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Satisfaction Level of BPJS Kesehatan Participants Using the C4.5 Algorithm Okta Mazhona, Irvan; Yuhandri
SYSTEMATICS Vol 2 No 3 (2020): December 2020
Publisher : Universitas Singaperbangsa Karawang

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Abstract

Patient satisfaction is an important thing in a hospital service. The level of patient satisfaction can be a reference for improving service to patients. Satisfaction is the level of feelings that arise as a result of the performance of the service received after comparing it with what is expected. This study aims to measure the level of satisfaction of inpatient BPJS Kesehatan participants with the services provided by the Special Hospital for Mother and Children (RSKIA) Annisa Payakumbuh in terms of five attributes, namely Tangibles (Real Form), Reliability, Assurance, Responsiveness (Responsiveness) and Empathy (Empathy). To measure the level of patient satisfaction at RSKIA Annisa Payakumbuh used data mining method of Classification Algorithm C4.5 which is one of the most effective Decision tree algorithms for classification. The data were obtained from the summary of the results of the BPJS Kesehatan inpatient patient satisfaction survey at RSKIA Annisa Payakumbuh. Furthermore, the data will be processed using the C4.5 algorithm which will produce rules and Decision trees. The results of data processing using the C4.5 Algorithm obtained Responsiveness as the root variable and resulted in 8 rules with 3 satisfied rules and 5 unsatisfied rules. Based on the results of this study, it can be concluded that the use of the C 4.5 Algorithm Decision tree can be used to measure the level of satisfaction of BPJS Kesehatan inpatients at RSKIA Annisa Payakumbuh. The results of this study are expected to help the RSKIA Annisa in making policies to improve services for patients.
Mining Data In Identification Of Consumer Patterns Of Agricultural Machine Sales Using Fp-Growth Algorithm Sofianti, Eka; Defit, Sarjo; Yuhandri
SYSTEMATICS Vol 2 No 3 (2020): December 2020
Publisher : Universitas Singaperbangsa Karawang

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Abstract

The sales transaction data for agricultural machinery at the Mandiri Jaya Teknik Solok store is a large data set making it difficult to identify consumer purchasing patterns. Large data sets can be processed into useful information. Sales transaction data available at the Mandiri Jaya Teknik Solok store can be processed into useful information to increase sales. This study aims to identify consumer purchasing patterns in order to know which items are often sold and to find out which items need to be stocked more and to increase sales. The data that is processed in this study uses the sales transaction data obtained from the sales invoice of Toko Mandiri Jaya Teknik Solok. Data is in the form of sales data for 13 weeks of 20 items with a minimum support value of 15% and a confidence value of 60%. The method uses one of the data mining techniques associated with the FP-Growth algorithm, where the Fp-Growth algorithm uses the concept of tree development in searching for the types of items that are often purchased (frequency item sets). The tools used are Rapidminer 9.8 so that the purchase patterns of goods are obtained which are used as information to predict the level of frequently sold items. The result of the sales data processing process is the association rule. Association Rule is obtained in the form of a relationship between goods sold together with other goods in a transaction. From this pattern, it can be recommended to the shop owner as information for preparing stock of goods to increase sales results. This research is very suitable to be applied to determine the patterns of consumer spending such as the relationship of each item purchased by consumers, so this research is appropriate for use by stores.