F7231038620

Recommender System using Content Based Filtering for News Portal in Indonesia
Sri Hesti Mahanani¹, Valentinus, Dennis², Tuga Mauritsius³, Nilo Legowo^4
1Sri Hesti Mahanani Information System Management Department, BINUS Graduated Program – Master Information System Management Bina Nusantara University, Jakarta, Indonesia.
²Valentinus Information Information System Management Department, BINUS Graduated Program – Master Information System Management Bina Nusantara University, Jakarta, Indonesia.
³Dennis Information Information System Management Department, BINUS Graduated Program – Master Information System Management Bina Nusantara University, Jakarta, Indonesia.
⁴Tuga Mauritsius Information System Management Department, BINUS Graduated Program – Master Information System Management Bina Nusantara University, Jakarta, Indonesia.
⁵Nilo Legowo Information System Management Department, BINUS Graduated Program – Master Information System Management Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on January 12, 2020. | Revised Manuscript received on January 30, 2020. | Manuscript published on March 30, 2020. | PP: 173-178 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7231038620/2020©BEIESP | DOI: 10.35940/ijrte.F7231.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Nowadays there is much news on the internet. It makes the reader become information overload. The reader does not know the most important news for them. The digital era, especially in Indonesia, generated data in Bahasa very fast that referred to as big data. Data mining by process big data can collect the data insight that the reader already read. This paper proposes a new model to proceed with Bahasa news and use the TF-IDF method to collect the feature of the article. Cosine similarity from the news article used to rank the new unknown articles to recommend articles based on their preference. we can filtering the stream of information and highlight the most likely article they will read but based on their preference that we already collect implicitly from the article that they read it, it’s a scroll depth of the article they read.Then we can serve the news more personalized from what they love to read.
Keywords: Data minning, tf-idf, cosine similarity, article recommender, big data, bahasa.