Skripsi

Optimizing indonesian-sundanese bilingual translation with adam-based neural machine translation / Anita Qotrun Nada

Abstrak
This research seeks to construct an automatic translation between Indonesian and Sundanese languages based on the Neural Machine Translation (NMT) method. The model used in this study is the Long Short-Term Memory (LSTM) type which carries out an encoder-decoder structure model learned with Bible data. The text translation here was conducted in different epochs to optimize the process followed by the Adam optimization algorithm. Testing the Adam optimizer with different epoch settings yields a BLEU score for Indonesian to Sundanese translations of 0.991785 higher than the performance of the None optimizer. Experimental results demonstrate that Indonesian to Sundanese translation using Adam optimization with 1000 epochs consistently performed better in BLEU - Bilingual Evaluation Understudy - scoring than Sundanese to Indonesian translation. Limitations of the research were also put forth particularly technical issues related to the collection of data and the Sundanese language rsquo s complex grammatical features that the model can only partially express honorifics and the problem of polysemy. Also it must be mentioned that no special hyperparameter selection was performed as parameters were chosen randomly. In future studies transformer-based models can be investigated since these architectures will better deal with complex language via their self-attention mechanism.

Informasi Detail

DDC

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Prodi

Universitas Negeri Malang. Program Studi Teknik Informatika, 2025.

Deskripsi Fisik

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Bahasa

No Reg

0003/RS/25

Edisi

Skripsi (Sarjana)--Universitas Negeri Malang. 2025

Subjek

1. BAHASA INDONESIA - TERJEMAHAN - TRANSLATING
2. BAHASA SUNDA - TERJEMAHAN - TRANSLATING
3. TERJEMAHAN - BAHASA INDONESIA - SUNDA - ADAM-BASED NEURAL MACHINE TRANSLATION - PENILAIAN

Pembimbing

1. Prof. Aji Prasetya Wibawa, S.T., M.M.T., Ph.D.

Lampiran Berkas