Design of Intelligent Model for Text-Based Fake News Detection Using K-Nearest Neighbor Method
DOI:
10.33395/sinkron.v9i1.14306Keywords:
Fake News, Detection, Machine Learning, K-NN, AccuracyAbstract
Text-based fake news detection is a crucial issue considering its negative impacts on society and individuals. One of the main impacts that has a significant and detrimental impact on society is disinformation, where false or misleading information can cause confusion and uncertainty in society. This can lead to misunderstandings and develop into riots in society which can lead to legal problems that are detrimental to society. In order to overcome this problem, a method is needed to detect fake news. This study aims to build a fake news detection method using machine learning, which is a technology widely used by researchers to detect and analyze past data. Various methods have been produced using machine learning, including the K-Nearest Neighbor (K-NN) method which is proposed as an effective solution to detect fake news. K-NN is a machine learning algorithm that works by classifying text based on its proximity to known data in feature space. This method is proposed because of its ability to handle non-linear data and its low complexity. The application of K-NN can increase the accuracy in detecting fake news by utilizing the characteristics of relevant text, thus helping in efforts to filter information and maintain the integrity of news circulating in the community. In a study conducted using the FakeNewsDetection dataset, the model evaluation results showed that KNN produced a Mean Absolute Error (MAE) of 0.011 and a Root Mean Squared Error (RMSE) of 0.077, better than the performance of other methods such as SVM and Neural Network.
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Abdullah, N. A. S., Rusli, N. I. A., & Yuslee, N. S. (2024). Development of a machine learning algorithm for fake news detection. Indonesian Journal of Electrical Engineering and Computer Science, 35(3), 1732–1743. https://doi.org/10.11591/ijeecs.v35.i3.pp1732-1743
Afrianto, N., Fudholi, D. H., & Rani, S. (2022). Prediksi Harga Saham Menggunakan BiLSTM dengan Faktor Sentimen Publik. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(1), 41–46. https://doi.org/10.29207/resti.v6i1.3676
Ahmed Medjahed, S. (2015). A Comparative Study of Feature Extraction Methods in Images Classification. International Journal of Image, Graphics and Signal Processing, 7(3), 16–23. https://doi.org/10.5815/ijigsp.2015.03.03
Alisya, S. N., Harahap, B., & Wayahdi, M. R. (2024). Design of a Website-Based Battuta University Employee Payroll System. 1(4), 16–21.
Buslim, N., & Iswara, R. P. (2019). Pengembangan Algoritma Unsupervised Learning Technique Pada Big Data Analysis di Media Sosial sebagai media promosi Online Bagi Masyarakat. Jurnal Teknik Informatika, 12(1), 79–96. https://doi.org/10.15408/jti.v12i1.11342
Egerton, T. O., Sochima, E. P., & Palimote, J. (2020). Application of Supervised Machine Learning Algorithms to Detect Online Fake News. International Journal of Computer Science and Mathematical Theory, 6(1).
Haq, M. Z., Octiva, C. S., Ayuliana, A., Nuryanto, U. W., & Suryadi, D. (2024). Algoritma Naïve Bayes untuk Mengidentifikasi Hoaks di Media Sosial. Jurnal Minfo Polgan, 13(1), 1079–1084. https://doi.org/10.33395/jmp.v13i1.13937
Indra, Agus Umar Hamdani, Suci Setiawati, Zena Dwi Mentari, & Mauridhy Hery Purnomo. (2024). Comparison of K-NN, SVM, and Random Forest Algorithm for Detecting Hoax on Indonesian Election 2024. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 13(1), 166–179. https://doi.org/10.23887/janapati.v13i1.76079
Irena, B., & Erwin Budi Setiawan. (2020). Fake News (Hoax) Identification on Social Media Twitter using Decision Tree C4.5 Method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(4), 711–716. https://doi.org/10.29207/resti.v4i4.2125
Khalis Sofi, Aswan Supriyadi Sunge, Sasmitoh Rahmad Riady, & Antika Zahrotul Kamalia. (2021). Perbandingan Algoritma Linear Regression, Lstm, Dan Gru Dalam Memprediksi Harga Saham Dengan Model Time Series. Seminastika, 3(1), 39–46. https://doi.org/10.47002/seminastika.v3i1.275
Nurhasanah, N., Sumarly, D. E., Pratama, J., Heng, I. T. K., & Irwansyah, E. (2022). Comparing SVM and Naïve Bayes Classifier for Fake News Detection. Engineering, MAthematics and Computer Science (EMACS) Journal, 4(3), 103–107. https://doi.org/10.21512/emacsjournal.v4i3.8670
Nurohanisah, S., Astuti, R., & Muhammad Basysyar, F. (2024). Deteksi Berita Palsu Menggunakan Algoritma Random Forest. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 422–428. https://doi.org/10.36040/jati.v8i1.8418
Ramadhan, N. G., Adhinata, F. D., Segara, A. J. T., & Rakhmadani, D. P. (2022). Deteksi Berita Palsu Menggunakan Metode Random Forest dan Logistic Regression. JURIKOM (Jurnal Riset Komputer), 9(2), 251. https://doi.org/10.30865/jurikom.v9i2.3979
Senhadji, S., & Ahmed, R. A. S. (2022). Fake news detection using naïve Bayes and long short term memory algorithms. IAES International Journal of Artificial Intelligence, 11(2), 746–752. https://doi.org/10.11591/ijai.v11.i2.pp746-752
Shafira, A. (2023). Hoax COVID-19 News Detection Based on Sentiment Analysis in Indonesian using Support Vector Machine (SVM) Method. International Journal on Information and Communication Technology (IJoICT), 8(2), 66–77. https://doi.org/10.21108/ijoict.v8i2.682
Tambunan, T., Yohanna, M., & Silalahi, A. P. (2023). Penerapan Metode Random Forest Dalam Mendeteksi Berita Hoax. METHOMIKA Jurnal Manajemen Informatika Dan Komputerisasi Akuntansi, 7(2), 301–306. https://doi.org/10.46880/jmika.vol7no2.pp301-306
Wayan Sumartini Saraswati, N., Putu Krisna Suarendra Putra, I., Dewa Made Krishna Muku, I., & Dana Pramitha, G. (2023). Support Vector Machine For Hoax Detection. 6(2), 107–117. https://doi.org/10.31598
Wiguna, R. A. raffaidy, & Rifai, A. I. (2021). Analisis Text Clustering Masyarakat Di Twitter Mengenai Omnibus Law Menggunakan Orange Data Mining. Journal of Information Systems and Informatics, 3(1), 1–12. https://doi.org/10.33557/journalisi.v3i1.78
Yonathan, A., Sujaini, H., & Pratama, E. E. (2022). Perbandingan Algoritma Klasifikasi dalam Pendeteksian Hoax pada Media Sosial. Jurnal Aplikasi Dan Riset Informatika (JUARA), 1(1), 44–49. https://doi.org/10.26418/juara.v1i1.53126
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