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Sensing Trending Topics in Twitter for Greater Jakarta Area

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 1 (2017)

DOI
10.11591/ijece.v7i1.pp330-336
ISSN
2088-8708, 2088-8708
Terbit
2017-02-01
Penerbit
Institute of Advanced Engineering and Science
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

Information and communication technology grows so fast nowadays, especially related to the internet. Twitter is one of internet applications that produce a large amount of textual data called tweets. The tweets may represent real-world situation discussed in a community. Therefore, Twitter can be an important media for urban monitoring. The ability to monitor the situations may guide local government to respond quickly or make public policy. Topic detection is an important automatic tool to understand the tweets, for example, using non-negative matrix factorization. In this paper, we conducted a study to implement Twitter as a media for the urban monitoring in Jakarta and its surrounding areas called Greater Jakarta. Firstly, we analyze the accuracy of the detected topics in term of their interpretability level. Next, we visualize the trend of the topics to identify popular topics easily. Our simulations show that the topic detection methods can extract topics in a certain level of accuracy and draw the trends such that the topic monitoring can be conducted easily.

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16 penulis · 27 tautan kepenulisan bersama

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  • University of Indonesia
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Rekan penulisOrganisasiArtikel bersama
Rodia SyamwilAfiliasi tidak diketahui2
Ida FithrianiUniversity of Indonesia1
Elbert KrisonUniversity of Indonesia1
Sindy DevilaUniversity of Indonesia1
Dwi RetnoAfiliasi tidak diketahui1
Sri Endah WahyuningsihAfiliasi tidak diketahui1
Musdalifah MusdalifahAfiliasi tidak diketahui1
Ikhsani Alfi WijayantiAfiliasi tidak diketahui1
Nurul HidayahAfiliasi tidak diketahui1
Sicilia SawitriAfiliasi tidak diketahui1
Ismatul UlyaAfiliasi tidak diketahui1
Ravika Setyo AmantikaAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

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Sumber metadata: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, pertama di-harvest 27 Okt 2026, terakhir terlihat 8 Nov 2026.
Metadata sebagai oai_dc, dikonversi dari Crossref
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