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All Journal Jurnal Pilar Nusa Mandiri EXPLORE Jurnal Computer and Technology
Tuhpatussania, Siti
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Computer Science & IT

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Journal : Jurnal Pilar Nusa Mandiri

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COMPARISON OF LEXRANK ALGORITHM AND MAXIMUM MARGINAL RELEVANCE IN SUMMARY OF INDONESIAN NEWS TEXT IN ONLINE NEWS PORTALS Tuhpatussania, Siti; Utami, Ema; Hartanto, Anggit Dwi
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3190

Abstract

The presence of online media has shifted print media for news readers to get information that is fast, accurate, and easy to access. However, the problem arises because the length of the news text makes the reader bored to search for the news as a whole so the news that is obtained will be less accurate. For this reason, it is necessary to have an automatic text summary that was raised in this study as well as to compare the Maximum Marginal Relevance (MMR) algorithm and the LexRank algorithm to the summary of Indonesian news texts on the online news portal graphanews. com. the results of the comparison test of text summarization using f-measure , precision and recall show the performance of text summarization with the MMR algorithm is better where f-measure is 91.65%, precision is 91.08% and recall is 92.23%.
COMPARISON OF PORTERS STEMMING ALGORITHM AND NAZIEF & ADRIANI'S STEMMING ALGORITHM IN DETERMINING INDONESIAN LANGUAGE LEARNING MODULES Tuhpatussania, Siti; Utami, Ema; Hartanto, Anggit Dwi
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3940

Abstract

One of the methods used to improve the performance of text summarization to obtain complete information in a learning module is by transforming the words in a module into basic words or, in other words, through a steaming process. The steaming process in Indonesian language texts is more complicated/complex because there are word affixes that must be removed to get the root word (root word) of a word, so this research will compare the two stemming algorithms of Porter and stemming Nazief & Adriani in the learning module at Mataram University of Technology. The test results of the Nazief & Adriani stemming algorithm on an average process duration of 51.8 seconds with an average accuracy of 74.175%. In Porter's Algorithm, the average processing time is 16.875 seconds, with an accuracy of 73.225%.
Co-Authors Anggit Dwi Hartanto, Anggit Dwi Ema Utami Erniwati, Surni Mutaqin, Zaenul