Batik culture is very strong in Indonesia, this is the reason that batik can be found throughout the archipelago, with unique characteristics that distinguish it in each region. However, people are often confused and find it difficult to recognize one type of batik from another. One of the famous types of batik motif is Batik Parang. This research aims to establish a Convolutional Neural Network (CNN) model to classify Batik Parang and help people distinguish it from other batik motifs. Deep learning, particularly CNN, was chosen because it has a high accuracy rate in image classification. A quantitative Experimental design is used, using a dataset of 100 batik images evenly divided into two classes, namely Batik Parang and not Batik Parang. The dataset is divided into two categories, namely training data and testing data, with a data ratio of 80:20. Thus, by using Convolutional Neural Network (CNN), the classification between Batik Parang and not Batik Parang produces an accuracy of 95%, with the use of epoch = 118 and batch_size = 100.

This study aims to determine: (1) The effect of interpersonal communication on employee work productivity at the Kolaka Regency Tourism Office, (2) The effect of motivation on employee work productivity at the Kolaka Regency Tourism Office. The sample in this study amounted to 55 respondents using a sampling technique, namely the census method. Testing research instruments using validity and reliability tests with SPSS 23.0. The research data analysis technique used the Structural Equation Modeling (SEM-PLS) method with SmartPLS 4.0. Based on the results of this study it can be seen that the interpersonal communication variable has a positive and significant effect on work productivity as indicated by a T-Statistics value of 1.694 and a P-Values ??of 0.047 and the motivational variable has a positive and significant effect on work productivity as indicated by a T-Statistical value of 2.191 and P-Values ??of 0.015.