Perancangan Sistem Pengenalan Tulisan Tangan pada Jawaban Esai Menggunakan Metode CNN-LSTM Berbasis Android

Elsa Apriani
Pratiwi, Nunik

METIK JURNAL
Vol. 9 No. 2 (2025): METIK Jurnal

Technological developments drive educational innovation, one of which is a handwriting recognition system to accelerate essay answer assessment. This study designs an Android application that recognizes students' handwriting using the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) methods. The system was developed using a prototyping approach through the stages of identifying needs, designing interfaces, implementing features, and testing. The evaluation results showed an accuracy of 60.37%, Character Error Rate (CER) of 16.84%, and Word Error Rate (WER) of 78.41%. Although the WER is still high, character accuracy is good enough for the early stages of development and provides a promising basis for future system improvements. Testing using Black Box Testing ensures that all features run according to their functions. This system is expected to make it easier for teachers to correct essay answers more efficiently, quickly, and consistently, as well as support the digitalization of assessment in the educational environment.