Dony Novaliendry, Adam Permana, Nurindah Dwiyani, Noper Ardi, Cheng-Hong Yang, Fadhillah Majid Saragih
The development of neural networks in the current industrial era 4.0 should help various work fields, one of which is the scientific literature. The problem that often occurs is that scientific papers still use manual sorting of themes/semantics. The purpose of this research is to build a semantic text classification application that can allow users to sort by theme/semantics by using a neural network model, Recurrent Neural Network (RNN) embedded in a smartphone. The development of this application uses the waterfall method in which there are analysis and system design. The application implements the text recognition feature of the Firebase ML Kit. It is developed using a general machine learning cycle method or approach consisting of data identification, data preparation, algorithm selection, model training, model evaluation and model deployment. The model was built using abstract data from scientific papers from the State University of Padang Library. The total data obtained 84 training data and 21 test data using a ratio of 80:20 percent to perform the validation test. The neural network model uses the AverageWordVec specification provided by TensorFlow Lite Model Maker with three classification outputs. The model validation test reached 0.7619 accuracy values with 0.7782 loss values. The model is executed using the TensorFlow Lite interpreter embedded in the application. The application results fulfill the overall system functional requirements analysis. © 2024 The authors.
Electronic Department, Universitas Negeri Padang, Padang, 25131, Indonesia; Engineering Department, Sekolah Tinggi Ilmu Pelayaran, Jakarta, 14150, Indonesia; Informatic Department, Politeknik Negeri Batam, Batam, 29461, Indonesia; Electronic Department, National Kaohsiung University Science and Technology, Kaohsiung, 807618, Taiwan
