Pembuatan Kata Kunci Otomatis Dalam Artikel Dengan Pemodelan Topik

Authors

  • Lucky Adhikrisna Wirasakti Universitas Amikom Yogyakarta,Yogyakarta
  • Rony Permadi Universitas Amikom Yogyakarta,Yogyakarta
  • Anggit Dwi Hartanto Universitas Amikom Yogyakarta,Yogyakarta
  • Hartatik Hartatik Universitas Amikom Yogyakarta,Yogyakarta

DOI:

https://doi.org/10.30865/mib.v4i1.1707

Keywords:

Topic Modeling, Latent Dirichlet Allocation, K-Means, Articles, Blogging

Abstract

This study aims to determine the appropriate keywords to be used in the publication of articles on the blog, the Latent Dirichlet Allocation (LDA) Model, an opportunity model which will produce a variety of different topics. Beginning with taking data from articles / blogs, then cutting articles per section and doing data preprocessing, and changing vector data into corpus to be modeled with LDA, then grouping with K-means to search for topics with higher assistance. The results of quoting from the LDA Model obtained 4 topics with 8 words with the highest probability value, mesin (0.09375857), maksimal (0.064600445), mazda (0.10009629), varian (0.07572112), cx-8 (0.10170187), mazda (0.101048954), mobil (0.09820121), dan mazda (0.05679208)

Author Biographies

Lucky Adhikrisna Wirasakti, Universitas Amikom Yogyakarta,Yogyakarta

Fakultas Ilmu Komputer, Program Studi Informatika

Rony Permadi, Universitas Amikom Yogyakarta,Yogyakarta

Fakultas Ilmu Komputer, Program Studi Informatika

Anggit Dwi Hartanto, Universitas Amikom Yogyakarta,Yogyakarta

Fakultas Ilmu Komputer, Program Studi Informatika

Hartatik Hartatik, Universitas Amikom Yogyakarta,Yogyakarta

Fakultas Ilmu Komputer, Program Studi Informatika

References

Y. P. N. S. Chyi-Kwei, "Clustering scientific documents with topic modeling," Springer Link, vol. 100, no. 3, p. 767–786, September 2014.

A. Agustina, "Analisis Dan Visualisasi Suara Pelanggan Pada Pusat Layanan Pelanggan Dengan Pemodelan Topik Menggunakan Latent Dirichlet Allocation (LDA)," ITS, Surabaya, 2017.

I. G. A. d. H. Rosyid, "Penerapan Information Retrieval Menggunakan Pemodelan Topik Pada," in Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), Banda Aceh, 2019.

A. D. S. R. W. ,. D. S. A. E. N. E. W. A. M. N. S. "Veronica Ong, "Personality Prediction Based on Twitter Information," in Computer Science and Information Systems (FedCSIS), Prague, Czech Republic, 2017.

Muh. Fajriyanto, "Penerapan Metode Bayesian Dalam Model Latent Dirichlet Allocation Di Media Sosial," 2018. [Online]. Available: http://journal.student.uny.ac.id/ojs/index.php/math/article/view/11158/10697. [Accessed November 2019].

B. D. D. Liangjie Hong, "Empirical Study of Topic Modeling in Twitter," in First Workshop on Social Media Analytics, New York, NY, USA, 2010.

S. B. T. I. G. N. M. J. Zulhanif, "LOGIK@," 2017. [Online]. Available: http://journal.uinjkt.ac.id/index.php/logika/article/view/6474/3963.

E. R. J. C. C. D. M. a. D. A. M. Daniel Ramage, "Stanford University," 2009. [Online]. Available: http://nlp.stanford.edu/dramage/papers/tmt-nips09.pdf.

Downloads

Published

2020-01-29

Issue

Vol. 4 No. 1 (2020): Januari 2020

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).