In this article, we try to design the architecture of a smart notification system using an Android gadget for academic notification in college. Academic notification in colleges now utilizes bulletin boards and online media such as websites or social media. The problem faced is the high cost and resources required to deliver the academic notification. Another problem is whether the information delivered can be right to the students who need it. We proposed the architecture of a smart notification system that can reduce the cost, and the information delivered can be right on target to the students in need.
Public health center (puskesmas) as the driving force for health-minded development are responsible for improving the quality of its services through the delivery of relevant, fast, and targeted information. Likewise, the laboratory unit at the puskesmas is the frontline of the first level health care center. Especially in the Corona pandemic today, where the role of laboratories as a testing facility for Covid-19 virus testing is very important. Ease of obtaining data/information, a high level of readability, timely and minimal errors are needed. Therefore, this research has developed a lab application in puskesmas to collect, process, and present data in a structured, easy-to-read, timely, and accurate way (minimizing human error) using the Prototype model. The quality of lab application is evaluated by the ISO/IEC 25010 model and the evaluation results show that lab application meets the characteristics of ISO/IEC 25010 (valid).
The clusterization is one of methods which utilized to grouping a dataset which has a specific characteristics value. The processed data can be numerical or non-numerical data. Non-numeric data must be transformed first into numerical data. The case study in this study was to group research from six fields of science. The research data is non-numerical data is converted into the research contributions percentage in the science field. Utilized the c-means algorithm, the data was successfully grouped into three excellent research fields. The aim of the clustering is to know how many researchers in one cluster. Dataset is processed by utilizing the c-means algorithm to generated 3 clusters, they are an expeditious technology, entrepreneur and economic creative development, social engineering and strategic area infrastructure development. The data clustering result is presented in the graphic form by utilized the studio Rapidminer application.
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