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Dengue fever is still a crucial public health problem in Indonesia, with the highest fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38.

This article presents a system for predicting a human's risk of contracting infectious diseases based on their personal attributes and environments (region, specific location features and climate contexts). This system is also intended to help human experts in the domain (i.e. epidemiologists) to represent their knowledge and ease their jobs related to personalized infectious disease risk prediction. The system consists of a knowledge representation to encode epidemiological knowledge about infectious disease risk, and an algorithm that auto-converts the encoded knowledge into a model that predicts the risk as a probability. The knowledge representation, Infectious Disease Risk (IDR), consists of an ontology and rules to represent the knowledge structure and its quantification in a way that allows auto-generation to a prediction model, Bayesian Network (BN). The algorithm, BN-Builder, converts the IDR knowledge-base to an infectious disease risk BN, including populating the basis of predictive reasoning from the IDR rules. A user interface facilitates encoding of epidemiological knowledge into the IDR knowledge-base. The system's output, personalized infectious disease risk prediction, is validated for three disease-country contexts: Dengue Fever and Tuberculosis in Indonesia, and Cholera in India. The personalized infectious disease risks are reliable (p values > 0.05) for each population parameter. The personalized infectious disease risk probability can be reliably predicted using this system. Inclusion of more granularity on contexts in this domain will be considered in further development of this system.

The stock market is one of the investment choices that always have traction from time to time. Aside from being a means of corporate funding, investing in the stock market can benefit investors. Investing also has a higher risk because the pattern of stock prices is volatile, which is caused by internal and external factors. One external factor that affects stock prices is the macro-economic, where these factors are events that occur in a country where one of the economic sectors affected is stock prices. Investors often feel confused about the right time in decisions making related to buying or selling stock. One way to look at how the prospect of stock prices is a stock price forecasting activity. For this study, we will be making use of the recurrent neural network (RNN) to forecast stock prices for the next periods. This research involves two variables: the closing stock price and the rupiah exchange rate against the dollar for the daily period. We achieve a MAPE value of 1.546% for RNN model without the variable foreign exchange rate and 1.558% for the RNN model that uses the foreign exchange rate against the dollar.

Infectious diseases are a major cause of human morbidity, but most are avoidable. An accurate and personalized risk prediction is expected to alert people to the risk of getting exposed to infectious diseases. However, as data and knowledge in the epidemiology and infectious diseases field becomes available, an updateable risk prediction model is needed. The objectives of this article are (1) to describe the mechanisms for generating a Bayesian Network (BN), as risk prediction model, from a knowledge-base, and (2) to examine the accuracy of the prediction result. The research in this paper started by encoding declarative knowledge from the Atlas of Human Infectious Diseases into an Infectious Disease Risk Ontology. Automatic generation of a BN from this knowledge uses two tools (1) a Rule Converter generates a BN structure from the ontology (2) a Joint & Marginal Probability Supplier tool populates the BN with probabilities. These tools allow the BN to be recreated automatically whenever knowledge and data changes. In a runtime phase, a third tool, the Context Collector, captures facts given by the client and consequent environmental context. This paper introduces these tools and evaluates the effectiveness of the resulting BN for a single infectious disease, Anthrax. We have compared conditional probabilities predicted by our BN against incidence estimated from real patient visit records. Experiments explored the role of different context data in prediction accuracy. The results suggest that building a BN from an ontology is feasible. The experiments also show that more context results in better risk prediction.