Comparative analysis of decision tree and support vector machine algorithm in sentiment classification for birds of paradise content
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Published:
Dec 30, 2023
Keywords:
Birding, Decision Tree, Support Vector Machine, Sentiment, Classification
Section
Computer Science
Statistics Article
Article View : 285 Times
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Abstract
This research aims to analyze public sentiments towards National Geographic's content on the bird of paradise from the perspective of nature-based tourism. The method utilized is CRISP-DM, comprising stages of business understanding, data understanding, modeling, evaluation, and deployment. Focusing on sentiments expressed in response to National Geographic's Bird of Paradise content, this study seeks insights into how the public perceives and values nature-oriented tourism experiences. Comparing the results of DT and SVM algorithms with and without the SMOTE reveals noteworthy differences in classification performance. Without SMOTE, both DT and SVM exhibit relatively lower accuracy and AUC values compared to their counterparts with SMOTE. For DT, adding SMOTE substantially improves accuracy (from 92.44% to 95.20%) and AUC (from 0.517 to 0.956), indicating enhanced classification accuracy and model robustness. In addition, SVM demonstrates significant performance gains with SMOTE, achieving notably higher accuracy (from 92.12% to 98.63%) and AUC (from 0.617 to 0.999). The significantly higher values across various performance metrics for SVM underscore its effectiveness in handling imbalanced datasets and accurately classifying sentiment data. Therefore, researchers and practitioners may consider leveraging SVM for sentiment analysis tasks in similar contexts to achieve optimal classification results and enhance decision-making processes.
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How to Cite
Singgalen, Y. A. (2023). Comparative analysis of decision tree and support vector machine algorithm in sentiment classification for birds of paradise content. International Journal of Basic and Applied Science, 12(3), 100–109. https://doi.org/10.35335/ijobas.v12i3.298
References
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Y. A. Singgalen, “Penerapan Metode Additive Ratio Assessment ( ARAS ) dan Ranking of Centroid ( ROC ) dalam Pemilihan Layanan Akomodasi dan Local Cuisine,” J. Comput. Syst. Informatics, vol. 5, no. 1, pp. 51–60, 2023, doi: 10.47065/josyc.v5i1.4569.
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M. R. Muttaqin, T. I. Hermanto, and M. A. Sunandar, “Penerapan K-Means Clustering Dan Cross-Industry Standard Process for Data Mining (CRISP-DM) Untuk Mengelompokan Penjualan Kue,” Komputasi J. Ilm. Ilmu Komput. dan Mat., vol. 19, no. 1, pp. 38–53, 2022, [Online]. Available: https://journal.unpak.ac.id/index.php/komputasi
H. J. Christanto and Y. A. Singgalen, “Sentiment Analysis on Customer Perception towards Products and Services of Restaurant in Labuan Bajo,” J. Inf. Syst. Informatics, vol. 4, no. 3, pp. 511–523, 2022, doi: 10.51519/journalisi.v4i3.276.
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M. Kruger and A. Viljoen, “Bird(er)s of a feather? A typology of birders to South African national parks based on their behavioural involvement,” Ann. Leis. Res., vol. 26, no. 1, pp. 1–26, 2023, doi: 10.1080/11745398.2020.1813041.
P. Tryjanowski, S. Murawiec, and C. Randler, “No such Thing as Bad Birding Weather, but Depends on Personal Experience,” Leis. Sci., vol. 0, no. 0, pp. 1–13, 2023, doi: 10.1080/01490400.2023.2167026.
C. Randler, N. Staller, N. Kalb, and P. Tryjanowski, “Charismatic Species and Birdwatching: Advanced Birders Prefer Small, Shy, Dull, and Rare Species,” Anthrozoos, vol. 36, no. 3, pp. 427–445, 2023, doi: 10.1080/08927936.2023.2182030.
N. Groβmann and C. Randler, “Developing an instrument to assess the satisfaction and frustration of basic psychological needs during the leisure activity of birdwatching (Birding-BPNSF),” J. Leis. Res., vol. 0, no. 0, pp. 1–23, 2023, doi: 10.1080/00222216.2023.2287003.
