Development of dance movement model using Laban movement analysis
Dance is an art product that represents aspects of religion, culture, and community tradition. Each dance movement is a combination of body movements that represent meaning. So far, students and dance teachers have learned dance movements based on rote learning, where the obstacle that arises is tha...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/28272/1/Development%20of%20dance%20movement%20model%20using%20Laban%20movement%20analysis.pdf http://eprints.utem.edu.my/id/eprint/28272/2/Development%20of%20dance%20movement%20model%20using%20Laban%20movement%20analysis.pdf |
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| Summary: | Dance is an art product that represents aspects of religion, culture, and community tradition. Each dance movement is a combination of body movements that represent meaning. So far, students and dance teachers have learned dance movements based on rote learning, where the obstacle that arises is that a dancer finds it difficult to adapt dance movements according to the meaning of the dance. A problem that often arises is that dynamic dance movements cause dancers or dance students to not be able to adjust their movements, especially since each dancer has a different body shape causing accuracy, flexibility and less optimal performance. Therefore, this research tries to solve the problem of accuracy and flexibility of dance movements by using the method and evaluation that is Laban Movement Analysis (LMA) which is the notation of dance movements created by Rudolf Laban and has become the international standard in dance notation. LMA has four variable components, namely Body, Space, Shape, and Effort, which are used in the process of evaluating movement and extracting movement data that has multidisciplinary aspects, including anatomy, kinesiology, psychology and aesthetics. LMA is widely recognized as an analysis for studying movement, meaning, and documentation of movement sequences. Dance movements have aspects of space, time, coordinates, beauty and speed, which are then carried out by the data mining process for the feature extraction process to study and evaluate the dance movement data. In this study, develop all aspects of LMA components (Body, Space, Shape, and Effort) to carry out the process of analyzing movement patterns in the feature data processing. Several studies have used aspects of LMA in analyzing dance movements, but no researcher has involved all aspects of LMA components, especially the Effort element. In this study, the researcher has conducted a study to identify dance movement patterns using all components of Body, Space, Shape, and Effort (BSpShEf) and pay attention to the composition of LMA components. After being analyzed and processed using the Hidden Markov Model (HMM) method, the average accuracy of the results exceeded 95%. This study uses two types of dance movement data, namely classical and contemporary movement data. From the results of the study, the accuracy of recognizing classical dance moves was 96.19%, while the accuracy of recognizing contemporary dance moves was 96.13%, so it can be said that this study was successful in recognizing classical and contemporary dance moves so that dancer students and dance teachers in the process of learning dance moves, especially classical dance and contemporaries do not need to bother finding the exact movement point according to the sequence and rhythm of the actual dance movements, after doing this study. |
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