Query Tuning in Semantic Inference Fuzzy Logic Algorithm for Real-Time Recommendations

The query tuning, which is mostly found in the syntax semantics of fuzzy logic inference algorithms, uses the query syntax of the boolean data type. Fuzzy logic inference is converted using AND and OR operators to connect variables so that it can arrange criteria according to the wishes of the user. The preparation of multi-criteria for each fuzzy and non-fuzzy variable causes a lot of "rule-base" semantics, all of which will not occur. This results in an input platform for output that matches the criteria that the user wants, using a large storage capacity. The purpose of this research is to propose different tuning queries for fuzzy logic tuning queries. The query tuning proposed in this study uses virtual memory as a platform to display information according to the user's wishes. The semantics used are according to the syntax of the fuzzy logic membership set theory formula, which is processed directly in virtual memory without operators: AND, OR, NOT.