Perbandingan Evaluasi Kernel Support Vector Machine dalam Analisis Sentimen Chatbot AI pada Ulasan Google Play Store

Penulis

  • Celine Mutiara Putri Universitas Islam Negeri Sultan Syarif Kasim Riau
  • M. Afdal Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Mustakim Mustakim Universitas Islam Negeri Sultan Syarif Kasim Riau

DOI:

https://doi.org/10.32493/jtsi.v7i3.41354

Kata Kunci:

ChatGPT; Perplexity AI ; Bing Chat; Sentiment analysis; SVM; Kernel; Hyperparameter

Abstrak

Artificial Intelligence (AI) menjadi sangat penting dimasa ini karena kemampuannya sebagai asisten pribadi untuk meningkatkan efisiensi, mengotomatisasi tugas-tugas rutin, dan mempercepat proses manual. Chatbot AI menjadi salah satu penerapan praktis AI dalam pemahaman bahasa, memiliki berbagai manfaat serta kekurangan yang menimbulkan berbagai komentar para penggunanya pada kolom ulasan di Google Play Store. Penelitian ini membahas tentang analisis sentimen ulasan aplikasi  chatbot AI menggunakan empat kernel SVM. Pelabelan menggunakan InSet Lexicon dan hyperparaeter untuk menghasilkan parameter terbaik. Tujuan penelitian yaitu untuk mengetahui bagaimana pengguna merespons interaksi dengan ChatGPT, Perplexity AI, dan Bing Chat  serta membuktikan penggunaan kernel yang tepat pada SVM dapat meningkatkan nilai akurasi. Diskenariokan persentase pembagian antara data uji dan data latih adalah 70:30, 80:20, dan 90:10, pelabelan data menggunakan 2 kelas sentimen dan 3 kelas sentimen, serta menggunakan dan tidak menggunakan teknik SMOTE Oversampling. Hasil eksperimen diperoleh akurasi tertinggi menggunakan SVM kernel Linier skenario 90:10 dengan nilai accuracy 92.68%.

Referensi

Ananda, D., & Suryono, R. R. (2024). Analisis Sentimen Publik Terhadap Pengungsi Rohingya di Indonesia dengan Metode Support Vector Machine dan Naï ve Bayes. Jurnal Media Informatika Budidarma, 8(2), 748–757.

Auliaddina, S., & Arifin, T. (2024). Use of Augmentation Data and Hyperparameter Tuning in Batik Type Classification using the CNN Model. Sistemasi: Jurnal Sistem Informasi, 13(1), 114–128.

Caramiaux, B., & Fdili Alaoui, S. (2022). “Explorers of Unknown Planets”: Practices and Politics of Artificial Intelligence in Visual Arts. Proc. ACM Hum.-Comput. Interact., 6(CSCW2). https://doi.org/10.1145/3555578

Ernawati, S., & Wati, R. (2024). Evaluasi Performa Kernel SVM dalam Analisis Sentimen Review Aplikasi ChatGPT Menggunakan Hyperparameter dan VADER Lexicon. Jurnal Buana Informatika, 15(1), 40–49.

Gezici, B., Bölücü, N., Tarhan, A., & Can, B. (2019). Neural Sentiment Analysis of User Reviews to Predict User Ratings. 2019 4th International Conference on Computer Science and Engineering (UBMK), 629–634. https://doi.org/10.1109/UBMK.2019.8907234

Iorliam, A., & Ingio, J. A. (2024). A Comparative Analysis of Generative Artificial Intelligence Tools for Natural Language Processing. Journal of Computing Theories and Applications, 1(3), 311–325. https://doi.org/10.62411/jcta.9447

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/https://doi.org/10.1016/j.bushor.2018.03.007

Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence? Discover Artificial Intelligence, 2(1), 4. https://doi.org/10.1007/s44163-022-00022-8

Kavak, A. C. (2023). ChatGPT, Google Bard, Microsoft Bing, Claude, and Perplexity: Which is the Right AI Tool? In Zeo.org. https://zeo.org/resources/blog/chatgpt-google-bard-microsoft-bing-claude-and-perplexity-which-is-the-right-ai-to

Kumar, P., & Prusty, M. (2020). Outlier-SMOTE: A refined oversampling technique for improved detection of COVID-19. Intelligence-Based Medicine, 3–4, 100023. https://doi.org/10.1016/j.ibmed.2020.100023

Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature.

Nanda, M. A., Seminar, K. B., Nandika, D., & Maddu, A. (2018). A comparison study of kernel functions in the support vector machine and its application for termite detection. Information, 9(1), 5.

