Klasifikasi Berita Televisi Menggunakan Metode K-NN, Naïve Bayes dan SVM
DOI:
https://doi.org/10.30865/jurikom.v11i6.8420Keywords:
News television, preprocessing, k-nn, naïve bayes, svmAbstract
News through television media is still one of the media that is widely used by the public in obtaining the latest information. The Central Java TVRI Public Broadcasting Institution has a news program called Berita Jawa Tengah which airs every day and doesn’t have a classification system. This research was carried out in several stages, in the initial stage preprocessing was carried out which included: data collection, cleaning, case folding, tokenizing, normalization, stopword removal, stemming, then continued with word weighting (TF-IDF) and finally applying the K-Nearest Neighbor classification method (K-NN), Naïve Bayes and Support Vector Machine (SVM). The results of the classification carried out show that the K-NN classification method has higher results compared to other methods, namely an Accuracy value of 0.94, Precision 0.92, Recall 0.94 and f1-score 0.93, so it can be concluded that Television news classification using the K-NN method is the method that provides the most accurate results.
References
N. L. R. Maha Rani, “Persepsi Jurnalis dan Praktisi Humas terhadap Nilai Berita,” Jurnal ILMU KOMUNIKASI, vol. 10, no. 1, pp. 83–96, 2013, doi: 10.24002/jik.v10i1.155.
R. M. Juwita, E. Haerani, S. K. Gusti, and S. Ramadhani, “Klasifikasi Berita Menggunakan Metode K-Nearest Neighbor,” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 5, no. 2, pp. 259–268, 2022, doi: 10.32672/jnkti.v5i2.4192.
N. Isnaini, Adiwijaya, M. S. Mubarok, and M. Y. A. Bakar, “A multi-label classification on topics of Indonesian news using K-Nearest Neighbor,” Journal of Physics: Conference Series, vol. 1192, no. 1, 2019, doi: 10.1088/1742-6596/1192/1/012027.
K. Khamar, “Short Text Classification Using kNN Based on Distance Function,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 4, pp. 1916–1919, 2013.
Nur Ghaniaviyanto Ramadhan, “Indonesian Online News Topics Classification using Word2Vec and K-Nearest Neighbor,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 6, pp. 1083–1089, 2021, doi: 10.29207/resti.v5i6.3547.
S. Li and J. Ou, “Multi-Label Classification of Research Papers Using Multi-Label K-Nearest Neighbour Algorithm,” Journal of Physics: Conference Series, vol. 1994, no. 1, 2021, doi: 10.1088/1742-6596/1994/1/012031.
Y. F. Safri, R. Arifudin, and M. A. Muslim, “K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor,” Scientific Journal of Informatics, vol. 5, no. 1, pp. 9–18, 2018.
N. Hidayati and A. Hermawan, “K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation,” Journal of Engineering and Applied Technology, vol. 2, no. 2, pp. 86–91, 2021.
A. Pamuji, “Performance of the K-Nearest Neighbors Method on Analysis of Social Media Sentiment,” JUISI, vol. 7, no. 1, pp. 32–37, 2021.
S. Uddin, I. Haque, H. Lu, M. A. Mon, and E. Gide, “Comparative performance analysis of K nearest neighbour (KNN) algorithm and its different variants for disease prediction,” scientific report, 2022, doi: https://doi.org/10.1038/s41598-022-10358-x.
H. Ma’rifah, A. P. Wibawa, and M. I. Akbar, “Klasifikasi artikel ilmiah dengan berbagai skenario preprocessing,” SAKTI, vol. 2, no. 2, pp. 70–78, 2020.
S. Kim and J. Gil, “Research paper clasification systems based on TF-IDF and LDA schemes,” human centric computing and information sciences, 2019.
Suyanto, Data Mining untuk Klasifikasi dan Klasterisasi Data, Revisi. Bandung: Informatika, 2019.
T. Rosandy, “Perbandingan Metode Naive Bayes Classifier dengan Metode Decision Tree Untuk Menganalisa Kelancaran Pembiayaan,” Jurnal TIM Darmajaya, vol. 02(01), pp. 52–62, 2016.
D. B. Setyohadi, F. A. Kristiawan, and Ernawati, “Perbaikan Performansi Klasifikasi dengan Preprocessing Iterative Partitioning Filter Algorithm,” TELEMATIKA, vol. 14, no. 1, pp. 12–20, 2017.
S. Kim and J. Gil, “Research paper clasification systems based on TF-IDF and LDA schemes,” human centric computing and information sciences, 2019.
K. Khamar, “Short Text Classification Using kNN Based on Distance Function,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 4, pp. 1916–1919, 2013.
B. S. Prakoso, D. Rosiyadi, H. S. Utama, and D. Aridarma, “Klasifikasi Berita Menggunakan Algoritma Naive Bayes Classifer Dengan Seleksi Fitur Dan Boosting,” RESTI, vol. 3, pp. 227–232, 2019.
Y. F. Safri, R. Arifudin, and M. A. Muslim, “K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor,” Scientific Journal of Informatics, vol. 5, no. 1, pp. 9–18, 2018.
Sudianto, A. D. Sripamuji, I. M. Ramadhanti, R. R. Amalia, J. Saputra, and B. Prihatnowo, “Penerapan Algorithma Support Vector Machine dan Multi-layer Percepton pada klasifikasi Topik Berita,” JANAPATI, vol. 11, pp. 84–91, 2022.
