Sentimen Analisis Kesehatan Mental Anxiety dengan Metode Decision Tree Menggunakan Software Orange
Keywords:
Mental health; anxiety; sentiment analysis; decision tree; OrangeAbstract
Mental health, particularly anxiety disorders, has become a global concern due to the rising prevalence of mental health issues worldwide. Anxiety significantly affects individuals' quality of life and productivity, making it essential to accurately analyze and detect its symptoms. This study aims to apply the decision tree method for sentiment analysis of anxiety in texts collected from various sources such as mental health forums and social media. The decision tree method was chosen for its simplicity and effectiveness in classifying data based on identified patterns. Orange software was utilized to build the classification model due to its user-friendly interface and visualization capabilities. The results indicate that the decision tree model was able to effectively identify anxiety patterns in the texts, contributing to a better understanding of sentiment analysis in the mental health context. This study also introduces a more accessible approach for practitioners and researchers in this field.
References
[1] G. Zahra, N. Fadhilah, R. A. Saputra, and A. H. Wibowo, “Deteksi Tingkat Gangguan Kecemasan Menggunakan Metode Random Forest,” J. Fak. Tek. UMT, vol. 13, no. 1, pp. 38–47, 2024, [Online]. Available: http://jurnal.umt.ac.id/index.php/jt/index
[2] N. A. Pasa, Y. Maulita, and I. G. Prahmana, “Penggunaan Metode Rough Set pada Tingkat Kecemasan ( Anxietas ) Mahasiswa dalam Menyusun Tugas Akhir,” no. 4, 2024.
[3] A. Yahyadi and F. Latifah, “Analisis Sentimen Twitter TerhadapKebijakan Ppkm Di Tengah Pandemi Covid-19Menggunakan Mode Lstm,” J. Inf. Syst. Applied, Manag. Account. Res., vol. 6, no. 2, pp. 464–470, 2022, doi: 10.52362/jisamar.v6i2.791.
[4] M. A. Ramadhan and M. I. Wahyudin, “Analisis Sentimen Mengenai Keberhasilan Indonesia di Ajang Thomas Cup 2020 (Studi Kasus Media Sosial Twitter) Menggunakan Metode Naïve Bayes dan Decision Tree,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 6, no. 4, pp. 505–511, 2022, doi: 10.35870/jtik.v6i4.560.
[5] O. B. Islamuddin and I. Yuadi, “Analisis Text Clustering Pada Data Mining Menggunakan Metode K- Nearest Neighbor ( Knn ) Dan Decision Tree,” J. Khatulistiwa Inform., vol. 11, no. 2, pp. 128–134, 2023.
[6] F. Firmansyah and A. Yulianto, “Pemodelan Pembelajaran Mesin untuk Prediksi Kesehatan Mental di Tempat Kerja,” J. Minfo Polgan, vol. 13, no. 1, pp. 397–407, 2024, doi: 10.33395/jmp.v13i1.13674.
[7] Muhammad Daffa Al Fahreza, Ardytha Luthfiarta, Muhammad Rafid, and Michael Indrawan, “Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z,” J. Appl. Comput. Sci. Technol., vol. 5, no. 1, pp. 16–25, 2024, doi: 10.52158/jacost.v5i1.715.
[8] Y. F. Nugraini, R. Rohmat Saedudin, and R. Andreswari, “Implementasi Data Mining Dalam Kasus Mental Health Pada Sosial Media Twitter Menggunakan Metode Naive Bayes,” e-Proceeding Eng., vol. 8, no. 5, pp. 9260–9265, 2021.
[9] S. M. Tambunan, Y. Nataliani, and E. S. Lestari, “Perbandingan Klasifikasi dengan Pendekatan Pembelajaran Mesin untuk Mengidentifikasi Tweet Hoaks di Media Sosial Twitter,” J. Edukasi dan Penelit. Inform., vol. 7, no. 2, p. 112, 2021, doi: 10.26418/jp.v7i2.47232.
[10] M. R. Firdaus, N. Rahaningsih, and R. D. Dana, “Analisis Sentimen Aplikasi Shopee di Goole Play Store Menggunakan Klasifikasi Algoritma Naïve Bayes,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 6, no. 1, pp. 228–237, 2024.
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