SOFT CLUSTERING USING SENTENCE EMBEDDING ON LEARNING OUTCOMES TEXT
Analysis of learning outcomes is one of the things needed to carry out continuous improvement in the curriculum used. This problem is included in the multi-label classification task. However, there is no data that has ground truth yet, and the annotation process takes quite a long time. Therefore...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/82443 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Analysis of learning outcomes is one of the things needed to carry out continuous
improvement in the curriculum used. This problem is included in the multi-label
classification task. However, there is no data that has ground truth yet, and the
annotation process takes quite a long time. Therefore, in this final assignment, soft
clustering of texts is carried out into competencies. In grouping text, sentence
embedding shows very good results compared to embedding at word level
granularity. This final assignment analyzes learning outcomes by producing a
final score obtained from the product of the weight and competency
representation value.
The weights were obtained through experiments in developing alternative soft
clustering models, namely fuzzy c-means, Gaussian mixture models and
calculating semantic similarity scores. Meanwhile, the representation of the value
of each competency is obtained by implementing the Weiszfeld algorithm.
Experimental and testing results show that the fuzzy c-means model using a
variation of the all-mpnet-base-v2 sentence embedding model shows the best
results with macro average F-1 score 0.73, micro average f-1 score 0.63, and
weighted average f-1 Score 0.69 compared to other variations. |
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