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: 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: Correlation between antioxidant activity of synthetic zeolites pillared titanium dioxide and iron (III) oxide with adsorption DPPH
Abstrak: Penyakit degeneraif akibat berlebihnya radikal bebas di dalam tubuh merupakan penyebab kematian tertinggi di dunia. Radikal bebas dalam tubuh mengakibatkan penuaan dini dengan merusak jaringan lemak yangberada di bawah kulit sehingga menghilangkan kekencangan kulit dan kulit menjadi keriput.Salah satu cara untuk menanggulangikelebihan radikal bebas adalah dengan menambahkan antioksidan alami pada produk farmasi dan bahan pangan. Zeolit merupakan salah satu material yang berpotensi sebagai antioksidan. Zeolit bekerja sebagai antioksidan dengan cara memerangkap radikal bebas dalam strukturnya sehingga radika bebas menajdi tidak aktif dan aman dihilangkan dari tubuh. Kinerja zeolit sebagai antioksidan dapat ditingkatkan dengan memilar zeolit dengan oksida logam misalnya TiO₂ atau Fe₂O₃.
Keyword: DPPH, Fe2O3, Methylene Blue, Synthetic zeolite type A and TiO2