R. Humphreys, “A ‘Game’ Bird? On Why Hunting is Not a Game and Thus Not a Sport,” Sport. Ethics Philos., vol. 17, no. 4, pp. 432–442, 2023, doi: 10.1080/17511321.2023.2189292.
J. Spring, “Nature-based tourism and guided wildlife tours: designing wildlife tour experiences that optimise sustainable learning opportunities,” J. Ecotourism, vol. 22, no. 1, pp. 187–207, 2023, doi: 10.1080/14724049.2022.2098963.
E. Conti and I. Farsari, “Disconnection in nature-based tourism experiences: an actor-network theory approach,” Ann. Leis. Res., vol. 0, no. 0, pp. 1–18, 2022, doi: 10.1080/11745398.2022.2150665.
D. Sumanapala, I. D. Wolf, and B. Weiler, “Enhancing Tour Guide Training for Delivering Nature-Based Tourism Experiences in a Developing Country,” J. Qual. Assur. Hosp. Tour., vol. 00, no. 00, pp. 1–25, 2023, doi: 10.1080/1528008X.2023.2253561.
N. Richardson and A. Insch, “Enabling transformative experiences through nature-based tourism,” Tour. Recreat. Res., vol. 48, no. 2, pp. 311–318, 2023, doi: 10.1080/02508281.2021.1952396.
E. Sthapit, P. Björk, and D. N. Coudounaris, “Memorable nature-based tourism experience, place attachment and tourists’ environmentally responsible behaviour,” J. Ecotourism, vol. 22, no. 4, pp. 542–565, 2023, doi: 10.1080/14724049.2022.2091581.
L. Kou, X. Xiao, H. Xu, and J. Cheng, “Understanding tourist experiences of sounds at nature-based destinations: from a relational perspective,” Curr. Issues Tour., vol. 27, no. 4, pp. 600–618, 2023, doi: 10.1080/13683500.2023.2168522.
A. Douglas, P. Mostert, and L. Slabbert, “Millennials as consumers of wildlife tourism experiences,” World Leis. J., vol. 64, no. 4, pp. 487–507, 2022, doi: 10.1080/16078055.2022.2097736.
I. Falardeau, P. Marcotte, and L. Bourdeau, “How ‘natural’ is innovation in nature-based tourism?,” Loisir Soc., vol. 45, no. 1, pp. 134–149, 2022, doi: 10.1080/07053436.2022.2053326.
C. Clark and G. P. Nyaupane, “Understanding Millennials’ nature-based tourism experience through their perceptions of technology use and travel constraints,” J. Ecotourism, vol. 22, no. 3, pp. 339–353, 2023, doi: 10.1080/14724049.2021.2023555.
Y. A. Singgalen, “Analisis Sentimen Pengunjung Pulau Komodo dan Pulau Rinca di Website Tripadvisor Berbasis CRISP-DM,” J. Inf. Syst. Res., vol. 4, no. 2, pp. 614–625, 2023, doi: 10.47065/josh.v4i2.2999.
Y. A. Singgalen, “Penerapan Metode CRISP-DM untuk Optimalisasi Strategi Pemasaran STP (Segmenting , Targeting , Positioning) Layanan Akomodasi Hotel , Homestay , dan Resort,” J. Media Inform. Budidarma, vol. 7, no. 4, pp. 1980–1993, 2023, doi: 10.30865/mib.v7i4.6896.
Y. A. Singgalen, “Analisis Sentimen Top 10 Traveler Ranked Hotel di Kota Makassar Menggunakan Algoritma Decision Tree dan Support Vector Machine,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 1, pp. 323–332, 2023, doi: 10.30865/klik.v4i1.1153.
Y. A. Singgalen, “Analisis Sentimen dan Sistem Pendukung Keputusan Menginap di Hotel Menggunakan Metode CRISP-DM dan SAW,” J. Inf. Syst. Res., vol. 4, no. 4, pp. 1343–1353, 2023, doi: 10.47065/josh.v4i4.3917.
