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Ischemic stroke is a disease that occurs due to disruption of blood circulation to the brain due to blood clots in the brain. The blockage is called cerebral infarction. In diagnosing the presence of cerebral infarction in the brain, machine learning is used because it is not enough just to use a CT scan to diagnose. To deal with the problem of classification of cerebral infarction data obtained from Dr. Cipto Mangunkusumo’s Hospital in Jakarta, this study proposes the use of Fuzzy C-Means Clustering (FCM), Fuzzy Possibilistic C-Means (FPCM), and Radial Base Function Fuzzy Possibilistic C-Means (RBFFPCM) method as a clustering method and a Support Vector Machine (SVM) method as a classification method. This method will be compared to the level of accuracy. The greatest level of accuracy is generated from the Radial Base Function Fuzzy Possibilistic C-Means (RBFFPCM) method with an accuracy value of 91%.

Sinusitis is a condition resulting from inflammation of sinus walls. In handling the disease, machine learning method is often used to find more precise and accurate treatment plan for patients. For instance, fuzzy clustering is widely used for pattern recognition and data mining. Due to uncertainty and ambiguity, this method is used to overcome the non-linearity of medical dataset. In this study, fuzzy clustering was provided with kernel methods. We used some Kernel methods such as, Kernelized Fuzzy c-Means (KFCM), Kernelized Possibilistic c-Means (KPCM), Kernelized Fuzzy Possibilistic c-Means (KFPCM), and Kernelized Possibilistic Fuzzy c-Means (KPFCM) for clustering sinusitis dataset. The dataset was retrieved from Cipto Mangunkusumo Hospital Jakarta, Indonesia, which contains 4 features and 200 instances of this condition. These level of accuracy and model performance are used to compare these approaches. The result showed that KFCM has the highest accuracy for categorizing sinusitis dataset with accuracy of 96.97% and running time of 0.01 s.

Soft Tissue Tumor (STT) are cell growths, whose existence are not limited to the presence of tumors in soft tissues. Furthermore, they are classified into soft tissue and non-soft tissue tumor and early detection is important to determine the right course of treatment. This research, therefore, aims to compare fuzzy kernel c-means, fuzzy kernel possibilistic c-means and support vector machines on Soft Tissue Tumor dataset, obtained from Nur Hidayah Hospital, Yogyakarta, Indonesia, consisting of 50 STT and 25 non-STT samples. The results conclude that fuzzy kernel c-means provides a better running time when using the parameter \( \sigma = 0.05 \). However, support vector machines, with the parameter \( \sigma = 0.0001 \) performs better than other methods in terms of accuracy, sensitivity, precision, and F1-Score.

There are two classes of sinusitis, acute and chronic. This research compares the modified hierarchical clustering based on density peaks (HCDP), using kernel function with support vector machines, utilizing dataset taken from Cipto Mangunkusumo Hospital, Indonesia. This collected detail consists of 102 acute and 98 chronic sinusitis samples. The results of this research therefore conclude that HCDP, using Gaussian radial basis kernel function, with the parameter \( \sigma = 0.01 \) possess an accuracy of 72.69% and has a better running time. However, the average accuracy of support vector machines is 98.53%.

Taking action against climate change and its effect has been agreed and stated in the 13th Goals of SDGs (Sustainable Development Goals) been declared by UNs High Level Meeting in New York on 25–27 September 2015. Thus, in the 13th goals of indicators 13.2 stated that‚ Action Index of Climate Change Indicator needs to be developed into 14 indicators”. In terms of to support the 13th Goals of SDGs, so the availability of information on disaster prone areas is an important and high demand for anticipating and handling of rehabilitation particularly of infrastructure and for the emergency response. This research is an empirical evidence and concrete experiences from pilot project in 2013 which aims to assess the urgency of creating measurement related particularly adaptation to vulnerable people links to climate change effect of which resulted by disaster in Indonesia in Padang City. The disaster risk reduction and preparedness are an effort to strengthen the resilience against unexpected disaster, therefore, the provision of Disaster Preparedness Indices would be expected to facilitate the measurement of disaster knowledge level of community related with communication-information, as consequent, reducing the size of disaster victims can be anticipated.