A Performance Comparison of Algorithms On the Indonesian Tweet Comment Labeled with ITE Law
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Abstract
The presence of Twitter as an online forum causes everyone to be free to comment. This is one of the reasons the government issued the law on Electronic Information and transactions (ITE) to oversee all activities in cyberspace. However, the growing and growing amount of comment data is also increasingly difficult to analyze. Therefore, the application of data mining using the Rapid Miner application is proposed in this study to help in finding the most effective and efficient method, by looking at the accuracy, precision, and time lapse required when processing the comment data. In this study, a total of more than 12,000 data, containing comments and seven types of labels based on ITE were collected for analysis. After pre-processing the data, the researchers chose five of several classification methods, namely Naive Bayes, k-NN, Decision Tree, SVM, and Perceptron methods to be tested. From the tests that have been carried out through the Rapid Miner application, it was found that SVM became the best method used to classify comment data, with an accuracy of 55.32%. Meanwhile, the method with the lowest accuracy is occupied by Perceptron with a total accuracy of only 18.04%. Based on observations, the best accuracy results only reached 55.32% due to the large number of labels considered in the prediction.
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References
[2] Efendi, A., and Shasrini, T., “Communication Ethics of Criticism in the Public Space of Twitter Social Media,” Experimental Student Experiences, 1(5), 440-445, 2023.
[3] Hakim, L., Kusumasari, T. F., and Lubis, M.. “Text mining of UU-ITE implementation in Indonesia,” Journal of physics: conference series (Vol. 1007, No. 1, p. 012038). IOP Publishing, 2018.
[4] Hartono, P. C., and Widiantoro, A. D, “Analisis Prediksi Harga Saham Unilever Menggunakan Regresi Linier dengan RapidMiner,” Journal of Computer and Information Systems Ampera, 5(3), 174-190. 10.51519/journalcisa.v5i3.481, 2024.
[5] Nabila, Z., Isnain, A. R., Permata, P., and Abidin, Z., “Analisis data mining untuk clustering kasus covid-19 di Provinsi Lampung dengan algoritma k-means,” Jurnal Teknologi Dan Sistem Informasi, 2(2), 100-108, 2021.
[6] Nurhachita, N., and Negara, E. S., “A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students,” IAES International Journal of Artificial Intelligence, 10(2), 324, 2021.
[7] Pristiyono, Ritonga, M., Ihsan, M. A. A., Anjar, A., and Rambe, F. H., “Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm,” In IOP Conference Series: Materials Science and Engineering (Vol. 1088, No. 1, p. 012045). IOP Publishing, 2021.
[8] Rafiq, A., “Dampak media sosial terhadap perubahan sosial suatu masyarakat,” Global Komunika: Jurnal Ilmu Sosial Dan Ilmu Politik, 3(1), 18-29, 2020.
[9] Santoso, M. H., “Application of association rule method using apriori algorithm to find sales patterns case study of indomaret tanjung anom,” Brilliance: Research of Artificial Intelligence, 1(2), 54-66., 2021.
[10] Taranto-Vera, G., Galindo-Villardón, P., Merchán-Sánchez-Jara, J., Salazar-Pozo, J., Moreno-Salazar, A., and Salazar-Villalva, V., “Algorithms and software for data mining and machine learning: a critical comparative view from a systematic review of the literature,” The Journal of Supercomputing, 77, 11481-11513, 2021.
[11] Utomo, W., “The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia,” ILKOM Jurnal Ilmiah, 13(1), 31-35, 2021.