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All Journal International Journal of Electrical and Computer Engineering Indonesia Medicus Veterinus Perfecting a Video Game with Game Metrics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Akbar, Saiful
Data And Software Engineering Research Group School Of Electrical Engineering And Informatics, Institut Teknologi Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Medicine & Pharmacology Veterinary

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Journal : International Journal of Electrical and Computer Engineering

 1 
Enhancing Performance in Medical Articles Summarization with Multi-Feature Selection Susetyo Bagas Bhaskoro; Saiful Akbar; Suhono Harso Supangkat
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.487 KB) | DOI: 10.11591/ijece.v8i4.pp2299-2309

Abstract

The research aimed at providing an outcome summary of extraordinary events information for public health surveillance systems based on the extraction of online medical articles. The data set used is 7,346 pieces. Characteristics possessed by online medical articles include paragraphs that comprise more than one and the core location of the story or important sentences scattered at the beginning, middle and end of a paragraph. Therefore, this study conducted a summary by maintaining important phrases related to the information of extraordinary events scattered in every paragraph in the medical article online. The summary method used is maximal marginal relevance with an n-best value of 0.7. While the multi feature selection in question is the use of features to improve the performance of the summary system. The first feature selection is the use of title and statistic number of word and noun occurrence, and weighting tf-idf. In addition, other features are word level category in medical content patterns to identify important sentences of each paragraph in the online medical article. The important sentences defined in this study are classified into three categories: core sentence, explanatory sentence, and supporting sentence. The system test in this study was divided into two categories, such as extrinsic and intrinsic test. Extrinsic test is comparing the summary results of the decisions made by the experts with the output resulting from the system. While intrinsic test compared three n-Best weighting value method, feature selection combination, and combined feature selection combination with word level category in medical content. The extrinsic evaluation result was 72%. While intrinsic evaluation result of feature selection combination merger method with word category in medical content was 91,6% for precision, 92,6% for recall and f-measure was 92,2%.
Co-Authors Anindya Prameswari Arifiansyah, Fitra Benhard Sitohang Benhard Sitohang Fitra Arifiansyah Hery Heryanto Ida Bagus Kade Suardana Ida Bagus Komang Ardana Inne Gartina Husein Irfan Amal Jasman Pardede Kurniandha Sukma Yunastrian Kurniandha Yunastrian Marsa Ahmada Suhono Harso Supangkat Susetyo Bagas Bhaskoro Susetyo Bagas Bhaskoro Yunastrian, Kurniandha Sukma