Indonesia is a country prone to experiencing natural hazards and disasters, which have frequently damaged public infrastructure, including hospitals. The role of hospitals is crucial to alleviate the impact of disasters. However, there is still a lack of study that analyzes the factors that influence the readiness of hospitals in emergency situations. Filling in this gap, the aim of this paper is to analyze and rank hospitals across West Java and Yogyakarta, Indonesia by the resilience of their emergency management approaches. This research seeks to measure hospital resiliency during emergencies and disasters. Results indicate that the emergency and disaster management coordination, response and disaster recovery planning, communication and information management, logistics and evacuation, human resources, finance, patient care and support services, decontamination and security are key attributes for the decision-making matrix. Based on the Hospital Safety Index tool, this research proposes the Hospital Emergency and Disaster Management (HEDM) index by combining the key attributes and sub-attributes using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as a multi-attribute decision-making technique. The paper concludes that the anticipated benefits of analyzing the resilience of hospitals by using HEDM is the identification of the most susceptible hospitals based on their levels of readiness and resiliency in areas which are prone to experiencing disasters. This prioritization is important for resource allocation and budget planning.
Sales data in 3 different shops (shop, Shop Maker Fernando and Son) at Tohaga Market in the form of PD book transactions are only seen in the absence of follow-up to determine the decision on who will come. Party owner only records the transactions of products sold and only see income per month. But with that data should be utilized to strategize on sales to come. By using the method of Frequent Pattern Growth Algorithm, the store can take decisions which require goods inventory more compared to other goods, and the placement of the goods in accordance with the relationship between the goods that are usually purchased a consumer can also be determined based on a Minimum Support and Minimum Confidence. Based on Market Basket Analysis obtained from the calculation of the Association by using the method of Frequent Pattern Growth Algorithm, then search for the value of the support and confidence to use Association Rules, Rules that are generated will be test by using Software RapidMiner. Then the placement of goods and inventory items in 3 different stores can be controlled with either the service so that the consumer will be increased, which in turn can increase the sales turnover. In this study Support is determined using threshold 40% and 83% Confidence. Having regard to the relationship of support and confidence the store owner can provide and put the items to be sold
The library is a source of information and a place of learning. Each book lending information is stored by the library so as to produce large data lending books. Big data if not utilized will create problems in the future. In this study, researchers will utilize a priori algorithms and Tanagra software to group library book borrowing data at Ahmad Dahlan ITB based on trends that occur together in library visit activities. In the process of borrowing books, of course the raw data will be processed by dividing it into different pieces of data. Among the lending data tables processed are general lending tables, 2-itemset candidate tables, lending tabular tables, support value tables, confidence value tables. From the results of this study it can be seen what books are often borrowed together with a minimum support of 5% and 10% confidence one of which is Taxation and Taxation Accounting with a minimum support of 7.30% confidence 62.79%. And can be used as a reference for ITB Ahmad Dahlan in the procurement and placement of library book layout.
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