Sentiment classification of coral reef 101 content using decision tree algorithm through CRISP-DM
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Published:
Dec 30, 2023
Keywords:
Coral, Reef, Sentiment, Classification, Decision Tree
Section
Computer Science
Statistics Article
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Abstract
This research aims to classify public sentiment regarding the content of "Coral Reef 101," published by National Geographic. The methodology employed is the Cross-Industry Standard Process for Data Mining (CRISP-DM), encompassing stages such as business understanding, data understanding, modeling, evaluation, and deployment. The Decision Tree algorithm is utilized in conjunction with the SMOTE operator. This comprehensive approach enables the systematic analysis of public sentiment towards coral reef content, facilitating a deeper understanding of public perception and attitudes. The results of this study indicate that the DT algorithm with SMOTE demonstrates an accuracy of 87.51% +/- 4.28% (micro average: 87.50%), a precision of 80.35% +/- 5.10% (micro average: 80.00%) (positive class: Positive), recall of 100.00% +/- 0.00% (micro average: 100.00%) (positive class: Positive), f-measure of 89.02% +/- 3.22% (micro average: 88.89%) (positive class: Positive), and an AUC of 0.875 +/- 0.044 (micro average: 0.875) (positive class: Positive). These metrics demonstrate the effectiveness of the DT algorithm with SMOTE in accurately classifying public sentiment towards coral reef-related content, particularly in correctly identifying positive sentiment instances. The high accuracy, precision, recall, f-measure, and AUC values underscore the robustness and reliability of the model in sentiment analysis tasks.
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How to Cite
Singgalen, Y. A. (2023). Sentiment classification of coral reef 101 content using decision tree algorithm through CRISP-DM. International Journal of Basic and Applied Science, 12(3), 121–130. https://doi.org/10.35335/ijobas.v12i3.297
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Y. A. Singgalen, “Analisis Sentimen Konsumen terhadap Food , Services , and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan,” Build. Informatics, Technol. Sci., vol. 4, no. 4, pp. 1899–1914, 2023, doi: 10.47065/bits.v4i4.3231.
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 Perbandingan Top 10 Best-Value dan Top 10 Traveler- Ranked Hotel Menggunakan MOORA,” J. Comput. Syst. Informatics, vol. 4, no. 4, pp. 899–911, 2023, doi: 10.47065/josyc.v4i4.4070.
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.
H. Christanto et al., “Analisis Perbandingan Decision Tree , Support Vector Machine , dan Xgboost dalam Mengklasifikasi Review Hotel Trip Advisor,” J. Teknol. Inform. dan Komput. MH. Thamrin, vol. 9, no. 1, pp. 306–319, 2023.
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D. A. Munawwaroh and A. H. Primandari, “Implementasi Crisp-Dm Model Menggunakan Metode Decision Tree Dengan Algoritma Cart Untuk Prediksi Lila Ibu Hamil Berpotensi Gizi Kurang,” J. Ilm. Pendidik. Mat., vol. 10, no. 2, pp. 367–380, 2022, [Online]. Available: http://dx.doi.org/10.31941/delta.v10i2.2172
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.
A. A. Arifiyanti, M. Fuad, P. Fikri, and B. Utomo, “Analisis Sentimen Ulasan Pengunjung Objek Wisata Gunung Bromo pada Situs Tripadvisor,” Explor. J. Sist. Inf. dan Telemat., vol. 13, no. 1, pp. 32–37, 2022.
L. A. Pramesti and N. Pratiwi, “Analisis Sentimen Twitter Terhadap Program MBKM Menggunakan Decision Tree dan Support Vector Machine,” J. Inf. Syst. Res., vol. 4, no. 4, pp. 1145–1154, 2023, doi: 10.47065/josh.v4i4.3807.
A. A. Arifiyanti, M. F. Pandji, and B. Utomo, “Analisis Sentimen Ulasan Pengunjung Objek Wisata Gunung Bromo pada Situs Tripadvisor,” Explor. J. Sist. Inf. dan Telemat., vol. 13, no. 1, p. 32, 2022, doi: 10.36448/jsit.v13i1.2539.
N. L. W. S. R. Ginantra, C. P. Yanti, G. D. Prasetya, I. B. G. Sarasvananda, and I. K. A. G. Wiguna, “Analisis Sentimen Ulasan Villa di Ubud Menggunakan Metode Naive Bayes, Decision Tree, dan k-NN,” Janapati, vol. 11, no. 3, pp. 205–216, 2022.
