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      Optimasi Sistem Irigasi Evapotranspiratif untuk Budidaya Padi dengan Fine Bubble Technology

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      Date
      2025
      Author
      Purnomo, Kayla Azzahra Putri
      Arif, Chusnul
      Purwanto, Yohanes Aris
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      Abstract
      Peningkatan efisiensi penggunaan air pada sistem irigasi menjadi sangat penting agar ketahanan pangan dapat terus tejaga. Fine bubble technology merupakan solusi yang mampu meningkatkan oksigen terlarut dalam air irigasi. Akan tetapi, belum adanya optimasi penggunaan air dan pengayaan oksigen tersebut. Penelitian ini bertujuan melakukan optimasi penggunaan air untuk menentukan tinggi muka air optimum dengan Fine bubble technology menggunakan Jaringan saraf tiruan (JST) dan Algoritma genetika (AG). Empat skenario diuji dengan dua sistem irigasi yang berbeda, yaitu irigasi permukaan dan bawah permukaan.dalam penelitian ini: SA 1 dan SA 2 menggunakan FBT dengan tinggi muka air 7 cm, sedangkan SA 3 dan SA 4 tanpa FBT dengan tinggi muka air masing-masing 0- -5 cm dan 2 cm. Hasil pengamatan menunjukkan bahwa FBT mampu meningkatkan kadar DO dalam air irigasi yang mampu meningkatkan produktivitas tanaman padi. Pemodelan dengan JST menghasilkan koefisien determinasi (R²) sebesar 1, menandakan tingkat akurasi yang sangat baik. Optimasi menggunakan AG menunjukkan bahwa produksi gabah maksimum sebesar 961,46 g/m² dicapai pada tinggi muka air optimal 7,8 cm dari permukaan tanah untuk fase awal.
       
      Increasing water use efficiency in irrigation systems is very important so that food security can be maintained. Using fine bubble technology (FBT) to enhance dissolved oxygen (DO) in water, supported by artificial neural networks (ANN) and genetic algorithms (AG). Four treatment scenarios were tested: SA 1 and SA 2 applied FBT with a 7 cm water table, while SA 3 and SA 4 did not use FBT, with water tables of 0–5 cm and 2 cm respectively. Two irrigation methods such as surface and subsurface were used across these scenarios. Results showed that FBT significantly increased DO levels, which in turn boosted rice plant productivity. The ANN model achieved a coefficient of determination (R²) of 1, indicating highly accurate predictions. Optimization using AG showed that the maximum grain yield of 961,46 g/m² was achieved at an optimal water table of 7.8 cm from the soil surface for beginning.
       
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      http://repository.ipb.ac.id/handle/123456789/164949
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      • UT - Civil and Environmental Engineering [1042]

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      Indonesia DSpace Group 
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