Judul: Prediksi Panjang Musim Hujan Menggunakan Time Delay Neural Network
Abstrak: Indonesia has abundant natural resources in agriculture. Good agricultural results can be obtained by determining a good growing season plan. One of important factors which determines the successful of crop is the length of the rainy season. The length of the rainy season is dynamic and difficult to be controlled. Therefore the planning of the growing season becomes inaccurate and cause crop failures. This research aims to develop a model to predict the length of the rainy season using time delay neural network (TDNN). Observational data used in this research is the length of rainy season from three weather and climate stations of the Pacitan region from 1982/1983 to 2011/2012. Predictor data used in this reserach is sea surface temperature (SST) derived from the region of Nino 1+2, Nino 3, Nino 4, and Nino 3.4 from 1982 to 2011. Model with the best accuracy was obtained by Pringkuku station with RMSE of 1.97 with parameters of delay [0 1 2 3], learning rate 0.1, 40 hidden neurons, and predictors of Nino 3 and R-squared of 0.82 with parameters of delay [0 1], learning rate 0.3, 5 hidden neurons, and predictors of Nino 3.
Keyword: SST, the length of the rainy season, TDNN

Judul: Prediksi Panjang Musim Hujan Menggunakan Cascade Neural Network
Abstrak: In Indonesia, agriculture becomes an important sector for national development and national economy. The length of rainy season is one of the rainfall variables that affect agricultural production. The changing of the rainy season length can impact on crop failures. This research aims to develop a model for predicting the length of rainy season using Cascade Neural Network (CNN). Predictor data used in this research is sea surface temperature from region of NINO 1+2, NINO 3, NINO 4, dan NINO 3.4 from 1982 to 2011. Observational data used is the length of rainy season of Pacitan region in Arjosari, Kebon Agung, and Pringkuku weather stations from 1982/1983 to 2011/2012. This research obtained the best model from Pringkuku weather station with R2 of 0.72 and RMSE of 1.87 using a learning rate of 0.3, 10 hidden neurons and NINO 4 region.
Keyword: sea surface temperature, the length of rainy season, neural network, cascade neural network

Judul: Sintesis Flavon dari Fenol dan Benzoil Klorida
Abstrak: Flavone is a class of flavonoid, commonly found in plants with wide bioactivity spectrum. A widely used flavone synthesis route is through acidcatalyzed oxidative cyclization of β-diketone. In this study, unsubstituted flavone had been synthesized via 5-step from phenol, with 22% yield and successfully increased the yield of previous study on Fries rearrangement of phenyl acetate and benzoylation of o-HAP steps. Phenol was acetylated resulting 93% phenyl acetate. AlCl3-catalyzed Fries rearrangement in 1.5 hour and at 120 °C converted phenyl acetate into o-hydroxyacetophenone (o-HAP) with 40% yield. AlCl3 as catalyst produced o-HAP with higher and reproducible yield compared with BF3·Et2O and ZnCl2. Benzoylation of o-HAP followed by Baker-Venkataraman rearrangement of the ester product using KOH in dried pyridine produced β-diketone 1-(2- hydroxyphenyl)-3-phenylpropane-1,3-dione. The yield of ester and β-diketone were 71% and 85%, respectively. Finally, oxidative cyclization of β-diketone with sulphuric acid as catalyst produced 99% of flavone. The flavone product had been characterized spectroscopically and its melting point as well.
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