Indonesia is an archipelagic country with a coastline of about 81,000 km and has a variety of very large biological and non-biological resources. Position as an archipelagic country with a very wide sea causes each region to have the potential to produce salt. The Covid-19 pandemic has resulted in various negative impacts on several fields such as the economy, social, SMEs and community services. Covid-19 has also resulted in a decrease in salt production and farmers also have difficulty marketing it. The research problem is the large number of salt farmers and number of indicators in providing assistance so that a model for measuring performance of salt farmers is needed. This performance measurement model is guided by several indicators, namely land area (K1), production result (K2), business capital (K3), and marketing system (K4). The method used Interval type-2 Fuzzy Analytic Hierarchy Process (IVFAHP). IVFAHP is used to determine indicators that most influence measurement of salt farmers. This study aims to build a model for measuring performance of salt farmers to increase economic productivity and ability of human resources to deal with COVID-19 pandemic. The contribution is a group-based decision by developing the fuzzy interval type-2 method with triangular fuzzy number (TFN) one midpoints. The findings from study are that the most influential indicators in dealing with COVID-19 pandemic are business capital and salt marketing. This research also produces recommendations for improvement salt farmers in an effort to increase salt production.
This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.
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