Nurkholis, A., Alita, D., & Munandar, A. (2022). Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(2 SE-Information Systems Engineering Articles). https://doi.org/10.29207/resti.v6i2.3906

Permata Aulia, T. M., Arifin, N., & Mayasari, R. (2021). Perbandingan Kernel Support Vector Machine (Svm) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19. SINTECH (Science and Information Technology) Journal, 4(2), 139–145. https://doi.org/10.31598/sintechjournal.v4i2.762

Prastyo, P. H., Ardiyanto, I., & Hidayat, R. (2020a). Indonesian Sentiment Analysis: An Experimental Study of Four Kernel Functions on SVM Algorithm with TF-IDF. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020. https://doi.org/10.1109/ICDABI51230.2020.9325685

Prastyo, P. H., Ardiyanto, I., & Hidayat, R. (2020b). Indonesian Sentiment Analysis: An Experimental Study of Four Kernel Functions on SVM Algorithm with TF-IDF. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 1–6. https://doi.org/10.1109/ICDABI51230.2020.9325685

Rabbani, S., Safitri, D., Rahmadhani, N., & Anam, M. K. (2023). Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM: Comparative Evaluation of SVM Kernels for Sentiment Classification in Fuel Price Increase Analysis. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 153–160.

Rianti, D. L., Umaidah, Y., & Voutama, A. (2021). Tren Marketplace Berdasarkan Klasifikasi Ulasan Pelanggan Menggunakan Perbandingan Kernel Support Vector Machine. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 6(1), 98–105. https://doi.org/10.30998/string.v6i1.9993

Ribas, J. (2023). Building the New Bing. Bing Blogs. https://blogs.bing.com/search-quality-insights/february-2023/Building-the-New-Bing/

Rudd, J. M. (2018). Application of support vector machine modeling and graph theory metrics for disease classification. Model Assisted Statistics and Applications, 13(4), 341–349.

Rudolph, J., Tan, S., & Tan, S. (2023). War of the chatbot: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI rush and its impact on higher education. Journal of Apllied Learning & Teaching, 6(1), 1–9. https://doi.org/https://doi.org/10.37074/jalt.2023.6.1.23 Abstract

Saeidnia, H. R., Hosseini, E., Abdoli, S., & Ausloos, M. (2024). Unleashing the power of AI: a systematic review of cutting-edge techniques in AI-enhanced scientometrics, webometrics and bibliometrics. Library Hi Tech, ahead-of-p(ahead-of-print). https://doi.org/10.1108/LHT-10-2023-0514

Sari, P. K., & Suryono, R. R. (2024). Komparasi Algoritma Support Vector Machine dan Random Forest untuk Analisis Sentimen Metaverse. Jurnal Mnemonic, 7(1), 31–39.

Sengkey, D. F., Jacobus, A., & Manoppo, F. J. (2020). Effects of kernels and the proportion of training data on the accuracy of svm sentiment analysis in lecturer evaluation. IAES International Journal of Artificial Intelligence, 9(4), 734–743. https://doi.org/10.11591/ijai.v9.i4.pp734-743

Sullivan, M. (2023). Is Perplexity AI showing us the future of search? https://www.fastcompany.com/90883562/is-perplexity-ai-showing-us-the-future-of-search

Tuttle, J. F., Blackburn, L. D., & Powell, K. M. (2020). On-line classification of coal combustion quality using nonlinear SVM for improved neural network NOx emission rate prediction. Computers & Chemical Engineering, 141, 106990.

Wahyu Handani, S., Intan Surya Saputra, D., Hasirun, Mega Arino, R., & Fiza Asyrofi Ramadhan, G. (2019). Sentiment analysis for go-jek on google play store. Journal of Physics: Conference Series, 1196(1). https://doi.org/10.1088/1742-6596/1196/1/012032

Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023). A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122–1136. https://doi.org/10.1109/JAS.2023.123618

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., … Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4). https://doi.org/10.1016/j.xinn.2021.100179

Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/https://doi.org/10.1016/j.technovation.2023.102747

Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 Juli 2024

Unduhan

Diterbitkan

2024-07-31

Cara Mengutip

Putri, C. M., Afdal, M., Novita, R., & Mustakim, M. (2024). Perbandingan Evaluasi Kernel Support Vector Machine dalam Analisis Sentimen Chatbot AI pada Ulasan Google Play Store. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(3), 1236–1245. https://doi.org/10.32493/jtsi.v7i3.41354

Terbitan

Vol 7 No 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

Bagian

Article

Lisensi

Hak Cipta (c) 2024 Celine Mutiara Putri, M. Afdal, Rice Novita, Mustakim Mustakim

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