Complexity in data structure has led to the rapid development of computational statistics methods. Machine learning approaches have been introduced and applied to solve complex problems in many fields. This paper applies two common machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), in the detection of epilepsy. The diagnosis of epilepsy can usually only be made when a seizure is happening, which leads to some difficulties in the diagnostic process. The most recent way of diagnosing epilepsy is by using an Electroencephalograph (EEG) record. However, detecting epilepsy cases through EEG records takes a long time and may lead to misleading diagnostic results. The use of machine learning approaches is intended to generate fast and accurate classification results. As the EEG only generates a signal, direct analysis using RF or SVM cannot be carried out and the EEG record needs to be pre-processed. This paper uses Discrete Wavelet Transform and Line Length Features in the data preprocessing stage to decompose the signal by frequency and time. The classification results show that both RF and SVM perform very well and are able to classify cases of epilepsy accurately. The RF outperforms the SVM in the training dataset, while the SVM has a better performance in testing, with almost nom is classified cases. Several open problems relating to interpretation as well as parameter settings are described.

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Mount Arjuno is a 3,339meter high cone-shaped volcano located in East Java, Indonesia. At this time, information about climbers that is spread in online media makes it easy, there are many features that can be used to find climbing information on Mount Arjuno. Making this Arjuno augmented reality application uses the Rapid Application Development model with the main goal of producing high quality and quantity. Where this method places more emphasis on working on application software and user feedback in the planner. And testing for the tracking distance of the camera to the marker, to ensure the distance that can be traveled by the camera when scanning objects to targets. By creating an android application that uses the Marker Based Tracking method which can assist climbers in choosing hiking trails on Mount Arjuno so that climbers know the distance of the path traveled when climbing.

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