The selection process of bidikmisi scholarship involves many requirements as a criterion, thus requiring completion decisions supporters with multi-criteria. One of the decision support concept with multi-citeria is VIKOR Method. The basic concept of the VIKOR Method is to determine the rank of available samples by looking at the results of utility values, regrets and distance solutions as the best alternative of each sample by weighting criteria from the Analytic Hierarchy Process Method. This research purpose is to apply the VIKOR method in selecting the recipients of bidikmisi scholarship by considering various criteria that have been determined. The results shows that the VIKOR Method can be used to assist the selection process and determine the right scholarship recipients. At the VIKOR Method, each weight that are involved shows the result rank value, so it can be used as a compromise solution in dealing with multicriteria problems.
Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classification predictions. This study aims to improve CART predictive stability and accuracy with Random Forest. The case used in this study is the classification of inaccuracies in Open University student graduation. The results of the analysis show that random forest is able to increase the accuracy of the classification of the inaccuracy of student graduation that reaches convergence with the prediction of classification reaching 93.23%.
Echocardiogram (seringkali disebut "echo") adalah garis luar grafik dari gerakan jantung. Selama tes ini, gelombang-gelombang suara frekwensi tinggi, disebut ultrasound, menyediakan gambar-gambar dari klep-klep dan kamar-kamar jantung. Dalam penelitian ini dilakukan tes terhadap 132 pasien dengan respon meninggal atau hidup. Hasil ketepatan klasifikasi antara data training dengan data testing dengan analisis diskriminan adalah 96% sedangkan dengan menggunakan SVM diperoleh sebesar 88%. Pengelompokan dengan menggunakan K-Means dan Kernel K-Means menghasilkan ketepatan pengelompokan yang sama persis. Ini menunjukkan bahwa data echocardiogram memiliki pengelompokan yang baik. Kemudian hasil pengelompokan pada K-Means dibandingkan dengan data aktual yang diklasifikasikan dengan menggunakan diskriminan, SVM dan CART dimana dihasilkan bahwa data hasil dari K-Means memiliki ketepatan klasifikasi yang lebih baik dibandingkan dengan hasil klasifikasi pada data aktual.
Supervised learning in Machine learning is used to overcome classification problems with the Artificial Neural Network (ANN) approach. ANN has a few weaknesses in the operation and training process if the amount of data is large, resulting in poor classification accuracy. The results of the classification accuracy of Artificial Neural Networks will be better by using boosting. This study aims to develop a Boosting Feedforward Neural Network (FANN) classification model that can be implemented and used as a form of classification model that results in better accuracy, especially in the classification of the inaccuracy of Terbuka University students. The results showed the level of accuracy produced by the Feedforward Neural Network (FFNN) method had an accuracy rate of 72.93%. The application of boosting on FFN produces the best level of accuracy which is 74.44% at 500 iterations
Mutual funds is another investment opportunity with a more measurable risk as well as return high enough with enough capital is affordable for the community. Mutual fund performance can be measured by several indicators.. Modeling the performance of mutual funds modeled by regression of the data panel. The regression model estimation data panel will do with the three approaches, namely the approach of common effect, fixed effects and random effects. This research purpose to know the performance of mutual funds from stock selection skill variable influences, market timing ability and level of risk with the use of panel data analysis. The results shows that the Fund's performance is affected by the stock selection skill, market timing ability, and the level of risk. Model the right approach to model the performance of mutual funds by using a random effects model.
STEM (Science, Technology, Engineering, Mathematics) approach in mathematics learning is very suitable for improving higher-order thinking skills. However, the ability of teachers to develop authentic assessment models based on the STEM approach is still low. This study aims to develop an authentic assessment model suitable for learning based on the STEM approach in mathematics at Senior High School. This study used a Research & Development design adapted from the Borg & Gall model. Data were collected using a validation sheet and a questionnaire. The data source was 269 teacher respondents selected using multistage random sampling technique, while two experts from higher education as validators and 21 senior high school math teachers experienced as panellists were determined by purposive sampling technique. Data were analysed through a quantitative approach. The product of this research is an authentic assessment model consisting of twenty project tasks. The results of the analysis show that 16 project tasks with very good quality and four project tasks with good quality, declared appropriate and in accordance with the STEM approach-based learning.
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