Analisa Sentimen Pada Media Sosial “X” Pencarian Keyword ChatGPT Menggunakan Algoritma K-Nearest Neighbors (KNN)
DOI:
https://doi.org/10.35870/jimik.v5i3.1016Keywords:
K-Nearest Neighbors, Artificial Intelligence, ChatGPT, XAbstract
Sentiment analysis of the use of Artificial Intelligence (AI) is becoming increasingly important in public understanding of today's rapidly evolving technology, as it helps facilitate human activities. One of the key applications is the presence of ChatGPT, an AI capable of interacting with users through user input, such as answering various questions posed. This topic generates a lot of pros and cons, as widely discussed on social media. Research is needed to evaluate how wisely people use this AI. This study proposes an approach using the K-Nearest Neighbors (KNN) algorithm to analyze AI-related sentiment. The KNN algorithm is used to classify sentiment into positive, negative, or neutral, based on the similarity with the closest word in the feature space derived from text data. This method allows for efficient sentiment grouping without the need for complex models. Researchers chose sentiment analysis because it is an appropriate technique for data processing. Of the 1000 reviews collected from social media users on “X,” 853 were positive, and 147 were negative. The data was classified using the KNN algorithm, followed by an accuracy evaluation yielding 84.80%. The results of this sentiment analysis are expected to guide decision-makers in developing and applying AI technology more intelligently, in line with societal needs and expectations.
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Al Hadiq, M. F., & Ramadhan, C. U. (2023). Pengaruh model pembelajaran berbasis investigasi dengan dukungan ChatGPT terhadap keterampilan literasi digital siswa sekolah dasar. COLLASE (Creative of Learning Students Elementary Education), 6(6), 1187-1193. https://doi.org/10.22460/collase.v6i6.21673.
Amalia, S., Karomatan, T., & Ridwan, W. I. (2023). Pengaruh Penggunaan Chat Gpt Sebagai Content Creation Dalam Membangun Persepsi Konsumen Terhadap Strategi Pemasaran Umkm. Jurnal Ilmiah Publika, 11(2), 525-533.
Anuar, S., Muda, M. S., & Mat, A. C. (2024). Penggunaan Kecerdasan Buatan Pelajar Kolej Komuniti Kemaman. International Journal of Advanced Research in Education and Society, 6(1), 225-238. https://doi.org/10.55057/ijares.2024.6.1.19
Auna, H. S. A., & Hamzah, N. (2024). Studi Perspektif Siswa Terhadap Efektivitas Pembelajaran Matematika Dengan Penerapan Chatgpt. HINEF: Jurnal Rumpun Ilmu Pendidikan, 3(1), 13-25. https://doi.org/10.37792/hinef.v3i1.1160.
Dani, R. W. (2023, December). Analisis Peran Artificial intelligence (AI) Dalam Penulisan karya Ilmiah Pada ChatGPT-3, 5 dari OpenAI. In Proceeding of International Seminar on Adab and Humanities (Vol. 5, No. 1, pp. 187-200).
Darman, R. (2024). Peran ChatGPT sebagai artificial intelligence dalam menyelesaikan masalah pertanahan dengan metode studi kasus dan black box testing. Tunas Agraria, 7(1), 18–46. https://doi.org/10.31292/jta.v7i1.256
Dewi, M. N., & Putra, R. E. (2024). Pengembangan Sistem Analisis Sentimen Masyarakat Terhadap Chatgpt Pada Twitter Dengan Perbandingan Metode Naive Bayes Classifier Dan K-Nearest Neighbors. Journal of Informatics and Computer Science (JINACS), 5(03), 344-357. https://doi.org/10.26740/jinacs.v5n03.p344-357.
Fahriza, M. N., & Riza, N. (2023). Analisis Sentimen pada Ulasan Aplikasi Chat Generative Pre-Trained Transformer GPT menggunakan Metode Klasifikasi K-Nearest Neighbor (KNN). JATI (Jurnal Mahasiswa Teknik Informatika), 7(2), 1351-1358. https://doi.org/10.36040/jati.v7i2.6767.
Gaede, F. (2006). Marlin and LCCD—Software tools for the ILC. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 559(1), 177-180. https://doi.org/10.1016/j.nima.2005.11.138
Handoyo, E. R., Sugiarto, J., Lolo, A., & Chai, K. (2023). Identifikasi Pengaruh Penggunaan ChatGPT terhadap Kemampuan Berfikir Mahasiswa di Universitas Atma Jaya Yogyakarta Prodi Sistem Informasi Angkatan 2021. KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, 3(2), 342-352. https://doi.org/10.24002/konstelasi.v3i2.7241.
Irwansyah Saputra, D. A. K. (2022). Machine learning Untuk Pemula. Informatika Bandung.
Maula, S. R., Aprillian, S. D., Rachman, A. W., & Azman, M. N. M. (2023). Ketergantungan mahasiswa Universitas Jember terhadap artificial intelligence (AI). ALADALAH: Jurnal Politik, Sosial, Hukum dan Humaniora, 2(1), 1–14. https://doi.org/10.59246/aladalah.v2i1.608
Nur, M., Suryono, R. N., Bhagaskara, R. E., Pratama, M. A., & Pratama, A. (2023). Prosiding Seminar Nasional Teknologi dan Sistem Informasi (SITASI) 2023 Surabaya.
