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Haemorrhagic stroke, or also known as spontaneous intracerebral haemorrhage (PIS), is one of the pathological types of stroke caused by intracerebral blood vessels. This condition causes neurological symptoms that occur suddenly and often followed by symptoms due to the spatial effects or increased intracranial pressure (TIK). Generally speaking, the number of strokes is increasing, according to (RISKESDAS) the Ministry of Health of the Republic of Indonesia there is an increase in stroke prevalence from 8.3 (2007) to 12.2 (2013) per 1000 people. In PIS, primary injury occurs: neuron and glia damage mechanically followed by deformation, neurotransmitter release, mitochondrial dysfunction and cell membrane depolarization. Secondary injuries due to haemoglobin breakdown and coagulation factors especially thrombin. The product will activate microglia so it produces toxic substances such as reactive oxygen species (ROS), matrix metalloproteinase (MMP), cyclooxygenase-2, prostaglandin, heme oxygenase-1 (HO-1), complement factor, tumour necrotizing factor α (TNF α), and interleukin 1β causing network injury. In addition to injury, there's also a replacement of dead cells through the neurogenesis process, which is the growth of neuronal stem cells in the subventricular and hippocampus areas. The number of hemorrhagic strokes in Asia is higher than in the West. This could be due to the high incidence of hypertension in the Asian population.

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.

This study discusses the Analysis of Security Management Systems: K3 and Workload at PT XYZ. The purpose of this study is to determine the level of work risks that have occurred to those that have not occurred at PT XYZ. By knowing the level of risk, PT XYZ's management takes policies to avoid work risks that affect operational activities and company profits. The object of this research is all employees of PT XYZ. In carrying out security management in the form of risk analysis, researchers used a five (5) scale likelihood matrix table. Then the data from the assessment results were inputted into Microsoft Excel, so that researchers could find out the risk level of each activity at PT XYZ. The number of activities used in the assessment is twelve (12) activities or jobs, starting from employees entering the company area until employees leave or leave the company area. The results of this assessment are: 1) Occupational Safety and Health (K3) has an effect on the Security Management System at PT XYZ; 2) Workload affects the Security Management System at PT XYZ; and 3) K3 and Workload affect the Security Management System at PT XYZ simultaneously.