Books are one of the most widely used objects in daily life. With the development of the times, there are other alternatives that can be used to read books without having to buy books in stores. One alternative is the website www.goodreads.com where the website provides a variety of books. On the website, we can also give ratings and review s of books that we have read. These review s and ratings can provide a reference for readers. For this reason, an analysis of book rating is required based on data obtained from the www.kaggle.com website. By processing the data obtained will find the best book viewed from several aspects. The purpose of this research is to determine the rating of a book as a reference for readers in choosing the appropriate book. In this study using a classification algorithm naïve bayes data mining. This research was assisted by rapidminer and Python tools as tools to manage data. The results obtained are the results of determining the book rating using the naïve bayes method having an accuracy of 66.98%, precision 74.47% and recall 62.47% and the results of this analysis are obtained from the dataset available on the website www.kaggle.com showing that the majority book rating predictions tend to be low.

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KOPKAR PT. YKK AP INDONESIA is one of the cooperative bodies in the Regency of Tangerang which has a rental unit, this rental unit cooperates with PT. YKK AP INDONESIA to distribute the products. However, their distribution routes are still ineffective, random, and carelessly to the detail of the distances. It affected the high-costs distribution process. A method of saving matrix used in this research to determine the distribution route to the destination market in a way that distribution route must be passed, the number of vehicles, its capacity to get shorter routes, and low-cost transportation. From the data processing and discussion, we concluded that saving matrix can reduce company expenses from 1.082,8 km to 912,9 km, it also saved the distribution costs by Rp. 71.151/day or Rp. 2.134.530/month.