A. T. W. Putra and A. Triayudi, “Analisis Sentimen Pembelajaran Daring menggunakan Metode Naïve Bayes, KNN, dan Decision Tree,” Jurnal JTIK (Jurnal Teknologi …. pdfs.semanticscholar.org, 2022. [Online]. Available: https://pdfs.semanticscholar.org/177f/4bbf7cd48c9afb2af92cfe8c589bbe81ce6e.pdf
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Z. Lin, “Chinese legislation on protection of underwater cultural heritage in marine spatial planning and its implementation,” Int. J. Cult. Policy, vol. 29, no. 4, pp. 500–517, 2023, doi: 10.1080/10286632.2022.2080201.
E. Spooner et al., “Using Integrated Ecosystem Assessments to Build Resilient Ecosystems, Communities, and Economies,” Coast. Manag., vol. 49, no. 1, pp. 26–45, 2021, doi: 10.1080/08920753.2021.1846152.
M. He et al., “Coral reef applications of Landsat-8: geomorphic zonation and benthic habitat mapping of Xisha Islands, China,” GIScience Remote Sens., vol. 60, no. 1, pp. 1–27, 2023, doi: 10.1080/15481603.2023.2261213.
H. Zhang and S. Chen, “Overview of research on marine resources and economic development,” Mar. Econ. Manag., vol. 5, no. 1, pp. 69–83, 2022, doi: 10.1108/maem-11-2021-0012.
H. Tang, M. Lin, J. Yu, and Q. Yue, “New development of marine spatial planning in China: problems and policy suggestions on the implementation of National Plan for Main Functional Zones of Oceans,” Mar. Econ. Manag., vol. 5, no. 1, pp. 34–44, 2022, doi: 10.1108/maem-11-2021-0011.
C. Tang, Z. Chen, and J. Peng, “Marine tourism omnichannel coordination,” Mar. Econ. Manag., vol. 5, no. 2, pp. 147–172, 2022, doi: 10.1108/maem-01-2022-0003.
V. Reiskarts and K. Savenkovs, “Smarthub for supervising system for resource exploration and pollution control in deep-water and coastal areas based on ICT technologies,” Mar. Econ. Manag., vol. 6, no. 1, pp. 23–34, 2023, doi: 10.1108/maem-02-2023-0002.
M. Struwig, A. Van den Berg, and N. Hadi, “Challenges in the ocean economy of South Africa,” Dev. South. Afr., vol. 41, no. 1, pp. 1–15, 2023, doi: 10.1080/0376835X.2023.2232396.
A. Mehta, R. Wynberg, C. Ramcharan-Kotze, and A. J. Smit, “Exploring local perceptions around the value of marine biodiversity: the case of kelp in the Western Cape, South Africa,” Ecosyst. People, vol. 19, no. 1, 2023, doi: 10.1080/26395916.2023.2234499.
J. Davret, B. Trouillet, and H. Toonen, “The digital turn of marine planning: a global analysis of ocean geoportals,” J. Environ. Policy Plan., vol. 26, no. 1, pp. 75–90, 2023, doi: 10.1080/1523908X.2023.2283081.
J. M. Ip-Soo-Ching, P. Massingham, and A. Pomering, “Managing diverse knowledge systems of tourism operators in vulnerable marine ecosystems: addressing sustainability challenges through nature-based solutions,” Asia Pacific J. Tour. Res., vol. 27, no. 4, pp. 333–356, 2022, doi: 10.1080/10941665.2022.2061364.
E. Macpherson et al., “Designing Law and Policy for the Health and Resilience of Marine and Coastal Ecosystems—Lessons From (and for) Aotearoa New Zealand,” Ocean Dev. Int. Law, vol. 54, no. 2, pp. 200–252, 2023, doi: 10.1080/00908320.2023.2224116.
D. Eider, S. Partelow, S. Albrecht, L. Adrianto, and L. C. Kluger, “SCUBA tourism and coral reefs: a social-ecological network analysis of governance challenges in Indonesia,” Curr. Issues Tour., vol. 26, no. 7, pp. 1031–1050, 2023, doi: 10.1080/13683500.2021.2006612.
A. K. Smith et al., “Innovative local response to cyclone damaged reef leads to rapid tourism recovery,” J. Ecotourism, vol. 22, no. 3, pp. 354–374, 2023, doi: 10.1080/14724049.2022.2027952.
D. G. Borojo, J. Yushi, Z. Hongyu, L. Xiao, and M. Miao, “A pathway to the green revolution in emerging economies: how does green technological innovation affect green growth and ecological sustainability?,” Econ. Res. Istraz., vol. 36, no. 1, pp. 1–24, 2023, doi: 10.1080/1331677X.2023.2167223.
