Tukino Paryono, Eko Sediyono, Hendry, Baenil Huda, April Lia Hananto, Aviv Yuniar Rahman
The assessment document at the time of study program accreditation shows performance achievements that will have an impact on the development of the study program in the future. The description in the assessment document contains unstructured data, making it difficult to identify target indicators. Apart from that, the number of Indonesian-based assessment documents is quite large, and there has been no research on these assessment documents. Therefore, this research aims to classify and predict target indicator categories into 4 categories: deficient, enough, good, and very. Learning testing of the Indonesian language assessment sentence classification model using recurrent neural networks-long short-term memory (RNN-LSTM) using 5 layers and 3 parameters produces performance with an accuracy value of 94.24% and a loss of 10%. In the evaluation with the Adamax optimizer, it had a high level of accuracy, namely 79%, followed by stochastic gradient descent (SGD) of 78%. For the Adam optimizer, Adadelta, and root mean squared propagation (RMSProp) have an accuracy rate of 77%. © 2024, Institute of Advanced Engineering and Science. All rights reserved.
Department of Computer Science, Faculty of Information Technology, Universitas Kristen Satya Wacana, Salatiga, Indonesia; Department of Information System, Faculty of Computer Science, Universitas Buana Perjuangan, Karawang, Indonesia; Department of Informatics Engineering, Faculty of Engineering, Universitas Widyagama Malang, Malang, Indonesia; Department of Information & Communication Technology, School of Graduate Studies, Asia e University, Selangor, Malaysia
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