Prediksi Kebutuhan Air Bersih Di Surabaya Menggunakan Metode Time – Series Neural Network

Pakpahan, Nicholas Emanuel Gavin (2023) Prediksi Kebutuhan Air Bersih Di Surabaya Menggunakan Metode Time – Series Neural Network. Diploma thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Prasarana memegang peranan yang sangat penting dalam pertumbuhan dan perkembangan suatu wilayah, pola pertumbuhan dan prospek pengembangan ekonomi wilayah tersebut, dalam hal ini air merupakan suatu hal yang penting dan mendapat prioritas dalam perencanaan kota. Aktivitas manusia sehari – hari tidak pernah lepas dari air, khususnya air bersih. Kebutuhan air bersih tiap tahun pada umumnya senantiasa mengalami peningkatan, akan tetapi di sisi lain ketersediaan air bersih semakin terbatas karena sempitnya area resapan. Di Pulau Jawa pada tahun 2003, telah terjadi kekeringan di 12 kabupaten di Jawa Barat, 6 kabupaten di Jawa Tengah, dan 2 kabupaten di Jawa Timur. Pulau Jawa sendiri tergolong pulau yang kritis air, di mana setiap penduduk di Pulau Jawa hanya dapat dipenuhi kebutuhan airnya sebesar 1750 meter kubik. Nilai ini di bawah standar yang harusnya 2000 meter kubik per kapita per tahun. Jika dilihat dari komposisi dan beban kebutuhan yang harus disediakan, maka Pulau Jawa yang hanya 7% dari total luas daratan di Indonesia, namun dihuni 65% penduduk Indonesia, bisa mengalami kekurangan air. Hal ini dikarenakan kebutuhan air di Pulau Jawa setidaknya membutuhkan 45% - 55% dari sumber daya air, sementara potensi sumber daya air di Pulau Jawa saat ini hanya tersedia 4,5% dari total potensi SDA. Jika terus berlanjut, diprediksi terjadi kelangkaan air pada tahun 2040. Oleh karena itu diperlukan prediksi dan perencanaan dengan pemanfaatan sebaik mungkin. Surabaya sebagai target penelitian adalah kota terbesar kedua di Indonesia setelah Jakarta. Berdasarkan data SUSENAS (Survei Sosial dan Ekonomi Nasional) 2011, konsumsi air yang berasal langsung dari ledeng meteran air PDAM (Perusahaan Daerah Air Minum) adalah sebesar 70% disusul dengan pemanfaatan air tanah pada sumur terlindungi, yang dipengaruhi oleh curah hujan. Berdasarkan permasalahan ini, pemanfaatan metode Machine Learning Time-Series Neural Network digunakan dan diteliti untuk memprediksi angka kebutuhan air masa depan di Surabaya. Hasil prediksi menunjukkan bahwa pada tahun 2040, PDAM harus memenuhi kebutuhan air sebesar 625.253.727 meter kubik untuk 1.457.326 pelanggannya, atau 429.000 liter air per-pelanggan. Hasil penelitian juga menunjukkan bahwa ketika dibandingkan dengan metode regresi linier, metode Time-Series Neural Network memiliki MSE lebih baik.
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Infrastructure plays a very important role in the growth and development of a region, growth patterns and prospects for the economic development of the region, in this case water is an important matter and gets priority in urban planning. Daily human activities are never separated from water, especially clean water. The need for clean water every year generally continues to increase, but on the other hand the availability of clean water is increasingly limited due to the narrow catchment area. On the island of Java in 2003, there were droughts in 12 regencies in West Java, 6 regencies in Central Java and 2 regencies in East Java. Java Island itself is classified as a water-critical island, where each resident on the island of Java can only meet their water needs of 1750 cubic meters. This value is below the standard which should be 2000 cubic meters per capita per year. When viewed from the composition and burden of needs that must be provided, the island of Java, which is only 7% of the total land area in Indonesia, but is inhabited by 65% of Indonesia's population, could experience water shortages. This is because water needs on the island of Java require at least 45% -55% of water resources, while the potential for water resources on the island of Java is currently only 4.5% of the total potential of natural resources. If it continues, it is predicted that there will be water scarcity in 2040. Therefore, predictions and planning are needed with the best possible use. Surabaya as the research target is the second largest city in Indonesia after Jakarta. Based on Susenas data (National Social and economic Survey) 2011, the consumption of water that comes directly from PDAM (Regional Water Supply Company) taps is 70% followed by the use of groundwater in protected wells, which is affected by rainfall. Based on this problem, the utilization of the Machine Learning Time-Series Neural Network method is used and researched to predict future water demand in Surabaya. The prediction results show that in 2040, PDAM must meet the water needs of 625,253,727 cubic meters for 1,457,326 customers, or 429,000 liters of water per customer. The results also show that when compared with the linear regression method, the Time-Series Neural Network method has a better MSE.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Kebutuhan Air, Machine Learning, Time-Series Neural Network, Water Needs
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. R Medicine > R Medicine (General) > R858 Deep Learning T Technology > TD Environmental technology. Sanitary engineering > TD233 Water consumption
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	Nicholas Emanuel Gavin Pakpahan
Date Deposited:	07 Sep 2023 03:42
Last Modified:	07 Sep 2023 03:42
URI:	http://repository.its.ac.id/id/eprint/104184

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