Y. A. Singgalen, “Penerapan Metode Additive Ratio Assessment ( ARAS ) dan Ranking of Centroid ( ROC ) dalam Pemilihan Layanan Akomodasi dan Local Cuisine,” J. Comput. Syst. Informatics, vol. 5, no. 1, pp. 51–60, 2023, doi: 10.47065/josyc.v5i1.4569.
Y. A. Singgalen, “Implementasi Metode CRISP-DM dalam Analisis Model Pendukung Keputusan Simple Additive Weighting dan Pengembangan Basis Data Riwayat Pembelian Layanan Akomodasi Hotel,” J. Sist. Komput. dan Inform., vol. 5, no. 2, pp. 308–317, 2023, doi: 10.30865/json.v5i2.7153.
Y. A. Singgalen, “Penerapan CRISP-DM dalam Klasifikasi Sentimen dan Analisis Perilaku Pembelian Layanan Akomodasi Hotel Berbasis Algoritma Decision Tree ( DT ),” J. Sist. Komput. dan Inform., vol. 5, no. 2, pp. 237–248, 2023, doi: 10.30865/json.v5i2.7081.
J. Räikkönen, M. Grénman, H. Rouhiainen, A. Honkanen, and I. E. Sääksjärvi, “Conceptualizing nature-based science tourism: a case study of Seili Island, Finland,” J. Sustain. Tour., vol. 31, no. 5, pp. 1214–1232, 2023, doi: 10.1080/09669582.2021.1948553.
A. Akhshik, H. Rezapouraghdam, A. Ozturen, and H. Ramkissoon, “Memorable tourism experiences and critical outcomes among nature-based visitors: a fuzzy-set qualitative comparative analysis approach,” Curr. Issues Tour., vol. 26, no. 18, pp. 2981–3003, 2023, doi: 10.1080/13683500.2022.2106196.
Q. Jiang, C. S. Chan, S. Eichelberger, H. Ma, and B. Pikkemaat, “Sentiment analysis of online destination image of Hong Kong held by mainland Chinese tourists,” Curr. Issues Tour., vol. 24, no. 17, pp. 2501–2522, 2021, doi: 10.1080/13683500.2021.1874312.
V. H. Luong, A. Manthiou, J. Kang, and C. Nguyen, “The building blocks of regenerative tourism and hospitality: a text-mining approach,” Curr. Issues Tour., 2023, doi: 10.1080/13683500.2023.2228974.
L. G. R. Putra, Mayadi, and I. N. D. Setiaji, “Klasifikasi Jenis Client Menggunakan Algoritma Decision Tree Cart,” JSI J. Sist. Inf., vol. 14, no. 2, pp. 2842–2855, 2022, doi: 10.30812/jsi.v14i2.18826.
J. A. Syahid and D. Mahdiana, “Perbandingan algoritma untuk klasifikasi analisis sentimen terhadap Genose pada media sosial Twitter,” semanTIK, vol. 7, no. 1, pp. 9–16, 2021, doi: 10.5281/zenodo.5034916.
M. R. Muttaqin, T. I. Hermanto, and M. A. Sunandar, “Penerapan K-Means Clustering Dan Cross-Industry Standard Process for Data Mining (CRISP-DM) Untuk Mengelompokan Penjualan Kue,” Komputasi J. Ilm. Ilmu Komput. dan Mat., vol. 19, no. 1, pp. 38–53, 2022, [Online]. Available: https://journal.unpak.ac.id/index.php/komputasi
H. J. Christanto and Y. A. Singgalen, “Sentiment Analysis on Customer Perception towards Products and Services of Restaurant in Labuan Bajo,” J. Inf. Syst. Informatics, vol. 4, no. 3, pp. 511–523, 2022, doi: 10.51519/journalisi.v4i3.276.
A. Munir, E. P. Atika, and A. D. Indraswari, “Analisis Sentimen pada review hotel menggunakan metode pembobotan dan klasifikasi,” Jnanaloka, vol. 3, no. 1, pp. 33–38, 2022, doi: 10.36802/jnanaloka.2022.v3-no1-33-38.
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