Skripsi

AUTOMATED ESSAY SCORING UNTUK PENILAIAN JAWABAN ESAI BAHASA INDONESIA DENGAN INDOBERT EMBEDDING DAN FEEDFORWARD NEURAL NETWORK

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Penilaian

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Improving the quality of education can be supported by a more effective assessment system, one of which is Automated Essay Scoring (AES) for automatic essay evaluation. This study develops an Indonesian-language AES system using IndoBERT Embedding and a Feedforward Neural Network (FNN). The dataset used is the secondary dataset from the UKARA Challenge, developed by the NLP Research Team at Universitas Gadjah Mada, which has a limited number of data and an imbalanced class distribution (labels 1 and 0). Overall, the developed model, after being trained and evaluated on datasets A and B, achieved an F1-score of 0.767. On dataset A, the model trained using the SMOTE technique obtained an F1-score of 0.835 with a batch size of 16, epoch 7, and a learning rate of 1.26e-4. The best model on dataset B achieved an F1-score of 0.699 with a batch size of 64, epoch 4, and a learning rate of 5.7e-3. These results indicate that IndoBERT Embedding and FNN provide a reasonably good performance compared to the baseline provided by UKARA for the training set, although challenges remain regarding data imbalance and limited dataset size.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507001467	T168660	T1686602025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1686602025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer., 2025

Collation

xv, 100 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Analisa Data

Specific Detail Info

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Statement of Responsibility

MI

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