The common problem for data collection is happening missing value during the data collection and processing process that the quality of the data testing is decreased. A computational based technique for dealing with missing values, namely Genetic Algorithm Imputation (GAI). The usage was used to estimate the dataset's missing values. GAI generates the optimal set of missing values with the acquisition of information as a function of fitness to measure individual solutions' performance. GAI conducts continuous searching until the missing criteria value is found according to best fitness. So, it is trapped in optimal conditions temporarily. The improvement of GAI with tabu search is known as TS-GAI, that strength is two metaheuristic techniques modified at the mutase stage to distract the local optima's search. In applying missing values, this technique works better when many possible values are used instead of the mixed attribute having missing values. Because the new generation chromosome values generate many opportunities to make up for the missing values. The experimental results show that the TS-GAI shows better performance on 30% MV with a fitness value of 0.212. It converges at 159 iterations. Generally, TS-GAI is a faster iteration than simple GAI and it has a lower RMSE level than other imputation techniques
