Natural Language Processing untuk Sentimen Analisis Presiden Jokowi Menggunakan Multi Layer Perceptron
DOI:
https://doi.org/10.33633/tc.v19i3.3630Keywords:
Analisis Sentimen, Natural Language Processing, Multi-layer perceptron, Monte Carlo cross-validationAbstract
Analisis sentiment biasa digunakan untuk opini minning dalam artian memberikan sebuah identitas/label (Positif, Negatif, Neutral) kedalam data/corpus.NLP (Natural Language Processing) digunakan untuk mengolah data/corpus agar dapat dipahami/dimengerti oleh mesin atau bisa dikatakan data preprocessing/cleaning text.Teks klasifikasi digunakan untuk memproses data/corpus dimasukan kedalam model mesin klasifikasi menggunakan model Multi-layer perceptron yang nantinya akan menghasilkan sebuah prediksi persentase akurasi > 90% (lebih baik).Visualisasi data yang digunakan untuk mempresentasikan hasil dari model mesin yang merupakan supervised learning.Model selection digunakan untuk memperbaiki hasil persentase akurasi dari model mesin yang dilatih tadi ,untuk model selection bisa memakai model Monte Carlo cross-validation.Hasil pengujian pada sistem yang dibangun didapatkan nilai akurasi hingga 93,26 %.References
I. F. Rozi et al., “Analisis Mengenai Calon Presiden Indonesia 2019 Di,” pp. 27–31, 2019.
M. A. Assuja and S. Saniati, “Analisis Sentimen Tweet Menggunakan Backpropagation Neural Network,” J. Teknoinfo, vol. 10, no. 2, p. 48, 2016, doi: 10.33365/jti.v10i2.20.
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” J. Mach. Learn. Res., vol. 12, pp. 2493–2537, 2011.
G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. J. Miller, “Introduction to wordnet: An on-line lexical database,” Int. J. Lexicogr., vol. 3, no. 4, pp. 235–244, 1990, doi: 10.1093/ijl/3.4.235.
M. S. Akhtar, A. Kumar, D. Ghosal, A. Ekbal, and P. Bhattacharyya, “A Multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis,” EMNLP 2017 - Conf. Empir. Methods Nat. Lang. Process. Proc., pp. 540–546, 2017, doi: 10.18653/v1/d17-1057.
V. Singh and B. Saini, “An Effective Tokenization Algorithm for Information Retrieval Systems,” pp. 109–119, 2014, doi: 10.5121/csit.2014.4910.
A. I. Kadhim, Y. N. Cheah, and N. H. Ahamed, “Text Document Preprocessing and Dimension Reduction Techniques for Text Document Clustering,” Proc. - 2014 4th Int. Conf. Artif. Intell. with Appl. Eng. Technol. ICAIET 2014, pp. 69–73, 2015, doi: 10.1109/ICAIET.2014.21.
C.-F. Tsai, “Bag-of-Words Representation in Image Annotation: A Review,” ISRN Artif. Intell., vol. 2012, pp. 1–19, 2012, doi: 10.5402/2012/376804.
S. Al-Azani and E. S. M. El-Alfy, “Using Word Embedding and Ensemble Learning for Highly Imbalanced Data Sentiment Analysis in Short Arabic Text,” Procedia Comput. Sci., vol. 109, pp. 359–366, 2017, doi: 10.1016/j.procs.2017.05.365.
M. F. Muzakki, R. F. Umbara, F. Informatika, and U. Telkom, “Analisis Sentimen Mahasiswa Terhadap Fasilitas Universitas Telkom Menggunakan Metode Jaringan Saraf Tiruan Dan Tf-Idf,” vol. 6, no. 2, pp. 8608–8616, 2019.
A. Elmessiry, W. O. Cooper, T. F. Catron, J. Karrass, Z. Zhang, and M. P. Singh, “Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers,” JMIR Med. Informatics, vol. 5, no. 3, p. e19, 2017, doi: 10.2196/medinform.7140.
S. Rosenthal, P. Nakov, A. Ritter, and V. Stoyanov, “SemEval-2014 Task 9: Sentiment Analysis in Twitter,” 2019, [Online]. Available: http://arxiv.org/abs/1912.02990.
K. R. SPORNE, “the Phylogenetic Classification of the Angiosperms,” Biol. Rev., vol. 31, no. 1, pp. 1–29, 1956, doi: 10.1111/j.1469-185X.1956.tb01550.x.
S. Chithore, “International Journal of Advance Engineering and Research Sentiment Analysis with Multilayer Perceptron using Emoticon Space Model,” pp. 410–419, 2017.
S. Budi, “Text Mining Untuk Analisis Sentimen Review Film Menggunakan Algoritma K-Means,” Techno.Com, vol. 16, no. 1, pp. 1–8, 2017, doi: 10.33633/tc.v16i1.1263.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Nico Munasatya
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License Terms
All articles published in Techno.COM Journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This means:
1. Attribution
Readers and users are free to:
-
Share – Copy and redistribute the material in any medium or format.
-
Adapt – Remix, transform, and build upon the material.
As long as proper credit is given to the original work by citing the author(s) and the journal.
2. Non-Commercial Use
-
The material cannot be used for commercial purposes.
-
Commercial use includes selling the content, using it in commercial advertising, or integrating it into products/services for profit.
3. Rights of Authors
-
Authors retain copyright and grant Techno.COM Journal the right to publish the article.
-
Authors can distribute their work (e.g., in institutional repositories or personal websites) with proper acknowledgment of the journal.
4. No Additional Restrictions
-
The journal cannot apply legal terms or technological measures that restrict others from using the material in ways allowed by the license.
5. Disclaimer
-
The journal is not responsible for how the published content is used by third parties.
-
The opinions expressed in the articles are solely those of the authors.
For more details, visit the Creative Commons License Page:
? https://creativecommons.org/licenses/by-nc/4.0/
