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           Many strategies that can be used to increase sales and attract consumers to buy products offered by manufacturers. One strategy that is widely used by companies is the marketing mix strategy. Marketing activities that focus on product self-focus, pricing, distribution policies, and means of promotion are known as the marketing mix or Marketing Mix. This study aims to evaluate the internal evaluation and indicators of the appropriate marketing mix strategy and is applied to increase the sales volume at Griya Manik-Manik Kaca Plumbon. This study uses a qualitative descriptive analysis method, by collecting data directly from the company by making observations, interviews according to the research subject. The results showed that the strategy adopted by Griya Manik-Manik Kaca Plumbon has worked well, where the four marketing strategy variables support and complement each other, and have different roles in marketing their products. We recommend that the owner of the Plumbon Griya Glass Bead UMKM should be upgraded with the marketing mix that has been applied. The strategy used by the company is good, but it would be better if the company plans to continue to improve strategies considering that many other competitors have issued similar products.  

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

The shift in global paradigm and threat perspective has led to a wide variety of possible risks and uncertainties. This situation also occurs in the defense economy, so understanding the basic principles of risk and uncertainty is important, especially in a decision-making process. There are several elements and concepts that are usually used in all decision models. Almost all models, whether complex or simple, can be formulated using a standard structure and solved by using general evaluation procedures. For decisions involving a series of decisions and relating to various basic sequential conditions, the decision tree is an appropriate conceptual and schematic modeling tool. A decision tree is a schematic representation of a decision problem. A decision tree is a diagram made like a tree with branches and branches in a chronological order of events, with each having a choice and possibility of occurrence, as well as the results of each choice. The term decision tree is taken from the form of diagrams that have branches and twigs, just like a tree.