The purpose of this study is to design a digital platform design as one solution to solving poverty problems that exist in the community with various causes. Poverty is a complex problem caused by multidimensionality. The cause of poverty is not only a problem of economic inequality but also caused by social problems, education, and lack of access to communicate or communication. The methodology used in this research is Sociotechnical System (STS), STS analysis used six attributes, namely goals, people, buildings/infrastructure, technology, culture, and processes/procedures. This approach (STS) analyzes approaches based on social problems and technical problems. The final results of this study created a digital platform design as a public space to capture community participation in order to care and want to share with poor communities in the form of entrepreneurship training groups (UKM). This technology becomes a tool to help in educating society, not a rival of the community in accordance with the principles of society 5.0.

The Purpose of this research is to build an information system application to help teachers and students in high school in learning environmental education. Angkasa Lanud Husein Sastranegara Bandung Senior High School is an adiwiyata green school project, they also have an environmental friendly institution curriculum that is environmental education. The method used for this research is descrptive method. His method is carried out by examining an existing event and analyzed by interview and observation data collection technique. The result of this research is an application of environmental learning information systems that can help the learning process of students and teachers to achieve the appropriate target material and interactive learning so that students can easily understand the contents of the material provided.

The purpose of this research is to design a web-based honorary teacher payroll information system. This research uses descriptive approach method and prototype development method and the system approach used is a structured approach. The results of this study, starting from the design to the implementation stage of the web-based honorary teacher payroll information system, can simplify the work of the treasurer in the payroll process. The treasurer is the most important part in the educational institution, because it handles the employee payroll process including the honorary teacher. But apparently the treasurer is still experiencing various obstacles such as when the process of calculating and recording salaries is still using the manual method. This can be seen in the process of calculating and recording teacher salary data which is still done in a notebook. The coordinator also experienced difficulties when recapitulating teacher absence.

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Liver cancer on CT-scan image has different shapes, locations and textures in every image. The contrast difference between abnormal and healthy liver is often indistinguishable, making it difficult to evaluate. Liver abnormalities are such as swelling, fibrosis, and the presence of benign or malignant tumor. The difference of low contrast with wide size on the image is easily known as abnormality, but it is very hard to evaluate for small mass and low contrast. In this research, CAD was conducted to help the evaluation on liver abnormality, especially abnormality in small size. The research method used was active contour-based segmentation method. The research data were secondary data, the abdomen image was produced from the modality of Computed Tomography Scanner (CT-Scan) in Regional Public Hospital of Cibinong, Bogor. The data collection techniques were through observation on the image data of abnormal liver from either liver cancer patients, normal liver patients, as well as patients of other diseases as diagnosed by the doctor. Meanwhile, the data was processed through feature extraction process using the texture analysis of Gray-Level Co-occurrence Matrix (GLCM) with machine learning of Artificial Neural Network (ANN) to detect abnormality on image. The research stated that ANN can be used to categorize the images into normal and abnormal groups at 89% accuracy, 86% sensitivity, 92% specificity, 91% precision, and 10% overall error.