Judul: IPB (Bogor Agricultural University)
Abstrak: One of the large rivers flowing in the center of Semarang City is Garang River with watershed area about 203 km2 and having characteristics such as high flood discharge and flash flood. The flash flood on January 25th 1990 caused more than 45 people died and goods loss was estimated to be 8.5 billion rupiahs. In this research, a flood early warning system is made, in which an automatic rainfall recorder (ARR) was placed in upstream of Garang River at sub district Gunungpati and an automatic water level recorder (AWLR) was placed in Simongan weir. To receive rainfall and water level data, a computer as server that is placed in Semarang government office. This also functions for sending flood status of Garang River (prepared/caution/careful) to flood attendants and stake holders. Beside that, artificial neural network (ANN) is used to predict water level in Garang River where the input are rainfall in upstream river for 1, 2, 3, 4 or 5 days before the occasion and the water level in the downstream for 3 hours before the occasion. The output is water level in downstream of Garang River for next 2 hours. This system is integrated with information technology that is SMS (Short Message Services) and Web so that the flood early warning can be accessed anywhere as long as communication network is available. The result of research, time for sending data of rainfall and water level telemetry system is less than 10 minutes, while information system that is built to give flood early warning information is less than 10 minutes. Consequently it fulfill in which the time to receive the information less than time when flood happened that is 2 hours. The result of optimum predicting during the ANN training is model 4 with 20 neurons; speed training 0.9 and momentum 0.3 which the input rainfall are four days before the occasion and the water level are 3 hours before the occasion having MSE 0.0046. Finally, the results of survey to flood attendants and staffs from government show that 86 % of the respondent that they absolutely need the flood early warning system.
Keyword: Automatic Rainfall Recorder (ARR), Artificial Neural Network (ANN), Flood Early Warning, Automatic Water Level Recorder (AWLR)

Judul: Penerapan Jaringan Saraf Tiruan untuk Pemodelan Prakiraan Curah Hujan Bulanan
Abstrak: Rain is very usefull for life but heavy rainfall will lead to disaster if not anticipated properly. Rainfall forecasting is very usefull to anticipate the occurrence of extreme weather. This study developed artificial neural network models for the next three months rainfall forecasting. The data used in this study was the climate data from 1983 to 2013. The climate data used as model input were rainfall, temperature and relative humadity (RH). Temperature and RH averaged on a month period and observed three times a day. The best result from this study was the model with the highest correlation value. The Correlation values of the best model for rainfall forecasting of the next three months were 0.78, 0.83, 0.77 respectively.
Keyword: Bogor Agricultural University (IPB), temperature, RH, rainfall, ANN, forecast

Judul: Penentuan Nilai Eigen dan Vektor Eigen dari Matriks Tridiagonal 2-Toeplitz dengan Pendekatan Polinomial Chebyshev
Abstrak: The eigenvalues and eigenvectors of a matrix can be determined by finding its characteristic polynomials. The characteristic polynomials of a tridiagonal 2-Toeplitz matrix is shown to be closely connected to polynomials which satisfy the Chebyshev recurrence relationship. If the order of the matrix is odd, then the eigenvalues are found explicitly in terms of the Chebyshev zeros and the eigenvectors are found in terms of the polynomials satisfying the recurrence relationship. For even ordered matrices, the situation is more complicated. The problem in these cases is that although the Chebyshev recurrence formula is still applied, its initial values are not generating Chebyshev polynomials
Keyword: