125 research outputs found
Application of Data Mining for Prediction of High School Student Graduation Rates
No full textThe implementation of Data Mining in the education sector aims to develop methods that are able to discover valuable knowledge from data generated in the educational environment. This can be used to increase learning efficiency by paying more attention to students who are predicted to have low grades. However, in its application, each algorithm shows different performance depending on the attributes and dataset used. In this study, a dataset of semester grades and final school exam scores was used. Some of the prediction techniques used are decision trees, support vector machines, and neural networks. Of the four scenarios for the science major at SMAN 2 and SMAN 3 Pangkalpinang with 3 different models, the Mean Squared Error value shows that the test results are in accordance with the testing dataset and can be used as predictions of students' final grades, namely the decision tree model and support vector machine. For the Social Sciences major at SMAN 2 and SMAN 3 Pangkalpinang with 3 different models, the Mean Squared Error value shows that the test results are in accordance with the testing dataset and can be used as a prediction of students' final grades, namely the support vector machine model
Predictive business intelligence dashboard for food and beverage business
No full textThis research was conducted to provide an example of predictive business intelligence (BI) dashboard implementation for the food and beverage business (businesses that sell fast-expired goods). This research was conducted using data from a bakery's transactional database. The data are used to perform demand forecasting using extreme gradient boosting (XGBoost), and recency, frequency, and monetary value (RFM) analysis using mini batch k-means (MBKM). The data are processed and displayed in a BI dashboard created using Microsoft Power BI. The XGBoost model created resulted in a root mean square error (RMSE) value of 0.188 and an R2 score of 0.931. The MBKM model created resulted in a Dunn index value of 0.4264, a silhouette score value of 0.4421, and a Davies-Bouldin index value of 0.8327. After the BI dashboard is evaluated by the end user using a questionnaire, the BI dashboard gets a final score of 4.77 out of 5. From the BI dashboard evaluation, it was concluded that the predictive BI dashboard succeeded in helping the analysis process in the bakery business by: accelerating the decision-making process, implementing a data-driven decision-making system, and helping businesses discover new insights
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Web-Based Evaluation System Using Kirkpatrick Model for High School Education (A Case Study for Vocational High School in Jakarta)
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