Normalization of Abbreviation and Acronym on Microtext in Bahasa Indonesia by Using Dictionary-Based and Longest Common Subsequence (LCS)

Gunawan, Dani; Saniyah, Zurwatus; Hizriadi, Ainul

doi:10.1016/j.procs.2019.11.155

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…The amount of data tested is 400 tweets. For normalizing the Indonesian-language Twitter text, they get an accuracy value of 82% for the application of Dictionary Based, 74% for the application of the Longest Common Subsequences Algorithm only, and 84% for the combined application of the Longest Common Subsequences and Dictionary Based Algorithm so that it can be concluded that the implementation of the Dictionary based is proven help the normalization process and maximize the normalization results in the Longest Common Subsequences Algorithm [10].…”

Section: Related Workmentioning

confidence: 99%

KEBI 1.0: Indonesian Spelling Error Detection System for Scientific Papers using Dictionary Lookup and Peter Norvig Spelling Corrector

Fahrudin

Sa'diyah

Illah

et al. 2021

LKJITI

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Many Indonesian spelling errors occur in research papers published to the public, closely related to academics in all institutions such as research institutions, government, schools, and universities. The spelling errors usually writing punctuation, writing letters, writing words, writing words originating from foreign or regional languages (uptake words), using affixed words, and writing ineffective sentences. The mistakes made by the academics then become a cycle in the academic environment. They usually provide guidance for writing an undergraduate thesis, thesis, dissertations to students, or the other forms of documents and scientific papers. Therefore, the research proposed the application to facilitate all authors of scientific papers in producing quality scientific works based on the General Guidelines for Indonesian Spelling published by the Agency for Development and Language Development. The application is named KEBI 1.0 Checker (Indonesian Spelling Error 1.0 Checker), a web-based application with a built-in algorithm to detect and correct Indonesian Spelling in scientific papers. The experiment result shows that the application has given the best accuracy performance to correct the non-standard words, and typographical errors reached 100% and 55,52%, respectively. The application also has been detected 209 meaningless words. The application processing time is relatively low, the average time needed to correct non-standard words is 0.016 seconds, and typo words are 14.58 seconds. KEBI 1.0 Checker is helpful for the end-user in academics but needs to improve the vocabulary of the large corpus in various fields of science for correcting typo words.

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Section: Related Workmentioning

confidence: 99%

KEBI 1.0: Indonesian Spelling Error Detection System for Scientific Papers using Dictionary Lookup and Peter Norvig Spelling Corrector

Fahrudin

Sa'diyah

Illah

et al. 2021

LKJITI

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show abstract

“…This research used the approach of dictionary and Longest Common Subsequence (LCS). The result showed that their approach could solve the problem related to abbreviation, however, it is limited to pre-defined abbreviations and acronyms [19].…”

Section: Related Workmentioning

confidence: 99%

Improving the accuracy of text classification using stemming method, a case of non-formal Indonesian conversation

et al. 2021

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Background Stemming has long been used in data pre-processing to retrieve information by tracking affixed words back into their root. In an Indonesian setting, existing stemming methods have been observed, and the existing stemming methods are proven to result in high accuracy level. However, there are not many stemming methods for non-formal Indonesian text processing. This study introduces a new stemming method to solve problems in the non-formal Indonesian text data pre-processing. Furthermore, this study aims to improve the accuracy of text classifier models by strengthening stemming method. Using the Support Vector Machine algorithm, a text classifier model is developed, and its accuracy is checked. The experimental evaluation was done by testing 550 datasets in Indonesian using two different stemming methods. Findings The results show that using the proposed stemming method, the text classifier model has higher accuracy than the existing methods with a score of 0.85 and 0.73, respectively. These results indicate that the proposed stemming methods produces a classifier model with a small error rate, so it will be more accurate to predict a class of objects. Conclusion The existing Indonesian stemming methods are still oriented towards Indonesian formal sentences, therefore the method has limitations to be used in Indonesian non-formal sentences. This phenomenon underlies the suggestion of developing a corpus by normalizing Indonesian non-formal into formal to be used as a better stemming method. The impact of using the corpus as a stemming method is that it can improve the accuracy of the classifier model. In the future, the proposed corpus and stemming methods can be used for various purposes including text clustering, summarizing, detecting hate speech, and other text processing applications in Indonesian.

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“…Penelitian mengenai normalisasi NSW menjadi SW telah dilakukan dengan menerapkan berbagai metode. Metode konvensional berbasis leksikal telah dilakukan oleh [7] dengan terlebih dahulu mengidentifikasi jenis bahasa yang digunakan dan oleh [8] yang dikhususkan untuk microtext. Selanjutnya metode nearest neighbour digunakan dalam [9] untuk meningkatkan akurasi mormalisasi NSW.…”

Section: Pendahuluanunclassified

Nazief-Adriani Stemmer Dengan Imbuhan Tak Baku Pada Normalisasi Bahasa Percakapan Di Media Sosial

Lakonawa¹,

Mola²,

Fanggidae³

2021

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Penggunaan bahasa tak baku semakin marak dalam komunikasi di media sosial. Penggunaan bahasa tak baku tidak terbatas pada kalimat, klausa, atau frasa saja namun juga pada penggunaan kata. Pada penelitian ini, akan dilakukan normalisasi kata yang tak baku/ nonstandard word (NSW) tersebut ke kata baku/ standard word (SW) Bahasa Indonesia. Metode stemmer Nazief-Adriani (Nazief-Adriani stemmer (NAS)) dikembangkan menjadi nonstandard stemmer (NSS) dengan meningkatkan kemampuannya untuk mendeteksi imbuhan tak baku. Tujuan penelitian ini adalah membandingkan penggunaan NAS dan NSS dalam normalisasi NSW. Algoritma kemiripan Needleman-Wunsch digunakan untuk membobot hasil pencocokan. Hasil pengujian dengan Mean Reciprocal Rank (MRR) pada sebanyak 3.438 NSW didapatkan penggunaan NSS dengan jumlah kueri = 9 (Q=9) memiliki tertinggi sebesar 79.26% dengan rata-rata sebesar 50.48%. Sedangkan pengujian MRR menggunakan NAS dengan Q=9 mendapatkan hasil tertinggi sebesar 72.87% dan rata-rata sebesar 47.23%. Dari dua pengujian MRR yang dilakukan, ada 3 huruf yang memiliki hasil stemming tertinggi, baik dalam pengujian menggunakan NAS maupun menggunakan NSS yaitu huruf awal r, f dan j. Peningkatan nilai MRR paling signifikan terjadi pada huruf awal ‘d’, ‘n’ dan ‘t’ yang merupakan huruf awal dari sebagian imbuhan tak standar.

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Normalization of Abbreviation and Acronym on Microtext in Bahasa Indonesia by Using Dictionary-Based and Longest Common Subsequence (LCS)

Cited by 11 publications

References 7 publications

KEBI 1.0: Indonesian Spelling Error Detection System for Scientific Papers using Dictionary Lookup and Peter Norvig Spelling Corrector

KEBI 1.0: Indonesian Spelling Error Detection System for Scientific Papers using Dictionary Lookup and Peter Norvig Spelling Corrector

Improving the accuracy of text classification using stemming method, a case of non-formal Indonesian conversation

Nazief-Adriani Stemmer Dengan Imbuhan Tak Baku Pada Normalisasi Bahasa Percakapan Di Media Sosial

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