Development of University Digital Sketch-Map based on Experiences and Digital Traces

The main purpose of a university is education. However, in addition to the educational process, people who are active in the university environment will surely leave valuable moments or experiences, especially in places that are often visited or even places that leave unforgettable impressions. Even so, the lack of attention to the moments or experiences makes the moments easily forgotten, and not conveyed to future generations. Therefore, we developed a digital sketch-map using past photo data and the recollections of campus society, we also conduct investigations on the recollections we got using Falk's theory of “making of meaning” in a museum. The purpose of this study is to develop a digital sketch-map to ease archiving and saves some memories of Universitas Negeri Malang’s socie

Classification of Lexile Level Reading Load Using the K-Means Clustering and Random Forest Method

There are various ways to improve the quality of someone's education, one of them is reading. By reading, insight and knowledge of various kinds of things can increase. But, the ability and someone's understanding of reading is different. This can be a problem for readers if the reading material exceeds his comprehension ability. Therefore, it is necessary to determine the load of reading material using Lexile Levels. Lexile Levels are a value that gives a size the complexity of reading material and someone's reading ability. Thus, the reading material will be classified based a value on the Lexile Levels. Lexile Levels will cluster the reading material into 2 clusters which is easy, and difficult. The clustering process will use the k-means method. After the clustering process, reading material will be classified using the reading load Random Forest method. The k-means method was chosen because of the method has a simple computing process and fast also. Random Forest algorithm is a method that can build decision tree and it’s able to build several decision trees then choose the best tree. The results of this experiment indicate that the experiment scenario uses 2 cluster and SMOTE and GIFS preprocessing are carried out shows good results with an accuracy of 76.03%, precision of 81.85% and recall of 76.05%