Difficulty in finding information about levels maturity based on the type of fruit using data textual, make a search system using image as query needed. The concept of Content-Based Image Retrieval (CBIR) will search and display images again which are relevant based on the visual features query image. In this study an application was made to classify 5 classes of fruit, Star fruit, Mango, Melon, Banana and Tomato. Which in each class divided again into 52 subclasses consisting of the type and level of fruit maturity with a total of 5030 training data images. The method used to classify and extract images features is Convolutional Neural Network (CNN). After the image is classified, the search process is carried out to determine the fruit that is similar to the classified image. The results of the classification accuracy of 1294 images are 61%. While the retrieval of 50 images has a precision value of 88.93%.
Abstract. Any mistake in writing of a document will cause the information to be told falsely. These days, most of the document is written with a computer. For that reason, spelling correction is needed to solve any writing mistakes. This design process discuss about the making of spelling correction for document text in Indonesian language with document's text as its input and a .txt file as its output. For the realization, 5 000 news articles have been used as training data. Methods used includes Finite State Automata (FSA), Levenshtein distance, and N-gram. The results of this designing process are shown by perplexity evaluation, correction hit rate and false positive rate. Perplexity with the smallest value is a unigram with value 1.14. On the other hand, the highest percentage of correction hit rate is bigram and trigram with value 71.20 %, but bigram is superior in processing time average which is 01:21.23 min. The false positive rate of unigram, bigram, and trigram has the same percentage which is 4.15 %. Due to the disadvantages at using FSA method, modification is done and produce bigram's correction hit rate as high as 85.44 %.
Abstract. In the world of education, e-learning system is a system that can be used to support the educational process. E-learning system is usually used by educators to learners in evaluating learning outcomes. In the process of evaluating learning outcomes in the e-learning system, the form type of exam questions that are often used are multiple choice and short stuffing. For exam questions in the form of essays are rarely used in the evaluation process of educational because of the difference in the subjectivity and time consuming in the assessment process. In this design aims to create an automatic essay scoring feature on e-learning system that can be used to support the learning process. The method used in automatic essay scoring is Latent Semantic Analysis (LSA) with n-gram feature. The evaluation results of the design features automatic essay scoring showed that the accuracy of the average achieved in the amount of 78.65 %, 58.89 %, 14.91 %, 71.37 %, 64.49 % in the LSA unigram, bigram, trigram, unigram + bigram, unigram + bigram + trigram.
Abstractive Text Summarization try to creates a shorter version of a text while preserve its meaning. We try to use Recurrent Neural Network (RNN) to create summaries of Bahasa Indonesia text. We get corpus from Detik dan Kompas site news. We used word2vec to create word embedding from our corpus then train our data set with RNN to create a model. This model used to generate news. We search the best model by changing word2vec size and RNN hidden states. We use system evaluation and Q&A Evaluation to evaluate our model. System evaluation showed that model with 6457 data set, 200 word2vec size, and 256 RNN hidden states gives best accuracy for 99.8810%. This model evaluated by Q&A Evaluation. Q&A Evaluation showed that the model gives 46.65% accurary.
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