Pelayanan administrasi kepada mahasiswa menjadi satu hal yang penting pada sebuah universitas. Sistem informasi dapat membantu untuk mempermudah dan mempercepat pelayanan. Salah satu jenis pelayanan administrasi yaitu pelayanan manajemen wisuda yang masih dilakukan secara manual mulai dari pengambilan formulir hingga pengumpulan pelaksanaan wisuda. Sistem informasi wisuda merupakan salah satu cara agar pelayanan wisuda dapat dilaksanakan dengan lebih efektif dan efisien. Penelitian ini merancang sebuah sistem informasi manajemen wisuda berbasis website yang dapat membantu calon wisudawan, staf Prodi, dan panitia wisuda dalam manajemen proses wisuda. Metode waterfall digunakan dalam mengembangkan perangkat lunak. Sistem dirancang menggunakan use case diagram dan rich picture diagram untuk menjelaskan interaksi antara pengguna dengan sistem. Hasil dari penelitian ini adalah sebuah sistem informasi manajemen wisuda berbasis website yang menjadikan proses manajemen wisuda lebih efektif, efisien, dan sistematis. Berbagai fitur dalam sistem informasi ini memudahkan calon wisudawan, staf Prodi, dan panitia wisuda dalam melakukan manajemen data dan informasi wisuda. Selain itu, sistem informasi ini membuat pelaporan data menjadi lebih terkontrol karena data diperbarui secara real time.

Tuberculosis (TB) is known as an infectious disease caused by bacterium Mycobacterium Tuberculosis. It is one of the highest diseases that occur in Indonesia. The lung disease can be identified by analyzing the x-ray image of the lung.The problem that followed is that the x-ray imageswere analyzed separately by the specialist physician at separate times, so the patient should consult a doctor after getting the xray image. In this study, we create a modeling design that can detect TB disease early by using artificial neural network method that is backpropagation by using Matlab Software, furthermore analyze the performance of the modeling based on the level of accuracy.In training process this system uses 441 images while for the test used 221 x-ray images.The system's phases were started with preprocessing including median filter process and histogramequalization to improve image quality. The results of preprocessing is then classified with Backpropagation algorithm through training process. The results showed that TBC detection system can be built using backpropagation method with 4400 hidden layer hidden neurons with accuracy of 81.45% from the test process result. The accuracy of NN Backpropagation is better than SVM method whose accuracy reachesof 78.73%.

The number of tourism destinations in Indonesia means that each manager is competing to be the best known and most popular destination. Ketenger Tourism Village is one of the villages that uses its natural beauty to become a tourist spot and offers a range of attractions from waterfalls to special interest tours. To become a thriving Tourism Village, professional management is needed, both facilities and infrastructure, as well as the control of the tour manager. One of the keys to successful promotion is to have a logo/brand embodies the characteristics of the area. Aside from being a promotional media, a logo is also an identity that represents the place. This study considers the logo design process for the Ketenger Tourism Village. It starts with the data collection from direct observations on locations, interviews, and data taken from digital sources. After the data is collected, a brainstorming process is carried out to produce several keywords that can later be transformed into a few rough sketches that underlie the logo. The logo chosen must be attractive, unique and able to represent the Ketenger Tourism Village and be able to be a differentiator from other tourist attractions. Keywords: logo, tourism village, ketenger

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