Oktavia, I., & Isnain, A. R. (2024). Analisis sentimen opini terhadap tools artificial intelligence (AI) berdasarkan Twitter menggunakan algoritma Naïve Bayes. Jurnal Media Informatika Budidarma. https://doi.org/10.30865/mib.v8i2.7524
Pakpahan, R. (2021). Analisa pengaruh implementasi artificial intelligence dalam kehidupan manusia. Journal of Information System, Informatics and Computing Issue Period, 5(2), 506–513. https://doi.org/10.52362/jisicom.v5i2.616
Pratama, A. D., & Hendry, H. (2024). Analisa sentimen masyarakat terhadap penggunaan ChatGPT menggunakan metode support vector machine (SVM). JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 9(1), 327–338. https://doi.org/10.29100/jipi.v9i1.4285
Pujiono, I. P., Rachmawanto, E. H., & Hana, F. M. (2024). Pengaruh Asisten Virtual Berbasis Artificial Intelligence Terhadap Integritas Sertifikasi Kompetensi Pemrograman secara Online. JURNAL KRIDATAMA SAINS DAN TEKNOLOGI, 6(01), 34-46. https://doi.org/10.53863/kst.v6i01.1052.
Purbayanto, B., & Suharsono, T. N. (2023). Analisis Sentimen Pengguna X terhadap Chatgpt dengan Algoritme Naive Bayes. Jurnal Telematika, 18(2), 63-71. https://doi.org/10.61769/telematika.v18i2.614.
Purnama, B., & Iwan, S. (2021). Implementasi artificial intelligence dan machine learning. Bandung: Informatika Bandung.
Purnama, B., & Sofana, I. (2021). Implementasi artificial intelligence dan machine learning. Bandung: Informatika Bandung.
Rahayu, S., Purnama, J. J., Hamid, A., & Hikmawati, N. K. (2023). Analisis Sentimen AicoGPT (Generative Pre-trained Transformer) Menggunakan TF-IDF. Jurnal Buana Informatika, 14(02), 97-106. https://doi.org/10.24002/jbi.v14i02.7039
Rifaldi, M. I., Ramadhan, Y. R., & Jaelani, I. (2023). Analisis Sentimen Terhadap Aplikasi Chatgpt Pada Twitter Menggunakan Algoritma Naïve Bayes. J-SAKTI (Jurnal Sains Komputer dan Informatika), 7(2), 802-814. http://dx.doi.org/10.30645/j-sakti.v7i2.687.
Risnina, N. N., Permatasari, S. T. I., Nurulhusna, A. Z., Anjelita, F. M., Wulaningtyas, C., & Rakhmawati, N. A. (2023). Pengaruh ChatGPT terhadap proses pembelajaran mahasiswa di Institut Teknologi Sepuluh Nopember. Jurnal Pendidikan, Bahasa dan Budaya, 2(4), 119–132. https://doi.org/10.55606/jpbb.v2i4.2364
Saepudin, A., Aryanti, R., Fitriani, E., Royadi, R., & Ardiansyah, D. (2024). Analisis Sentimen Pemanfaatan Artificial Intelligence di Dunia Pendidikan Menggunakan SVM Berbasis Particle Swarm Optimization. Computer Science (CO-SCIENCE), 4(1), 71-79. https://doi.org/10.31294/coscience.v4i1.2921
Sakti, E. M. S., & Herwanto, A. (2023). Pelatihan Penggunaan Chatgpt Terhadap Minat Baca Siswa SMA Muhammadiyah 1 Jakarta. Jurnal Edukasi dan Multimedia, 1(3), 7-12. https://doi.org/10.37817/jurnaledukasidanmultimedia.v1i3
Saraswati, A. R., Karmina, V. A., Efendi, M. P., Candrakanti, Z., & Rakhmawati, N. A. (2023). Analisis pengaruh ChatGPT terhadap tingkat kemalasan berpikir mahasiswa ITS dalam proses pengerjaan tugas. Jurnal Pendidikan, Bahasa dan Budaya, 2(4), 40–48. https://doi.org/10.55606/jpbb.v2i4.2223
Setiawan, D., Karuniawati, E. A. D., & Janty, S. I. (2023). Peran Chat Gpt (Generative Pre-Training Transformer) Dalam Implementasi Ditinjau Dari Dataset. Innovative: Journal Of Social Science Research, 3(3), 9527-9539. https://doi.org/10.31004/innovative.v3i3.3286.
Sobari, A. A., & Syafrullah, M. (2023, October). PENERAPAN ALGORITMA K-NEAREST NEIGHBORS UNTUK MENGKLASIFIKASI SENTIEMEN MASYARAKAT TERHADAP KEBERADAAN CHAT GPT. In Prosiding Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI) (Vol. 2, No. 2, pp. 836-845).
Wijaya, T. N., Indriati, R., & Muzaki, M. N. (2021). Analisis Sentimen Opini Publik Tentang Undang-Undang Cipta Kerja Pada Twitter. Jambura Journal of Electrical and Electronics Engineering, 3(2), 78-83. https://doi.org/10.37905/jjeee.v3i2.10885
Wilie, D. P. (2023). Analisis sentimen opini publik terhadap ChatGPT di Twitter menggunakan metode Naive Bayes. Retrieved from https://www.kaggle.com/datasets/charunisa/ChatGPT-sentiment-analysis
Yaya, H., & Teguh, W. (2021). Dasar-dasar deep learning dan implementasinya. Yogyakarta: Gava Media Anggota IKAPI DIY.
Zhai, X. (2022). ChatGPT user experience: Implications for education. Available at SSRN 4312418. https://doi.org/10.2139/ssrn.4312418.
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