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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Knowledge Engineering and Data Science Jurnal Teknik Informatika Academia Open Teknika JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Inovasi Teknologi dan Edukasi Teknik
Gunawan
Institut Sains Dan Teknologi Terpadu Surabaya
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Journal : Knowledge Engineering and Data Science

 1 
Indonesian Language Term Extraction using Multi-Task Neural Network Joan Santoso; Esther Irawati Setiawan; Fransiskus Xaverius Ferdinandus; Gunawan Gunawan; Leonel Hernandez
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p160-167

Abstract

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two multilayer perceptron neural networks to perform Part-of-Speech (PoS) labeling and Noun Phrase Chunking. Our models were trained as a joint model to solve this problem. Our proposed method, with an f-score of 86.80%, can be considered a state-of-the-art algorithm for performing term extraction in the Indonesian Language using noun phrase chunking.
Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts Gunawan Gunawan; Fitria Fitria; Esther Irawati Setiawan; Kimiya Fujisawa
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p57-68

Abstract

Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of the entire contents of the article. Which then presents it back in summary form. The steps in this research will start with the user selecting or searching for text documents that will be summarized with keywords in the abstract as a query. The proposed approach performs text preprocessing for documents: sentence breaking, case folding, word tokenizing, filtering, and stemming. The results of the preprocessed text are weighted by term frequency-inverse document frequency (tf-idf), then weighted for query relevance using the vector space model and sentence similarity using cosine similarity. The next stage is maximum marginal relevance for sentence extraction. The proposed approach provides comprehensive summarization compared with another approach. The test results are compared with manual summaries, which produce an average precision of 88%, recall of 61%, and f-measure of 70%.
Co-Authors Adriel Ferdianto Aji Wibawa Andy Hermawan Anita Guterres Edwin Pramana Esther Irawati Setiawan F.X. Ferdinandus Fitria Fitria Joan Santoso Kimiya Fujisawa Leonel Hernandez Mandar, Gamaria Mauridhi Hery Purnomo Mochamad Alfan Rosid Rachmadany, Andry Ruslan Ruslan Shandy Darmawan Suhatati Tjandra Surya Sumpeno Yosi Kristian Yuliana Melita Pranoto Zaeni, Ilham Ari Elbaith