One of the successes in high school student learning lies in the use of learning method or model. The development of technology has given great influence on the world of today’s education. Learning media that are independent and technology-based become an attraction for students to improve learning motivation and to educate students to be able to learn independently. This study aims to develop the interactive learning media through webtoon applications as an effort to shape the character of high school students through literacy culture. This research also aims to foster high school students' interest in reading through smart phone technology. The subjects of this study were high school students. The research method used is the method of triangulation which includes data triangulation, triangulation between researchers and theory triangulation. Data collection techniques used in this studywere interviews, observation and documentation. The data obtained were then analyzed using descriptive qualitative analysis techniques. The results of this study are scientific publications and products in the form of interactive learning media in an effort to strengthen the character of high school students. Keywords: Webtoon, literacy culture, character education, interactive learning method
The hybridization between evolutionary genetic algorithm and tabu search has been proposed in this paper to address flow shop scheduling. It accommodates jobs that need to be rearranged and executed on identical machines serially. High agility is required in the manufacturing process, especially for the garment industry to be able to stand facing competitors. The manufacturing related to scheduling to deliver a product as early as possible, the tardiness, and waiting time are also concerned. A Genetic Algorithm was widely used to deal with this; which finds an optimal solution to the problems because it can obtain a more optimal solution. Unfortunately, it is easy to get stuck in optimum local (early convergence is faster). The tabu search algorithm works as a local explorer to better find and exploit the optimum local area, which can be combined with a Genetic Algorithm. This study aims to minimize the three objectives mentioned above to increase production agility. These strategies are evaluated on Taillard benchmark problems to show the significance of the proposed algorithm. The outcomes prove that the hybrid mechanism can boost the solution quality by 2.75% compared to our previous work and can resolve all of Taillard instances better. It has been proven by a 0.28% percentage relative deviation, which shows the error rate is lower and means better.
The increasing need for human interaction with computers makes the interaction process more advanced, one of which is by utilizing voice recognition. Developing a voice command system also needs to consider the user's emotional state because the users indirectly treat computers like humans in general. By knowing the type of a person's emotions, the computer can adjust the type of feedback that will be given so that the human-computer interaction (HCI) process will run more humanely. Based on the results of previous research, increasing the accuracy of recognizing the types of human emotions is still a challenge for researchers. This is because not all types of emotions can be expressed equally, especially differences in language and cultural accents. In this study, it is proposed to recognize speech-based emotion types using multifeature extraction and deep learning. The dataset used is taken from the RAVDESS database. The dataset was then extracted using MFCC, Chroma, Mel-Spectrogram, Contrast, and Tonnetz. Furthermore, in this study, PCA (Principal Component Analysis) and Min-Max Normalization techniques will be applied to determine the impact resulting from the application of these techniques. The data obtained from the pre-processing stage is then used by the Deep Neural Network (DNN) model to identify the types of emotions such as calm, happy, sad, angry, neutral, fearful, surprised, and disgusted. The model testing process uses the confusion matrix technique to determine the performance of the proposed method. The test results for the DNN model obtained the accuracy value of 93.61%, a sensitivity of 73.80%, and a specificity of 96.34%. The use of multi-features in the proposed method can improve the performance of the model's accuracy in determining the type of emotion based on the RAVDESS dataset. In addition, using the PCA method also provides an increase in pattern correlation between features so that the classifier model can show performance improvements, especially accuracy, specificity, and sensitivity.
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