Judul: Development of a Water - Environment - Plant Identification Model for Rice Farming with Fine Bubble Application
Abstrak: Penelitian ini bertujuan untuk mengembangkan model identifikasi hubungan antara tinggi muka air, oksigen terlarut, evapotranspirasi tanaman dengan produktivitas padi menggunakan model Jaringan Saraf Tiruan (JST). JST digunakan untuk membantu memodelkan kompleksitas pengaruh faktor-faktor lingkungan tersebut sebagai input terhadap produktivitas padi sebagai output. Penelitian ini dilakukan di Kinjiro Farm, Bogor pada area lahan terbuka dengan menggunakan 4 perlakuan yaitu TA 1 dipasang fine buble generator dengan set point 0 – 7 cm, TA 2 menggunakan sistem irigasi permukaan dengan set point 4 – 7 cm, TA 3 menggunakan sistem irigasi bawah permukaan dengan set point -5 – 0 cm, dan TA 4 menggunakan sistem permukaan dengan set point 4 cm. Pemodelan JST menunjukkan terdapat hubungan yang kuat antara parameter input yaitu tinggi air, tinggi tanaman hari sebelumnya, oksigen terlarut, dan evapotranspirasi aktual dengan parameter output yaitu tinggi tanaman yang ditunjukkan melalui koefisien determinasi R2 bernilai 0,977. Hasil ini menunjukkan pemodelan JST dapat dijadikan acuan untuk melakukan pendugaan pertumbuhan tanaman berdasarkan faktor lingkungan dan air., This research aims to develop a model for identifying the relationship between water level, dissolved oxygen, plant evapotranspiration and rice productivity using the Artificial Neural Network (ANN) model. ANN is used to help model the complexity of the influence of these environmental factors as input on rice productivity as output. This research was conducted at Kinjiro Farm, Bogor in an open land area using 4 treatments, namely TA 1 installed fine buble generator with set point 0 – 7 cm, TA 2 uses a surface irrigation system with set point 4 – 7 cm, TA 3 uses a subsurface irrigation system with set point -5 – 0 cm, and TA 4 uses a surface system with set point 4 cm. ANN modeling shows that there is a strong relationship between the parameters input namely water height, plant height the previous day, dissolved oxygen, and actual evapotranspiration with parameters output namely plant height which is shown through the coefficient of determination R2 worth 0.977. These results show that ANN modeling can be used as a reference for estimating plant growth based on environmental and water factors.
Keyword: Jaringan Saraf Tiruan, Artificial Neural Network, fine bubble technology, Produktivitas Padi, Sistem Irigasi, Irrigation System, Rice Productitivity

Judul: Pengembangan Model Optimasi Sistem Irigasi untuk Budidaya Padi Sawah dengan Perlakuan Fine Bubble Technology
Abstrak: Dalam usaha peningkatan produksi beras melalui budidaya padi sawah perlu memperhatikan aspek lingkungan dan penggunaan air. Ketersediaan oksigen di air membantu penyerapan unsur hara oleh tanaman. Penggunaan fine bubble technology merupakan solusi alternatif pengayaan oksigen dalam air untuk tanaman. Saat ini penggunaan air dengan pengayaan oksigen terlarut di air irigasi belum banyak dilakukan. Penelitian ini bertujuan melakukan optimasi penggunaan air dengan menentukan tinggi muka air optimum termasuk dengan fine bubble technology menggunakan Algoritma Genetika (AG). Pengamatan budidaya padi dilakukan pada bulan Februari hingga Juni 2024 di Kinjiro Farm Kota Bogor. Terdapat empat treatment (TA) dengan 2 sistem irigasi yang berbeda, yaitu irigasi permukaan dan bawah permukaan. TA 1 memiliki tinggi air 7 cm dan dilengkapi dengan fine bubble, TA 2 memiliki tinggi air 7 cm, TA 3 memiliki tinggi air -5 cm, dan TA 4 memiliki tinggi air 4 cm. Fine bubble technology yang diberikan pada sistem irigasi bawah permukaan mampu memperkaya oksigen di dalam air yang bermanfaat untuk proses respirasi tanaman. Proses optimasi AG menghasilkan bobot gabah optimal tanaman padi yaitu 7,40 ton/ha dengan ketinggian air optimal 3,5 cm dari permukaan tanah., In efforts to increase rice production through paddy cultivation, environmental aspects and water usage must be considered. Oxygen availability in water aids nutrient absorption by plants. Fine bubble technology (FBT) presents an alternative solution for enriching oxygen in irrigation water. Currently, oxygen- enriched irrigation water usage is limited. This study aims to optimize water usage by determining the optimal water level, including the use of FBT, using a Genetic Algorithm (GA). Rice cultivation observations were conducted from February to June 2024 at Kinjiro Farm, Bogor City. Four treatments (TA) with two different irrigation systems, surface and subsurface irrigation, were tested. TA 1 had a water level of 7 cm with fine bubbles, TA 2 had a water level of 7 cm, TA 3 had a water level of -5 cm, and TA 4 had a water level of 4 cm. FBT in subsurface irrigation enriched water oxygen content, beneficial for plant respiration. GA optimization resulted in an optimal rice yield of 7.40 tons/ha with an optimal water level of 3.5 cm above the soil surface.
Keyword: Identifikasi, Sistem Irigasi, Optimasi, padi, Algortima Genetika (AG), genetic algorithm (GA)

Judul: Distribution and Diversity of Ectoparasite Mites on Geckos in Indonesia
Abstrak: Data on the diversity and dispersal of parasitic mites on house geckos in Indonesia are very scarce. In this work, the distribution and diversity of mites living on three species of house geckos, namely Cosymbotus platyurus, Hemidactylus frenatus, and H. garnotii collected throughout Indonesia, has been elaborated. Geckos and mites were captured and immediately preserved in 70% ethanol. Whole mount of the mites was prepared by clearing in lactophenol followed by mounting on polyvinyl lactophenol solutions. The SEM preparation was conducted to examine the detail morphological characters of the mites. The results showed that among 448 individuals of geckos, 221 geckos were infected by Geckobia mites. Prevalences of C. platyurus, H. frenatus, and H. garnotii infested by mites were 14.8%, 50.69%, and 79.6%, respectively. Three different spesies of Geckobia (G1, G2, and G3) could be differentiated; and based on similarities of their morphological characters to ones described in published literatures, Geckobia G2 is Geckobia glebosum and Geckobia G3 is Geckobia bataviensis while G1 could not be identified to the species level. The highest mean intensity of Geckobia G1 infestation was found on H. garnotii (I=7.0), G. glebosum infestation on H. frenatus (I=3.5), and G. bataviensis infestation on H. garnotii (I=11.8). In general, C. platyurus was infested by the least number of mites. Geckobia G1 were found living on the skin folds on the claws, G. glebosum were found mainly on the body and thigh, and G. bataviensis were found on almost all parts of the host’s body. Geckobia mites are distributed randomly throughout Indonesian Archipelago, following the pattern of distribution of their hosts. So it is concluded that Geckobia G1, G. glebosum, and G. bataviensis are sympatric.
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