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All Journal International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology Jurnal Linguistik Komputasional Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) International Journal of Engagement and Empowerment (IJE2)

Afrida Helen, Afrida

Department of Informatics, School of Electrical Engineering and Informatics (STEI) Bandung Institute of Technology, Jalan Ganesha 10, Bandung, 40132

Author-ID : 564494

Humanities Computer Science & IT Education Other

Published : 6 Documents Claim Missing Document

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Automatic Abstractive Summarization Task for New Article Helen, afrida
EMITTER International Journal of Engineering Technology Vol 6, No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the statistical method and the Natural Processing Language (NLP) method.Statistical method produce Extractive summary that the summaries consist of independent sentences considered important content of document. Unfortunately, the coherence of the summary is poor. Besides that, the Natural Processing Language expected can produces summary where sentences in summary should not be taken from sentences in the document, but come from the person making the summary. So, the summaries closed to human-summary, coherent and well structured.This study discusses the tasks of generating summary. The conclusion is we can find that there are still opportunities to develop better outcomes that are better coherence and better accuracy.

Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya Putra, Berlian Juliartha Martin; Helen, Afrida; Barakbah, Ali Ridho
EMITTER International Journal of Engineering Technology Vol 6, No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Diskominfo Surabaya, as a government agency, received much community participatory for improvement of governmental services, with increasing number of 698, 2717, 4176 and 4298 participatory data respectively in 2011, 2012, 2013 and 2014. It is challenging for Diskominfo Surabaya to set a target by giving the response back within 24 hours. Due to task complexity to address the degree of participatory and to categorize the group of participatory, they faced difficulty to fulfill the target. In this research, we present a new system for measuring the sentiment degree of community participatory. We provide 5 functions in our system, which are: (1) Data Collection, (2) Data Preprocessing, (3) Text Mining, (4) Sentiment Analysis and (5) Validation. We propose our rule-based technique for the sentiment analysis of opinion mining with detection of 8 important parts, which are (1) Verb, (2) Adjective, (3) Preposition, (4) Noun, (5) Adverb, (6) Symbol, (7) Phrase, and (8) Complimentary. For applicability of our proposed system, we made a series of experiment with 410 data of community participatory in Twitter for Diskominfo Surabaya and compared with other sentiment classification algorithms which are SVM and Naive Bayes Classifier. Our system performed 77.32% rate of accuracy and outperformed to other comparing algorithms.

Co-Authors Aditya Pradana, Aditya Ali Ridho Barakbah Ayuni, Amalia Qurrota Firmansyah, Eka Kurnia Muhammad Afif Putra, Berlian Juliartha Martin Rustiman, Uus Yuliawati, Susi

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