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: Pengaruh Asam Azelat Pada Bio Gemuk Litium Kompleks Berbasis Minyak Kelapa Sawit (CPO) Terepoksidasi
Abstrak: Gemuk merupakan pelumas semipadat dengan viskositas tinggi, berupa koloid padat-cair yang terbuat dari minyak dasar dan bahan pengental. Gemuk litium kompleks merupakan gemuk yang umum beredar di pasaran. Gemuk litium kompleks adalah jenis gemuk yang menggunakan kompleks sabun litium sebagai pengental utama. Penelitian ini bertujuan agar didapatkan komposisi produk gemuk bio litium kompleks yang mempunyai kualitas lebih baik dan didapatkan hubungan antara penambahan agen pengompleks terhadap penampakan visual dan tekstur. Proses pembuatan gemuk terdiri dari epoksidasi CPO, saponifikasi dan homogenasi. Kinerja gemuk yang dihasilkan dapat dilihat melalui uji dropping point. Hasil penelitian menunjukan bahwa gemuk bio litium kompleks dengan dropping point tertinggi sebesar 207 derajat celcius pada penambahan asam azelat sebanyak 2,5%.
Keyword: Dropping point, Epoksidasi CPO, Gemuk, Saponifikasi