Abstrak - Sistem pendukung merupakan cara yang dapat digunakan untuk membantu seseorang maupun perusahaan dalam mengambil keputusan. Salah satunya yaitu pengambilan keputusan dalam pemilihan supplier kain. Pemilihan supplier merupakan salah satu bagian terpenting dalam suatu usaha. Untuk mendapatkan hasil yang maksimal, dibutuhkan supplier yang terbaik dan berkualitas. Karena banyaknya supplier kain, Yani kain kesulitan dalam memilih supplier dengan kelebihannya masing-masing. Metode yang digunakan dalam pengambilan keputusan supplier kain pada Yani kain adalah dengan menggunakan metode Multi-Objective Optimazion on the basis of Ratio Analysisi (MOORA). Pada penelitian ini terdapat 5 kriteria yang ada, yaitu desain, harga, kualitas, pengiriman dan pelayanan. Setelah dilakukan perhitungan terhadap 30 sample data supplier, dilakukan pengujian menggunakan confusion matrix dengan hasil akurasi sebesar 80%. Untuk implementasi aplikasi  dibangun berbasis web.   Kata kunci : MOORA, Sistem Pendukung Keputusan, Supplier, Web, Confusion Matrix.

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Advances in Web 2.0 technology encourage the creation of personal website content involving sentiments such as blogs, tweets, web forums, and various types of social media. The Internet Movie Database (IMDb) is a website that provides information about films from around the world, including the people involved, nominations received, and reviews from visitors. The number of movies and reviews on IMDb causes users or visitors to check the reviews to find out the film rating, so it takes time for users who have no experience using IMDb. Sentiment analysis can be a solution to label positive and negative reviews. One of the algorithms used in sentiment analysis is the Support Vector Machine (SVM) algorithm. This study aimed to test the accuracy of the SVM algorithm in the classification of sentiment review films on IMDb. The tests carried out using the Support Vector Machine algorithm resulted in an accuracy value of 86.5%. The SVM algorithm can also produce a precision value of 90.67% and a recall value of 91.62%.

Higher education in Indonesia has several programs to help reduce the burden on students, one of which is through a scholarship program. Scholarships given can be obtained with the terms and conditions that apply at each university. Mitra Gama Institute of Technology is one of the private universities in the province of Riau which always runs a scholarship aid program. The problem that has been happening so far is that the procedures carried out are still using a document checking system without involving a weighting system and the right criteria and time constraints have always been an obstacle in determining scholarship recipients. This research was conducted as a solution to create an innovation in the form of making a computerized decision support system using criteria and weight values ​​so that scholarship recipients are on target. Composite performance index is the method used in this study. The purpose of this research is to create a decision support system for the selection of scholarship recipients to be more systematic and time efficient in the process. There are 5 alternatives used and 4 criteria, namely parents' income, GPA, electricity consumption and semester. The results of the research carried out were obtained the 5 highest composite index values, namely MHS4 with a value of 200.00, MHS1 with a value of 134.14, MHS5 with a value of 120.00, MHS3 with a value of 87.00 and MHS2 with a value of 85.71.

Babies are special gifts from God. Babies' age is around 0-24 months old. They experience physical changes and rapid growth along with the intake of nutritional needs. Therefore, good feeding given by their mother will influence their growth. For this reason, a mother should aware of the importance of providing complimentary food intake besides breast milk. It can be given depends on the appropriate age, its frequency, amount, texture, active, responsive and hygienic. The main discussion in this research was to determine complementary foods for babies by implementing an Expert System. The development of this expert system used the Min Max and Naive Bayes methods, which are methodologies of facts or knowledge to get a conclusion. These methods were done by searching the data application and input information to get a conclusion. Algorithm testing was done using a confusion matrix. The results of this study showed that 20 respondents with the Naive Bayes algorithm and Naïve Bayes with Min-Max were successfully provided recommendations 18 of 20 data following the results of recommendations from experts. Bayes algorithm had an accuracy value, that was 95.83%, a precision value was 95% and a recall value was 95% in providing recommendations for complementary foods.