Story Generator Bahasa Indonesia dengan Skip-Thoughts - Mustofa, M; Fudholi, Dhomas Hatta (STIKOM Tunas Bangsa Pematangsiantar, 2022)
Currently, there are many studies that want computers to be able to imitate human creativity in stringing words into writing like a writer. This study aims to use the RNN algorithm to produce automatic story writing in Indonesian. The main contribution in this research is the creation and evaluation of the RNN algorithm based on the skip-thoughts model using an Indonesian language dataset. The skip-thoughts model consists of an encoder in the form of single GRU layer with 500 hidden units, and two decoders with single GRU layer each with 500 hidden units. The function of the encoder is to do the word mapping process from the input sentence, while the decoder predicts the sentence before (previous decoder) and the sentence after (next decoder) from the input sentence. The dataset used in the model training is in the form of stories in Indonesian with the genres of folklore and short stories. The model training process is run in 100 epochs, using the ADAM optimizer to get the optimal model. Based on the results of the assessment of respondents who have a background as writers, the folklore model shows a fairly good rating (average score of 65) for the S-P-O-K criteria, and a low rating for criteria of linkage between sentences (average score of 38) and the context of the whole story (average score of 32). The short story of life model shows a good rating (average score of 73) for the S-P-O-K criteria, and a low rating for the linkage between sentences criteria (average score of 48), and the context of the whole story (average score of 42). Based on the results of the assessment, the skip-thoughts model used in the Indonesian story generator has worked well, but it can still be improved by increasing the number of training datasets for each story genre used, as well as being more specific in determining the genre in order to obtain story integrity better.