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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Sodality: Jurnal Sosiologi Pedesaan MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Sosial dan Humaniora Jurnal Kawistara : Jurnal Ilmiah Sosial dan Humaniora Journal of Indonesian Tourism and Development Studies JURNAL ELEKTRO Jurnal Kebijakan dan Administrasi Publik AdBispreneur PAX HUMANA ARISTO JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komunikasi Kritis Humaniora MUWAZAH: Jurnal Kajian Gender Cakrawala Jurnal Penelitian Sosial Building of Informatics, Technology and Science Jurnal Mantik Journal of Information Systems and Informatics Jurnal Studi Sosial dan Politik Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Jurnal Sistem Komputer dan Informatika (JSON) JOURNAL OF BUSINESS AND ECONOMICS RESEARCH (JBE) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Cita Ekonomika: Jurnal Ilmu Ekonomi ARBITRASE: JOURNAL OF ECONOMICS AND ACCOUNTING International Journal on Social Science, Economics and Art KLIK: Kajian Ilmiah Informatika dan Komputer International Journal of Basic and Applied Science Indonesian Journal of Tourism and Leisure Jurnal InterAct Jurnal Sosiologi Engagement: Jurnal Pengabdian Kepada Masyarakat JKAP (Jurnal Kebijakan dan Administrasi Publik) Jurnal Kawistara
Yerik Afrianto Singgalen
Universitas Katolik Indonesia Atma Jaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Public Health Social Sciences Other

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Journal : International Journal of Basic and Applied Science

 1 
Sentiment classification of coral reef 101 content using decision tree algorithm through CRISP-DM Yerik Afrianto Singgalen
International Journal of Basic and Applied Science Vol. 12 No. 3 (2023): December: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v12i3.297

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.
Comparative analysis of decision tree and support vector machine algorithm in sentiment classification for birds of paradise content Yerik Afrianto Singgalen
International Journal of Basic and Applied Science Vol. 12 No. 3 (2023): December: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v12i3.298

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.
Culture and heritage tourism sentiment classification through cross-industry standard process for data mining Yerik Afrianto Singgalen
International Journal of Basic and Applied Science Vol. 12 No. 3 (2023): December: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v12i3.299

Abstract

This study investigates the efficacy of machine learning algorithms in sentiment classification within the context of Culture and Heritage Tourism content analysis. This study adopts the CRISP-DM method, a comprehensive methodology encompassing distinct stages, including business understanding, data understanding, modeling, evaluation, and deployment. The k-nearest Neighbors, Decision Tree, Naive Bayes Classifier, and Support Vector Machine models are used. The performance of each model is scrutinized through confusion matrix analysis, encompassing metrics such as accuracy, precision, recall, and F-measure. Additionally, the impact of the Synthetic Minority Over-sampling Technique (SMOTE) implementation on addressing data imbalance is assessed. Leveraging data from the national geographic channel's YouTube platform, with a focus on ma'nene content, results reveal SVM's consistent superiority, particularly with SMOTE integration, showcasing elevated accuracy (77.89%), precision (72.60%), recall (89.62%), and F-measure (80.21%) values. These findings underscore the importance of algorithm selection and data preprocessing methods in enhancing sentiment classification accuracy for culture and heritage tourism content, thus contributing quantifiable insights to the tourism research domain.
Co-Authors A.Y. Agung Nugroho Abigail Rosandrine Kayla Putri Rahadi Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel Aprius Sutresno, Stephen Astuti Kusumawicitra Astuti Kusumawicitra Laturiuw Astuti Kusumawicitra Laturiuw Bernardus Alvin Rig Bernardus Alvin Rig Biafra Daffa Farabi Biafra Daffa Farabi Billy Macarius Sidhunata Brito, Manuel Charitas Fibriani Christanto, Henoch Juli Christine Dewi Danny Manongga Dasra, Muhamad Nur Agus Eko Sediyono Eko Widodo Elfin Saputra Elfin Saputra Elly Esra Kudubun Eugenius Kau Suni Fang, Liem Shiao Faskalis Halomoan Lichkman Manurung Gatot Sasongko Gilberto Dennis G E Sidabutar Gintu, Agung Rimayanto Gudiato, Candra Henoch Juli Christanto Henoch Juli Christanto Henoch Juli Christanto Heru Prasadja Hindriyanto Dwi Purnomo Hironimus Cornelius Royke Irene Sonbay Irwan Sembiring Jesslyn Alvina Seah Jonathan Tristan Santoso Juli Christanto, Henoch Kartikawangi, Dorien Kusumawicitra, Astuti Manuel Brito Marthen Timisela Mavish, Steven Michael Kenang Gabbatha Nantingkaseh, Alfonso Harrison Nicolas Arya Nanda Susilo Nugroho, A. Y. Agung Octa Hutapea Octa Hutapea Pamerdi Giri Wiloso Pamerdi Giri Wiloso Pamerdi Giri Wiloso, Pamerdi Giri Pedro Manuel Lamberto Buu Sada Pinia, Nyoman Agus Perdanaputra Pontolawokang, Theresya Ellen Pristiana Widyastuti Pristiana Widyastuti Purwoko, Agus Puspitarini, Titis Radyan Rahmananta Radyan Rahmananta Rafael Christian Rahadi, Abigail Rosandrine Kayla Putri Rahmadini, Asyifa Catur Richard Emmanuel Adrian Sinaga Rosdiana Sijabat Ruben William Setiawan Samuel Piolo Seingo, Martha Maraka Setiawan, Ruben William Siemens Benyamin Tjhang Sri Yulianto Joko Prasetyo Stephen Aprius Sutresno Suharsono SUHARSONO Tabuni, Gasper Tharsini, Priya Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani