Machine translation of Indonesian: a review

Septarina, Amalia Agung; Rahutomo, Faisal; Sarosa, Moechammad

doi:10.21924/cst.4.1.2019.104

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…Ontology explains concepts and relations between one domain with another domain [40], [41], [49], [50], [51], [52], [53]. Therefore, it makes the knowledge exploration process related to the Covid-19 handling easier and faster.…”

Section: The Materials and Methodsmentioning

confidence: 99%

A Knowledge Management System Conceptual Model for the Sorong COVID-19 Task Force

Rumetna

Lina

Lopulalan

2020

JOIV : Int. J. Inform. Visualization

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Sorong is a city located in the West Papua Province, Indonesia. It has a strategical location because it serves as the gateway of the West Papua Province. In 2019, its population has reached 247.084. Such condition has been deemed risky during the Covid-19 pandemic. The performance of the Sorong City Task Force for Covid-19 handling acceleration (Satgas Covid-19) had been considered ineffective by the citizens because it had not provided a system which would provide valid information regarding the Covid-19 progress in the city, resulting in worry and panic buying among the citizens. To overcome the problem, the knowledge possessed by the Sorong City Covid-19 Task Force should be developed into a Knowledge Management System (KMS). The implementation of the system had been expected to be able to optimize the documentation, dissemination and creation of knowledge as well as its evaluation. The KMS then was built to improve the performance effectiveness of the Sorong City Covid-19 Task Force in managing and disseminating knowledge regarding Covid-19 to the citizens. The methods utilized in this research were the qualitative method and the 10-step knowledge management roadmap. The research produced a conceptual model of KMS which could be utilized by the Sorong City Covid-19 Task Force.

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Section: The Materials and Methodsmentioning

confidence: 99%

A Knowledge Management System Conceptual Model for the Sorong COVID-19 Task Force

Rumetna

Lina

Lopulalan

2020

JOIV : Int. J. Inform. Visualization

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“…Both of them can be used to translate one languge to other languge. However, Google Translate can tranlate over than 90 languages and U-dictionary can only translate over than 30 languages (Septarina et al, 2019).…”

Section: Machine Translationmentioning

confidence: 99%

Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Sipayung

Sianturi

Arta

et al. 2021

Elsya

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Better translation produced by computation linguistics should be evaluated through linguistics theory. This research aims to describe translation techniques between Google Translate and U-Dictionary. The study used a qualitative research method with a descriptive design. This design was used to describe the occurrences of translation techniques in both translation machine, with the researchers serving as an instrument to compare translation techniques which is produced on machine. The data are from expository text entitled “Importance of Good Manners in Every Day Life”. The total data are 122 words/phrases which are pairs of translations, English as source language and Indonesia as target language. The result shows that Google Translate apply five of Molina & Albir’s (2002) eighteen translation techniques, while U-dictionary apply seven techniques. Google Translate dominantly apply literal translation techniques (86,8%) followed by reduction translation techniques (4,9%). U-dictionary also dominantly apply literal translation techniques (75,4%), but follows with the variation translation techniques (13,1%). This study showed that both machines produced different target texts for the same source language due to different applications of techniques, with U-dictionary proven to apply more variety of translation techniques than Google Translate. The researcher hopes this study can be used as an evaluation for improving the performance of machine translations.

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“…Metode interlingua tidak perlu melewati langkah transfer karena representasi dari kalimat diasumsikan sama. Sistem ini tidak dapat menangani ambiguitas, maka setelah proses editing perlu disesuaikan dengan gaya penulisan dan target audiens [5].…”

Section: Rule-based Machine Translationunclassified

Perbandingan Translation Library Pada Python (Studi Kasus: Analisis Sentimen Penyakit Menular Di Indonesia)

Sari,

Agus Dwi Suarjaya,

Wira Buana

2021

jitter

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Era saat ini pengelolaan data dalam jumlah besar dan kompleks ini perlu adanya penguraian data dengan fitur terjemahan bahasa agar lebih efisien. Penggunaan machine translation dapat menerjemahkan data secara otomatis dari satu bahasa ke bahasa lainnya. Pendekatan machine translation yang diusulkan yaitu penggunaan library pada Python seperti deep-translator, textblob, dan goslate. Pada penelitian analisis sentiment penyakit menular terdapat tahapan pengolahan analisa yang di dalamnya terdapat proses translate data tweet. Hasilnya mengungkapkan bahwa ketiga library yang dibandingkan dapat digunakan untuk melakukan translation data, tetapi dapat dipilih dan disesuaikan dengan keperluan. Penggunaan library yang paling baik dihasilkan library deep-translator yang memiliki access free dengan unlimited translation dan support features yang lengkap serta dapat mendeteksi kata typo lalu diterjemahkan merujuk pada arti yang sesungguhnya, maka pada studi kasus analisis sentiment penyakit menular menggunakan library deep-translator untuk mengolah jumlah data tweet yang didapat saat crawling data.

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Machine translation of Indonesian: a review

Cited by 7 publications

References 10 publications

A Knowledge Management System Conceptual Model for the Sorong COVID-19 Task Force

A Knowledge Management System Conceptual Model for the Sorong COVID-19 Task Force

Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Perbandingan Translation Library Pada Python (Studi Kasus: Analisis Sentimen Penyakit Menular Di Indonesia)

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