Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.

Promotion becomes one of the important aspects of institutions of college. The number of competitors demanding the marketing must be fast and accurate in formulating strategies and decision making. Data warehouse and data mining become one of the means to build a decision support system that can provide knowledge and wisdom quickly to be taken into consideration in promotion strategy planning. Development of this system then does the process of testing with the number of data 6171 rows of student enrollment taken directly from a transactional database. The data is done ETL process and clustering with the k-means clustering algorithm, then the data in each cluster is done grouping and summarization to get weighting. After that just done ranking to produce wisdom, one of them determine the list of schools that will be the target roadshow. The analysis also produces several patterns of student enrollment, namely the registrant pattern from the wave of registration and favorite or non-favorite school categories. In addition, the results of system design in this study can be developed easily if requires added external data. Such as data of SMK/SMK school graduates in the area or data of students enrolling in other universities. This is one of the superiority of semantic-based data warehouses.