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Education has an important role as an effort to educate the nation. Education is the right of everyone, but not everyone can continue higher education for various reasons. One of them is limited financial and financial resources. TOPSIS is a multi-criteria dynamic strategy or elective decision which is an elective that has the littlest good ways from the positive ideal arrangement and the largest distance from the negative ideal solution from a geometric point of view using the Euclidean distance. This study aims to determine the criteria for scholarship recipients that can be implemented in a campus, school, or other social institution that provides funds for scholarship assistance. The research results are expected to serve as a reference for the decision making process. The results showed a preference value of 0.66 which is based on academic achievement

In the current conditions of each person must have a smartphone due to a lot of activities are done online. These activities can be in the form of learning, purchasing, transportation, and so forth. Smartphones offered currently have various specifications, sometimes prospective buyers feel confused to choose a smartphone as what they need. To overcome the problems in the decision of the selection of the best android smartphone that is with the decision support system using the method of Multi-Objective Optimization based on Ratio Analysis (MOORA). In this study, the data collected based on the 100 questionnaires that were distributed. The criteria used, namely random access memory (RAM), camera, price, storage capacity, battery life, and screen size. The results of the calculation obtained in this study determine each brand and type of smartphone the best android. Expected in this study can help prospective buyers who are confused in choosing the best android smartphone

In recent years, the diabetes mellitus in Indonesia has become a health problem in the community because its population has increased 2-3 times faster than other countries. Diabetes prevalence in Indonesia ranks 4th highest in the world after China, India and the United States. People can prevent complications and premature death if they detect early symptoms of diabetes. However, people do not know that they are at risk of diabetes, not had knowledge about the symptoms of diabetes, complexity of the process diagnosis and the high cost of examinations. Therefore, we need an application that can provide the results of the type of diabetes and its management solutions as practiced by experts. The aim of this research is to develop an expert system for detection types of diabetes such as: type one diabetes, type two diabetes, neuropathy diabetes, diabetes retinopathy, and diabetes nephropathy. The object of this research is diabetes carried out in March to April 2019 in the Klinik Pratama Desa Putera. This study uses primary data from patients who had a history of diabetes at Klinik Pratama Desa Putra and secondary data in the form of literature, research journals, and data documents needed to compile this study. In addition, we generated a knowledge base using forward chaining. The test results show that the expert system meets the functional requirements and the system performance reaches an accuracy of 100%. This expert system helps people in Indonesia to detect diabetes early so that it can prevent complications.

Saat ini usaha waralaba di Indonesia memiliki daya tarik yang relatif tinggi. Namun, para pelaku usaha banyak juga yang mengalami kegagalan. Bagi seseorang yang ingin memulai usaha perlu mempertimbangkan sentimen masyarakat terhadap usaha waralaba. Meskipun demikian, tidak mudah untuk melakukan analisis sentimen karena banyaknya jumlah percakapan di Twitter terkait usaha waralaba dan tidak terstruktur. Tujuan penelitian ini adalah melakukan komparasi akurasi metode Neural Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, dan Decision Tree dalam mengekstraksi atribut pada dokumen atau teks yang berisi komentar untuk mengetahui ekspresi didalamnya dan mengklasifikasikan menjadi komentar positif dan negatif. Penelitian ini menggunakan data realtime dari tweets pada Twitter. Selanjutnya mengolah data tersebut dengan terlebih dulu membersihkannya dari noise dengan menggunakan Phyton. Hasil pengujian dengan confusion matrix diperoleh nilai akurasi Neural Network sebesar 83%, K-Nearest Neighbor sebesar 52%, Support Vector Machine sebesar 83%, dan Decision Tree sebesar 81%. Penelitian ini menunjukkan metode Support Vector Machine dan Neural Network paling baik untuk mengklasifikasikan komentar positif dan negatif terkait usaha waralaba.