DEM (Digital Elevation Model) as a digital model of the earth’s surface elevation could be generated from remote sensing technology such as stereo imaging for various applications. To generate DEM from stereo imagery, interpolation or approximation process stage is required. Stochastic interpolation e.g. ordinary kriging uses semivariogram fitting to calculate weights of interpolation values based on known points. This research is applying regression types of machine learning for semivariogram fitting to interpolate DEM. Previous research conducted was LS-SVM (Least Square-Support Vector Machine) and SVR (Support Vector Regression) for semivariogram fitting process. Types of SVM and GPR (Gaussian Process Regression) are adopted for semivariogram fitting for ordinary kriging interpolation in this experiment. The result showed that in general SVM types could predict accuracy better than other types of regression, and GPR types produce better DEM accuracy based on the experiment.

The prevalence of autism children has increased rapidly in the last few periods. There is no cure for autism. But the management and treatment of accompanying medical conditions can be done. One of the effects of his medical condition is a sleep disorder. But children with autism have difficulty communicating the disorders they experience. In medicine, the detection of sleep disorders can be done through a test called polysomnography. One of the purposes of this test is to find the patient’s sleep patterns through the sleep stage classification. But the doctors need several days to analyze each test. This study proposes an application that can classify it automatically. The method used was based on machine learning. The two classifiers were classification via regression and random committee. The both performances were compared in sleep stages classification for the autism patients. The result showed that random committees had a higher performance than classification via regression. Its performance got more than 85% for accuracy, precision, recall, and F-measure. This study also implemented resampling to overcome imbalance class problems. It can be seen that this process was useful in improving the performance of both classifiers.

One of the materials essential for human life that must manage properly is the land. Land use and land cover (LULC) classification can help us how to manage land. The satellite can record images that can use as the data for LULC classification. This research aims to perform LULC classification using Convolutional Neural Network (CNN) on EuroSAT remote sensing image dataset taken from the Sentinel-2 satellite. CNN has become a well-known method to deal with image feature extraction. We used several CNN for feature extraction, such as VGG19, ResNet50, and InceptionV3. Then, we recalibrated the feature of CNN using Channel Squeeze & Spatial Excitation (sSE) block. We also used Support Vector Machine (SVM) and Twin SVM (TWSVM) as the classifier. VGG19 with sSE block and TWSVM achieved the highest experimental results with 94.57% accuracy, 94.40% precision, 94.40% recall, and 94.39% F1-score.

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Leptomeningeal metastasis is an indication of the malignancy that occurs in leukemia patients. Although it only has a 5-10% portion caused the leukemia patient to relapse, the abnormality is the basis in determining the best treatment given to them. Leptomeningeal metastasis are better detected by using Magnetic Resonance Imaging (MRI) because of their high sensitivity in neuraxis images. High ability to see and analyze is needed for a radiologist in reading the Brain MRI results of leukemia patients with suspect leptomeningeal metastasis. Therefore, the classification will take a long time and allow for the misreading of the results. In this experiment, we used a dataset from the Brain MRI of leukemia patients of Dharmais Cancer Hospital. We implemented the proposed method in performing the leptomeningeal metastasis segmentation. The preprocessing image applied for sharpening and removing unwanted noises in the image using the Median Filter. A hybrid semi-automated skull stripping was also developed to improve the accuracy of the segmentation. Then Fuzzy C-Means is used to segment the abnormalities and reach an average evaluation performance at 49.1% Jaccard Index.