Judul: Pendugaan Curah Hujan Dengan Bayesian Network Studi Kasus: Curah Hujan di Daerah Indramayu
Abstrak: The objective of this study was to built the probabilistic modeling and to compare the Bayesian Networks (BNs) method which considers the spatial dependencies among variables for predict rainfall to Auto Regressive Integrated Moving Average (ARIMA) method which assumes the spatial independencies among variables. This research use rainfall data of 14 stations in period 1979 to 2001 in Indramayu, West Java. A network is generated by K2 algorithm, and this networks is used to built probabilistic model using multinomial BNs. The rainfall prediction is based on the combination of Gaussian BNs and ARIMA (BNARIMA). The result shows that BNARIMA is more effective to represent the spatial dependencies among the stations and to predict rainfall than ARIMA.
Keyword: 

Judul: Rainfall occurence modeling with dynamic bayesian networks (Case study: rainfall occurence in Indramayu)
Abstrak: The objective of this research was to build Dynamic Bayesian Networks (DBN) model for modeling time series data and to predict occurence of dry, normal and wet month in Indramayu, West Java. DBN is the extention of Bayesian Networks (BN) considering a sequence of time slices corresponding to different snapshots in time of the same static BN, connected by temporal links. BN itself is a direct acyclic graph in which nodes represent random variables and edges indicate conditional dependencies among them. Thus, DBN can represent spatial and temporal dependences among variables. In this research, a discrete DBN model was constructed by learning network structures and parameters from the real data. Criteria for learning are introduced by a Bayesian networks viewpoint. In this case, K2/K3 algorithm and Adaptive Importance Sampling - Bayesian Network (AIS-BN) were used as learning algorithm. The research use rainfall data in 1997 to 2008 from 14 stations at Indramayu, West Java. It was categorized into 3 states: dry (0-100 mm), normal (100-200 mm), and wet (above 200 mm). The result shows DBN could model rainfall data from 14 stations which considered spatial and temporal dependences. The state probabilities of stations could be read from conditional probability of networks and it could be changed by instanted an evidence from other station. The state occurrences prediction of 12 months in 2008 by DBN model shows 12 of 14 stations have a value greater than 75% of accuracy. The accuracy measured by way of transformed Hellinger Distance.
Keyword: 

Judul: Karakteritsik kimia dan organoleptik tablet effervescent putih telur bersitarasa lemon dengan konsentrasi effervescent mix yang berbeda
Abstrak: Egg is an animal origin food product which has high nutritional value especially protein. Cholesterol content of eggs is only found in egg yolk, on the other hand albumen does not countain cholesterol at all but high content of protein, so it can be used as a free cholesterol-protein sources. The application of albumen as egg instant drink effervescent tablet can be an alternative of protein source for people who need free-cholesterol diet. It can also become an egg product diversification.
Keyword: