brawijaya | 1 |
| Garsinia Ely Riani | University of Brawijaya | 1 |
| Fita Lathifatul Mu’asyaroh | Affiliation unknown | 1 |
| Agus Wahyu Widodo | University of Brawijaya | 1 |
| Diva Kurnianingtyas | University of Brawijaya | 1 |
| Muchammad Chandra Cahyo Utomo | University of Brawijaya | 1 |
| Candra Fajri Ananda | University of Brawijaya | 1 |
| Farhanna Mar’i | University of Brawijaya | 1 |
| Cleoputri Yusainy | University of Brawijaya | 1 |
| Qoirul Kotimah | University of Brawijaya | 1 |
| Vivi Nur Wijayaningrum | University of Brawijaya | 1 |
| Gayatri Dwi Santika | University of Brawijaya | 1 |
| Agus Naba | University of Brawijaya | 1 |
| Aji Prasetya Wibawa | State University of Malang | 1 |
| Nadia Roosmalita Sari | University of Brawijaya | 1 |
| Elta Sonalitha | Universitas Merdeka Malang | 1 |
| Gusti Ahmad Fanshuri Alfarisy | University of Brawijaya | 1 |
| Muhammad Halim Natsir | University of Brawijaya | 1 |
| Agung Mustika Rizki | Institut Teknologi Sepuluh Nopember | 1 |
| Gusti Eka Yuliastuti | University of Brawijaya | 1 |
| Andi Hamdianah | University of Brawijaya | 1 |
| Hilman Taufiq | University of Brawijaya | 1 |
| Aprilia Nur Fauziyah | University of Brawijaya | 1 |
| Amalia Kartika Ariyani | University of Brawijaya | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.