Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students’ on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students’ graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.
The process for analyzing and extracting useful information from a large database that employs one or more machine learning techniques is Data Mining. There are many data mining methods that can be used in a variety of data patterns. One of them is prediction modeling. This study compares several data mining performance methods for prediction such as Naïve Bayes, Random Tree, J48, and Rough Set to get the most powerful classifier to extract the knowledge of air pollution data. The parameters being used for observation in the performance of the prediction methods are correctly and incorrectly classified instances, the time taken, and kappa statistic. The experimental result reveals that Rough Set is extremely good for classifying the Air Pollutant Index (API) data from Malaysia and Singapore. Rough Set has the lowest error and the highest performance compared to other methods with the accuracy more than 97%.
The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.
Determination of the right food crops needs to be done to improve the community's economy in the agricultural sector. The use of traditional cropping patterns needs to be changed by utilizing information technology. The utilization of data from local governments can be used to assist in providing recommendations for types of food crops by processing them with several data mining methods. This method can extract information to find patterns and knowledge from the data. The classification method approach is used as a grouping of data based on data attachment to sample data. This study uses several classification methods, namely Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Neural Network, Random Tree, Random Forest, dan K Nearest Neighbor (KNN). These methods were successfully compared to find out which method is the best to help recommend appropriate and accurate food crops based on the results of the classification performance of each method. Random Tree was chosen as the best method for the results of this performance comparison using discretization and normalization methods at the pre-processing stage of the data. It can be seen based on the results of the Accuracy, Precision, Recall, and F1-Score values on the use of discretization of 98%, respectively. Meanwhile, normalization showed that the results of the Accuracy, Precision, Recall, and F1-Score values are 99%, respectively.
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