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"Tugas akhir ini merupakan penelitian yang ditujukan untuk mengembangkan model dinamik pertumbuhan tanaman dengan metode Artificial Neural Network (ANN), dimana model ini memetakan hubungan antara input (massa tanaman sebelum, nutrisi, usia, serta lingkungan) dan output (pertambahan massa tanaman periode berikutnya). Metode ini dipilih berdasar pertimbangan bahwa tanaman bisa dilihat sebagai satu sistem, dimana sistem ini cukup rumit karena bersifat dinamik, non-linear, dan time-variant. Penelitian yang akan dilakukan meliputi penanaman tanaman dengan metode deep water culture (DWC), pengambilan data tanaman dan lingkungan baik secara manual atau dengan sensor yang dikirim ke server, dan pelatihan ANN untuk menemukan model yang paling tepat. Data-data yang diambil selanjutnya diolah dan dipilah menjadi data pelatihan dan validasi. Data-data pelatihan dikumpulkan dalam database yang terdiri dari input dan output yang digunakan untuk melatih model. Terdapat beberapa model yang memiliki variasi gaya, arsitektur, dan kedalaman pelatihan (skor cost). Hasil akhir menunjukkan bahwa pemodelan pertumbuhan tanaman dengan ANN dapat dilakukan dan memiliki performa yang lebih baik daripada dengan pendekatan persamaan linear. Performa terbaik ditunjukkan oleh arsitektur residual dua sisi dengan rerata error mutlak 7.7634%.

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This final project is a research aimed at developing a dynamic model of plant growth using the Artificial Neural Network (ANN) method, where this model maps the relationship between inputs (prior plant mass, nutrition, age, and environment) and output (increase in plant mass for the next period) . This method was chosen based on the consideration that plants can be seen as a system, where the system is quite complicated because it is dynamic, non-linear, and time-variant. The research that will be carried out includes planting plants with a deep water culture (DWC) method, taking plant and environmental data either manually or with sensors sent to the server, and ANN training to find the most appropriate model.

The data taken is then processed and sorted into training and validation data. Training data is collected in a database consisting of inputs and outputs used to train the model. There are several models that have variations in style, architecture, and depth of training (cost score). The final results show that modeling of plant growth with ANN can be done and has better performance than the linear equation approach. The best performance is shown by the two-sided residual architecture with an average absolute error of 7.7634%."

"The study was aimed to determine a proper time of measuring soybean nitrate reductase activities, and to what extend direct and indirect effect of those traits to the soybean yield and its component.The experiment was arranged in Randomized Completely Block Design with three blocks as replications. Six soybean varieties: Burangrang, Wilis, Tanggamus, Cikuray, Mallika, and Gamallika were used. In-vivo nitrate reductase activities were measured at seedling stage (14 dap), when root nodule start to be active (25 dap), maximum growth stage (34 dap), flowering stage (42 dap), and grain filling period (63 dap). Correlation and path analysis was applied to the data collected to determine direct and indirect effect of nitrate reductase activity, growth and yield components to the soybean yield.The results showed that the proper time of measuring nitrate reductase activity was at 42 dap (flowering stage). Large direct effect to grain yield per plant was indicated by seed number per plant, total dry weight, 3-seeded pods per plant and nitrate reductase activity total leaf fresh weight at 42 dap; meanwhile, number of filled pods per plant, number of branch per plant, and number of productive node per plant had a negative direct effect but the indirect effect was larger through seed number. It indicated that the seed number per plant, total dry weight, 3-seeded pods per plant and nitrate reductase activity total leaf fresh weight at 42 dap may be used for soybean yield selection."

"Soil fungi occurred in the rhizosphere in many cultivated crops and wild grasses in the sandy soil were effective in enhancing the growth promoting of plant. A field experiment to study the effect of soil fungi from sandy soil as plant growth promoting fungi (PGPF). The experiment was conducted in Clinical Laboratory of Plant Pathology and glass house Faculty of Agriculture Gadjah Mada University in Yogyakarta. The sandy soil was located in Samas and Bugel Yogyakarta. The treatment was conducted in Complete Randomize Design (CRD) with 5 replications. Among 33 rhizosphere fungi tested, 26 fungal isolates were hypovirulent isolate and showed the ability as PGPF i.e. Trichoderma spp. (SB32 , CB32, SB33, CB33 and CB21). They enchanced significantly the height and dry biomass of plants compare with control."

"Plants use resources, i.e. carbon, nutrients, water and energy, either for growth or to defend themselves from biotic and abiotic stresses. This volume provides a timely understanding of resource allocation and its regulation in plants, linking the molecular with biochemical and physiological-level processes. Ecological scenarios covered include competitors, pathogens, herbivores, mycorrhizae, soil microorganisms, carbon dioxide/ozone regimes, nitrogen and light availabilities. The validity of the “Growth-differentiation balance hypothesis” is examined and novel theoretical concepts and approaches to modelling plant resource allocation are discussed. "

"Lidah buaya (Aloe vera) is an Indonesian plant used as herbal medicine.The aim of this study was to to identity the enzymatic antioxidant activity and its potency as an antihaemolytic....."