<![CDATA[Graduating on time is the desire of all students, not only that graduating on time is an advantage for both parties, namely students and educational institutions. But the graduation status of students when predicted does not always produce predictions early, so that it can result in graduation not on time. This research was conducted using data of students who graduated for 4 years from 2016-2019. The classification method is an approach to grouping data in data mining, namely classifying data. This classification method can also be used to make predictions for information that has not been previously known. The classification data mining method that will be used is the neural network algorithm. Visualization to display recapitulation data visually more interesting and neural network algorithm to predict student graduation which is difficult to do manually. Attributes used in training data consist of Jenis Kelamin, Asal, Kelas, Jurusan, Umur, IPK, Tanggal Yudicium, Tahun Yudicium and Class Hasil. The attributes that become parameters are 9 attributes, of which 8 are predictor attributes and 1 are results attributes. Training and testing data by changing parameters, namely: Hidden Layer Size: 3, Training Time: 500, Learning Rate: 0.3, Momentum: 0.2 produces a classification showing the level of accuracy using a neural network algorithm is 92.83%. Displays some very complete reporting recapitulation, so that predictions and visualization of the data can help in graduating students and provide recommendations for appropriate actions and must be done by management or the authorities to make decisions.]]>
