To adapt the IT curriculum to the requirements of the IT industry skills, several methods have been proposed. Among them is the method of mining job advertisement data to find skills that are being sought by the industry. However, so far no significant research has focused on providing recommendations on skills that need to be taken along with other popular skills to fill the job vacancies offered. Traditional recommendation methods cannot be applied because information related to user or industry ratings on a skill is not available in advertisements. This article proposes an alternative solution to this need by developing recommendation techniques based on skill association rules, where the rules are mined using Apriori algorithm. The recommendation results were confirmed to curriculum managers in several universities, and had obtained quite good recall and precision, namely 70% and 76% respectively. The proposed recommendation system is also able to find skill combinations that are prominent in job advertisements.

One of the services in the university library is an information system to find the availability of library collections and the location of each collection shelf. But not many of these systems provide a mechanism that can recommend visitors not only about the books they want, but also other related books that may be needed. This study uses association rule mining techniques that are applied to library transaction data to identify relationships between books (titles) that attract visitors' attention. Relationships are built on interesting measurements between the titles, namely support and confidence, where support determines the combination of the most frequently borrowed book titles, while confidence produces the possibility that the title of the book will be borrowed along with other books. The pattern of book titles association with high confidence indicates that the titles are very related so it is recommended for visitors to consider borrowing along with the book they are looking for. In addition, the system can also recommend the procurement of new books and rack configurations to improve the visitor's experience when searching for books on the site. In the experiment, the precision of recommendations generated from the system reached 70%. Web applications were developed to help understand the effectiveness of the recommendation system based on association rules.