Inflation is the tendency of increasing prices of goods in general and happens continuously. Indonesia's economy will decline if inflation is not controlled properly. To control the inflation rate required an inflation rate forecasting in Indonesia. The forecasting result will be used as information to the government in order to keep the inflation rate stable. This study proposes Fuzzy Neural System (FNS) to forecast the inflation rate. This study uses historical data and external factors as the parameters. The external factor using in this study is very important, which inflation rate is not only affected by the historical data. External factor used are four external factors which each factor has two fuzzy set. While historical data is divided into three input variables with three fuzzy sets. The combination of three input variables and four external factors will generate too many rules. Generate of rules with too many amounts will less effective and have lower accuracy. The novelty is needed to minimalize the amount of rules by using two steps fuzzy. To evaluate the forecasting results, Root Means Square Error (RMSE) technique is used. Fuzzy Inference System Sugeno used as the comparison method. The study results show that FNS has a better performance than the comparison method with RMSE that is 1.81.

Enhancing food safety in developing countries, such as Indonesia, poses more challenges, especially those of the small- and medium-scale. Various food safety systems are available and readily implemented in the food industry. However, to ensure the effectiveness of such systems, pre-requisite programs should be applied prior to the implementation of food safety system. One of the most acknowledged pre-requisite program is Good Manufacturing Practices (GMP). The aim of this study is to assess the GMP compliance of some small-scale food companies in East Java. Three types of traditional food product were selected, include tempe chips, palm sugar, and instant herbal drink. A survey involving three companies for each type of traditional food was conducted. Data was obtained through observation and assessment based on tabulated criteria in GMP criteria. In essential, the result revealed the compliment level of the food companies being surveyed. There was different level of compliment between each type of the food industry, where the palm sugar industry had the lowest level of compliment compared to the other two. This difference is due to the food safety awareness, social and cultural influences, and also knowledge on food safety and hygiene practice.

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