J. J. Lv, N. Wang, H. Ju, and X. F. Cui, “Influence of green technology, tourism, and inclusive financial development on ecological sustainability: exploring the path toward green revolution,” Econ. Res. Istraz., vol. 36, no. 1, pp. 1–24, 2023, doi: 10.1080/1331677X.2022.2116349.
Y. A. Singgalen, “Analisis Performa Algoritma NBC , DT , SVM dalam Klasifikasi Data Ulasan Pengunjung Candi Borobudur Berbasis CRISP-DM,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1634–1646, 2022, doi: 10.47065/bits.v4i3.2766.
Y. A. Singgalen, “Penerapan Metode CRISP-DM dalam Klasifikasi Data Ulasan Pengunjung Destinasi Danau Toba Menggunakan Algoritma Naïve Bayes Classifier ( NBC ) dan Decision Tree ( DT ),” J. Media Inform. Budidarma, vol. 7, no. 3, pp. 1551–1562, 2023, doi: 10.30865/mib.v7i3.6461.
Y. A. Singgalen, “Analisis Sentimen Wisatawan terhadap Taman Nasional Bunaken dan Top 10 Hotel Rekomendasi Tripadvisor Menggunakan Algoritma SVM dan DT berbasis CRISP-DM,” J. Comput. Syst. Informatics, vol. 4, no. 2, pp. 367–379, 2023, doi: 10.47065/josyc.v4i2.3092.
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.
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.
Y. A. Singgalen, “Analisis Sentimen Konsumen terhadap Food , Services , and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan,” Build. Informatics, Technol. Sci., vol. 4, no. 4, pp. 1899–1914, 2023, doi: 10.47065/bits.v4i4.3231.
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 Perbandingan Top 10 Best-Value dan Top 10 Traveler- Ranked Hotel Menggunakan MOORA,” J. Comput. Syst. Informatics, vol. 4, no. 4, pp. 899–911, 2023, doi: 10.47065/josyc.v4i4.4070.
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.
H. Christanto et al., “Analisis Perbandingan Decision Tree , Support Vector Machine , dan Xgboost dalam Mengklasifikasi Review Hotel Trip Advisor,” J. Teknol. Inform. dan Komput. MH. Thamrin, vol. 9, no. 1, pp. 306–319, 2023.
R. Obiedat et al., “Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution,” IEEE Access, vol. 10, pp. 22260–22273, 2022, doi: 10.1109/ACCESS.2022.3149482.
D. A. Munawwaroh and A. H. Primandari, “Implementasi Crisp-Dm Model Menggunakan Metode Decision Tree Dengan Algoritma Cart Untuk Prediksi Lila Ibu Hamil Berpotensi Gizi Kurang,” J. Ilm. Pendidik. Mat., vol. 10, no. 2, pp. 367–380, 2022, [Online]. Available: http://dx.doi.org/10.31941/delta.v10i2.2172
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.
A. A. Arifiyanti, M. Fuad, P. Fikri, and B. Utomo, “Analisis Sentimen Ulasan Pengunjung Objek Wisata Gunung Bromo pada Situs Tripadvisor,” Explor. J. Sist. Inf. dan Telemat., vol. 13, no. 1, pp. 32–37, 2022.
L. A. Pramesti and N. Pratiwi, “Analisis Sentimen Twitter Terhadap Program MBKM Menggunakan Decision Tree dan Support Vector Machine,” J. Inf. Syst. Res., vol. 4, no. 4, pp. 1145–1154, 2023, doi: 10.47065/josh.v4i4.3807.
A. A. Arifiyanti, M. F. Pandji, and B. Utomo, “Analisis Sentimen Ulasan Pengunjung Objek Wisata Gunung Bromo pada Situs Tripadvisor,” Explor. J. Sist. Inf. dan Telemat., vol. 13, no. 1, p. 32, 2022, doi: 10.36448/jsit.v13i1.2539.
N. L. W. S. R. Ginantra, C. P. Yanti, G. D. Prasetya, I. B. G. Sarasvananda, and I. K. A. G. Wiguna, “Analisis Sentimen Ulasan Villa di Ubud Menggunakan Metode Naive Bayes, Decision Tree, dan k-NN,” Janapati, vol. 11, no. 3, pp. 205–216, 2022.
A. T. W. Putra and A. Triayudi, “Analisis Sentimen Pembelajaran Daring menggunakan Metode Naïve Bayes, KNN, dan Decision Tree,” Jurnal JTIK (Jurnal Teknologi …. pdfs.semanticscholar.org, 2022. [Online]. Available: https://pdfs.semanticscholar.org/177f/4bbf7cd48c9afb2af92cfe8c589bbe81ce6e.pdf
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