Implementasi Metode Long Short-Term Memory (LSTM) untuk Klasifikasi Berita Online Berdasarkan Konten Teks
- Indar Kusmanto Universitas Tomakaka, Mamuju, Indonesia
-
Musliadi KH
*
Universitas Universal, Batam,
Indonesia
https://orcid.org/0000-0002-0907-4407
- Hidayat Hidayat Universitas Tomakaka, Mamuju, Indonesia
- Kristian Kristian Universitas Tomakaka, Mamuju, Indonesia (*) Corresponding Author
Abstract
This study aims to classify Indonesian-language news using the Long Short-Term Memory (LSTM) method and to evaluate its performance through accuracy, precision, recall, and F1-score metrics. The dataset consists of 48,634 news titles collected from various national and regional portals, covering five main categories: finance, travel, health, food, and sports. The research process involves several text preprocessing stages-tokenization, stop-word removal, normalization, and stemming-followed by feature representation using word embedding and the design of the LSTM model architecture. The model's performance is assessed using a confusion matrix along with additional validation through cross-validation to ensure result consistency. The LSTM model demonstrates strong performance, achieving 90% accuracy, 89% precision, 88% recall, and 89% F1-score, indicating its capability to capture semantic patterns and contextual dependencies in textual data effectively. In addition, LSTM outperforms the baseline method with a 6% increase in accuracy, reinforcing its reliability for Indonesian text classification tasks. Overall, the findings confirm that the combination of optimal preprocessing techniques and a well-designed LSTM architecture enhances the performance of the news classification system and offers significant potential for various text analysis applications in the digital information era.
Downloads
References
D. R. Tobergte and S. Curtis, “Reuters Institute Digital News Report,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1689–1699, 2013.
K. Musliadi, Z. Hazriani, and W. Yuyun, “Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm,” J. Appl. Eng. Technol. Sci., vol. 4, no. September 2020, pp. 390–399, 2022, doi: https://doi.org/10.37385/jaets.v4i1.1143.
P. Malik, R. Pandit, A. Chourasia, L. Singh, P. Rane, and P. Chouhan, “Automated Fake News Detection: Approaches, Challenges, and Future Directions,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 4, pp. 682–692, 2023.
K. I. Roumeliotis, N. D. Tselikas, and D. K. Nasiopoulos, “Fake News Detection and Classification: A Comparative Study of Convolutional Neural Networks, Large Language Models, and Natural Language Processing Models,” Futur. Internet, vol. 17, no. 1, pp. 1–29, 2025, doi: 10.3390/fi17010028.
İ. R. Karaş et al., “Fake news and misinformation: A systematic review of detection and impact studies,” J. Contemp. Soc. Sci. Educ. Stud., vol. 5, no. 2, pp. 77–87, 2025, doi: 10.5281/zenodo.16749332.
Y. Song, X. Liu, and Z. Zhou, “A Comprehensive Review of Text Classification Algorithms,” J. Electron. Inf. Sci., vol. 9, no. 2, pp. 34–42, 2024, doi: 10.23977/jeis.2024.090205.
D. Ogaga and A. Olalere, “Evaluation and Comparison of SVM, Deep Learning, and Naïve Bayes Performances for Natural Language Processing Text Classification Task,” Https://Www.Researchgate.Net/Publication/375773207, no. November, 2023, doi: 10.20944/preprints202311.1462.v1.
M. KH, Kaharuddin, and I. Syafrinal, “Diagnosing Android-Based Virus Infections in Children Using Naive Bayes,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. XI, no. 2, pp. 273–280, 2025, doi: https://doi.org/10.33330/jurteksi.v11i2.3685.
L. J. Tutika, “A Comprehensive Review of Sentiment Analysis Techniques: From Naive Bayes to LSTM-A Review,” Int. J. Res. Publ. Rev., vol. 5, no. 11, pp. 4239–4246, 2024, [Online]. Available: www.ijrpr.com
K. Taha, P. D. Yoo, C. Yeun, D. Homouz, and A. Taha, “A comprehensive survey of text classification techniques and their research applications: Observational and experimental insights,” Comput. Sci. Rev., vol. 54, no. August, p. 100664, 2024, doi: 10.1016/j.cosrev.2024.100664.
