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All Journal PROFIT : Kajian Pendidikan Ekonomi dan Ilmu Ekonomi Jurnal FishtecH Jurnal ULTIMATICS Proceedings of the International Conference on Applied Science and Health Jurnal Ilmu Keperawatan Jiwa Indonesian Journal of Nursing Research (IJNR) Jurnal Ilmiah Wijaya Warta AKAB Ikhtisar: Jurnal Pengetahuan Islam IT For Society : Journal of Information Technology Student Research Journal
Rosalina Rosalina
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences

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Journal : Jurnal ULTIMATICS

 1 
Improving Multi-Document Summarization Performance by Utilizing Comprehensive Document Features Rosalina Rosalina
Ultimatics : Jurnal Teknik Informatika Vol 8 No 1 (2016): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (588.078 KB) | DOI: 10.31937/ti.v8i1.500

Abstract

The rapid growth of information technology and communication technology makes the volume of information available on the web increase rapidly. This development is leading to information overload. Multidocument summarization appears as a way to resolve the information overload problem in an effective way. In order to improve the performance of the multi-document summary this research combined the sentence features: sentence centroid, sentence position, sentence length and IsTheLongestSentence value to weight the sentences in order to find the most informative information of a text. In addition, this research uses a new method to calculate the weight of sentence position feature. The performance of the research result was evaluated using ROUGE metrics: ROUGE-N, ROUGE-L, ROUGE-W, ROUGE-S, and ROUGE-SU. The research result outperform MEAD system if it was evaluated using the dataset of cluster D133C and D134H and if it was evaluated using ROUGE-1, ROUGE-S and ROUGE SU for cluster D133C and ROUGE-2, ROUGE-3, ROUGE-4, ROUGE-L and ROUGE-W for cluster D134H. This shows that the research result captures the important words in the extracted summary and it generates longer sentences as longer sentence contains more material that would match the one in the reference summaries. Index Terms— multi-document summarization, document features, centroid based summarization
Aplikasi Pemantauan Media Sosial untuk Analisa Merek Rikip Ginanjar; Rosalina Rosalina; Aldo Wijaya
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.1745

Abstract

Abstract— In recent years, micro-blogs on the Internet have become a popular way of expressing feelings, thoughts, and even communicating opinions about products and services that are common among its users. Collecting user opinions can be an expensive and time-consuming task using conventional methods such as surveys. The sentiment analysis of the customer opinions makes it easier for businesses to understand their competitive value in a changing market and to understand their customer views about their products and services. In this research, Lexicon-Based approach especially AFINN lexicon is implemented to classify user twitter sentiment, throughout which, twitter Micro-blogs data has been collected, pre-processed analyzed, and classified. The results of this research is an android application that could classify users' perspective via tweets into positive and negative, which is represented in a pie chart for Monthly report. Index Terms— Sentiment Analysis, Brand Analysis, Twitter, Android Application
Co-Authors Abdul Wakhid Ace Baehaki Adela Fizka Pr Aldo Wijaya Dian Febri Sarwendah Dian Nirmala Sari Eko Supriadi fitriyanti fitriyanti Herpandi . M. Zhafran Zharif Amrin Rikip Ginanjar Sukarno Sukarno Triyono Triyono Uci Suraitha Karina Br Sitepu Wiwik Nurhikmah Yulia Djahir Yunita Galih Yudanari