Indonesia is a country that has a wealth of diverse cultures spread from Sabang to Merauke. Even though the government has included a lesson about Indonesian culture in terms of some subjects such as Art and Culture, but there are still many people including some students who do not know the forms and names of the tribes, traditional houses, traditional clothing, folk songs, traditional foods, local weapons, and musical instruments regions in Indonesia. Therefore, a learning application is created that utilizes mobile learning technology. Through the use of mobile learning, user can access learning content without any borders so that it can be accessed at any time so that it can be accessed at any time with interesting illustration. Indonesia National Culture Application is made useful to help users in learning the introduction of Indonesian culture that provides the knowledge with illustrations in the form of images, audio, text and video. This application also can be functioned to learn, answer quiz, to view a gallery of images and video and to do puzzle games. The achieved result is an application that can provide knowledge to the users so they can know the culture in Indonesia through the illustrations provided well.

The tourism sector become a promising country income. This is because its nature which is not easily run out if managed properly. This sector can be a great support especially for developing countries such as Indonesia, because usually developing countries income only rely on natural and human resources income. Museums are one of the tourism sectors that have not been explored properly. Museums in Indonesia are still few that using the latest technology such as smartphone application, wifi, bluetooth, RFID. So, that it makes the museum a less attractive place to visit, especially for young generation. This research tries to create an interactive broadcasting museum information by utilizing applications on smartphones, indoor positioning systems, and bluetooth low energy beacons to provide information to museum visitors about artifacts around museum visitor location. As a pilot project a testing was conducted at the Trowulan Museum, Mojokerto, East Java, Indonesia, which is a museum that exhibits the relics of the Majapahit kingdom. The application has been able to provide interactive information about artifacts around the visitor's location so as to create an interesting museum to visit.

Topic Based Vector Space Model (TVSM) proposed a new vector space that its dimensions is composed of topics. Every term and document is represented by vectors inside this vector space. By using topics as dimensions TVSM tries to overcome word-mismatch between terms with similar topics in finding relevant documents to query. This study proposes to develop relations between terms using WordNet and thesaurus to help TVSM calculating similarity between documents. Relations between terms are represented by relation score. This study proposes a way to find optimal relation score for a set of documents. To help indexing documents with multi language terms this study also proposes to use dictionary to expand query terms.

The rapid development of online business in recent years has driven Store X to embark on a digital transformation. By the end of 2020, Store X relocate their conventional business to online business. The greatest obstacle and key to success for online business operators, such as Store X, is gaining and retaining consumer loyalty in the face of an increasing number of competitors. Therefore, the company must be able to identify the character (behavior) of its clients to provide appropriate treatment. Each customer's behavior is unique, which means they must all be treated differently. However, all this time, Online Store X has provided the same treatment (as much of a discount) to all its customers due to the lack of information regarding their customers’ characteristics. Therefore, in this study, customers of Online Store X were segmented based on their transactional behavior using online transaction history data from March 2021 to March 2023. Two customer analysis models, LRFM and MLRFM, will be combined with RM K-Means to find the best combination through Silhouette Coefficient values. The optimal number of clusters (k) is then determined using the Elbow Method. The results indicate that the optimal number of clusters for both combinations is K=3, with the combination of MLRFM and RM K-Means is the best combination. The finest combination has a silhouette coefficient value of 0.8609. Based on this combination, it is also known that 2,053 customers in cluster 3 are loyal customers, while 2,339 customers in cluster 1 and 2 are lost customers. The results of this study were also implemented on websites built for X Store using Python programming languages and MySQL databases, making it easier for companies to see data visualization.

At Petra Christian University Library, book searching engine system is using a common keyword matching as search queries. So, the user must enter the correct keyword and this case often takes a long time for users to find book that needed. Therefore, required an application that's capable display the search results that classified into a group/cluster. Which is each cluster will contain the documents that have the same classification base on keyword that been input by user in order to assist users in perform book searching. In this study, have been build an application to classify the document search results into a group of documents that have the same classification/cluster. Input to the cluster obtained from regular search results then the classification/clustering done by using Suffix Tree Clustering (STC) uses the document phrase. Based on the testing, the resulting cluster has been able to cluster documents that correlate or have the same classification with the keyword that input by user. The average time that needed to process for each document is 0.1139 seconds.