ANALISA POLA DATA PENYAKIT DI KLINIK GIGI RDC DENGAN MENERAPKAN METODE ASSOCIATION
Keywords:Apriori, Association, Data Mining
Health is an expensive thing, so keeping it healthy is one of our obligations. To improve health, mastery of technology must increase, and if used correctly, technology use can be steered. For this reason, technology is used to analyze a patient or a patient's disease so that the hospital or clinic can serve it well. One way to solve this problem is to search for patterns or association rules (association rules) in the database whose owner has a relationship with data mining to find specific rules. A priori algorithmic approaches can perform searches on historical data to identify data patterns based on previously identified features. In this study, the approval rating was 20% and the trust rating was 80%. Among them, support is the percentage value of the combination of complaint items in the database, and confidence is the certainty of the relationship between items in the rules generated and processed by Excel and Tanagra software. The result is consistent with the highest RDC clinical case. The base value is 12.24 and the confidence level is 80.00%. and dental consultations. Therefore, if a patient complains about dental veneers, it is likely that 80.0% of the patients will consult first
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