C. Liu, “Long Short-Term Memory (LSTM)-based news classification model,” PLoS One, vol. 19, no. 5 May, pp. 1–23, 2024, doi: 10.1371/journal.pone.0301835.
T. C. Praha, Widodo, and M. Nugraheni, “Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM,” Int. J. Informatics Vis., vol. 8, no. 3, pp. 1213–1221, 2024, doi: 10.62527/joiv.8.2.2126.
E. P. Widhi, D. H. Fudholi, and S. Hidayat, “Implementation Of Deep Learning For Fake News Classification In Bahasa Indonesia,” J. Res. Soc. Sci. Econ. Manag., vol. 3, no. 02, pp. 370–381, 2023, doi: 10.59141/jrssem.v3i02.546.
I. Triyadi, B. Prasetiyo, and T. L. Nikmah, “News text classification using Long-Term Short Memory (LSTM) algorithm,” J. Soft Comput. Explor., vol. 4, no. 2, pp. 79–86, 2023, doi: 10.52465/joscex.v4i2.136.
A. F. Hanifah and R. Kusumaningrum, “Non-Factoid Answer Selection in Indonesian Science Question Answering System using Long Short-Term Memory (LSTM),” Procedia Comput. Sci., vol. 179, no. 2020, pp. 736–746, 2021, doi: 10.1016/j.procs.2021.01.062.
S. Li, Z. Wang, S. Yang, X. Luo, D. He, and S. Chan, “Internet of Things intrusion detection : Research and practice of NSENet and LSTM fusion models,” Egypt. Informatics J., vol. 26, no. October 2023, p. 100476, 2024, doi: 10.1016/j.eij.2024.100476.
A. Glenn, P. LaCasse, and B. Cox, “Emotion classification of Indonesian Tweets using Bidirectional LSTM,” Neural Comput. Appl., vol. 35, no. 13, pp. 9567–9578, 2023, doi: 10.1007/s00521-022-08186-1.
M. Liang and T. Niu, “Research on Text Classification Techniques Based on Improved TF-IDF Algorithm and LSTM Inputs,” Procedia Comput. Sci., vol. 208, pp. 460–470, 2022, doi: 10.1016/j.procs.2022.10.064.
B. Zhang, C. Song, Y. Li, and X. Jiang, “Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model,” Heliyon, vol. 8, no. 11, p. e11670, 2022, doi: 10.1016/j.heliyon.2022.e11670.
W. K. Sari, D. P. Rini, and R. F. Malik, “Text Classification Using Long Short-Term Memory With GloVe Features,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 5, no. 2, p. 85, 2020, doi: 10.26555/jiteki.v5i2.15021.
J. Alghamdi, Y. Lin, and S. Luo, “Fake news detection in low-resource languages: A novel hybrid summarization approach,” Knowledge-Based Syst., vol. 296, no. May, p. 111884, 2024, doi: 10.1016/j.knosys.2024.111884.
A. Ba Alawi and F. Bozkurt, “Performance Analysis of Embedding Methods for Deep Learning-Based Turkish Sentiment Analysis Models,” Arab. J. Sci. Eng., vol. 50, no. 10, pp. 7299–7321, 2025, doi: 10.1007/s13369-024-09360-4.
B. Liu, J. Chen, R. Wang, J. Huang, Y. Luo, and J. Wei, “Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing,” 2024 5th Int. Conf. Intell. Comput. Human-Computer Interact. ICHCI 2024, pp. 281–285, 2024, doi: 10.1109/ICHCI63580.2024.10808002.
Maspiati, Nasruddin, and K. Musliadi, “Sistem Informasi Pemesanan Baju Olahraga Berbasis Web Pada Konveksi ‘ Adher ’ Tammerodo Sendana Majene,” Sinov. - Sci. Lit. Innov. Technol. J., vol. 01, no. 01, pp. 7–13, 2024.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Metode Long Short-Term Memory (LSTM) untuk Klasifikasi Berita Online Berdasarkan Konten Teks
Pages: 242-248
Copyright (c) 2026 Indar Kusmanto, Musliadi KH, Hidayat Hidayat, Kristian Kristian
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- 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.
- 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.
- 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).
