Triclustering has been applied to three-dimensional gene expression data (gene, condition, and time) to group the dataset into sub-matrix groups that have similarities. One of the algorithms of triclustering analysis is the Extended Dimension Iterative Signature Algorithm (EDISA). This algorithm considers the Pearson distance between each gene and condition against the mean vector as a measure of similarity. The primary process in EDISA is the iteration process by deleting each gene and condition with a Pearson distance to the mean vector above a certain threshold. It is a measure of the similarity of a gene and condition to the mean of the tricluster candidate. EDISA was applied for lung disease gene expression's data using several scenarios with different thresholds. The result is that the higher the threshold value of each gene and condition, the more genes and conditions in the tricluster. Also, an evaluation was carried out using the Tricluster Diffusion (TD) Score value to find the best scenario where the best scenario was the scenario with the smallest TD Score. This algorithm's application to lung disease data generates triclusters, which can detect genes that distinguish the characteristics of patients with lung disease and healthy patients.