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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 Perkotaan 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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Implementation of SVM and DT for Sentiment Classification: Tempel Hamlet Content Reviews Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1826

Abstract

The study aims to investigate the effectiveness of sentiment analysis algorithms, specifically Support Vector Machine (SVM) and Decision Tree (DT), integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance issues. Guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, the research involves several stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The process begins with understanding the business objectives of sentiment analysis and proceeds to explore and prepare the dataset for analysis. SVM and DT algorithms, enhanced with SMOTE, are then implemented for sentiment classification. The study reveals promising results in sentiment analysis tasks. When integrated with SMOTE, SVM achieves an accuracy of 99.21%, while DT attains an accuracy of 98.33%. The Area Under the Curve (AUC) metrics indicate high confidence in classifying positive instances, with SVM and DT demonstrating AUC scores of 1.000 and 0.996, respectively. These findings underscore the efficacy of SVM and DT algorithms, enhanced with SMOTE, in accurately classifying sentiment within text data, thereby addressing class imbalance issues effectively
Performance Evaluation of Sentiment Classification Models: A Comparative Study of NBC, SVM, and DT with SMOTE Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1827

Abstract

This research explores the performance of sentiment classification models, namely Naive Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM), using the CRISP-DM methodology in the context of digital content analysis and data mining. The analysis was conducted on a SMOTE dataset in Rapidminer, yielding significant performance metrics. The NBC model achieved an accuracy of 86.98% +/- 0.96%, precision of 100.00% +/- 0.00%, recall of 78.82% +/- 1.55%, and f-measure of 88.15% +/- 0.97%, with an AUC of 0.657 +/- 0.203. Similarly, the DT model exhibited an accuracy of 93.20% +/- 0.42%, precision of 90.87% +/- 0.64%, recall of 98.88% +/- 0.31%, and f-measure of 94.70% +/- 0.31%, with an AUC of 0.918 +/- 0.006. Furthermore, the SVM model demonstrated an accuracy of 96.80% +/- 0.65%, precision of 98.99% +/- 0.28%, recall of 95.77% +/- 1.03%, and f-measure of 97.35% +/- 0.55%, with an AUC of 0.994. These findings highlight the efficacy of these models in accurately classifying sentiments within digital content, suggesting their suitability for various data mining applications. Recommendations for future research include exploring ensemble methods, continuous model updating, alternative sampling techniques, feature engineering approaches, and collaboration with domain experts to enhance real-world applicability
Comparative Analysis of DT and SVM Model Performance with SMOTE in Sentiment Classification Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1828

Abstract

This research investigates the efficacy of employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to analyze sentiment classification models. The study focuses on evaluating the performance of Decision Trees (DT) and Support Vector Machine (SVM) models integrated with the Synthetic Minority Over-sampling Technique (SMOTE) across various performance metrics, including accuracy, precision, recall, f-measure, and Area Under the Curve (AUC). Using CRISP-DM, the research ensures a systematic data preprocessing, modeling, and evaluation approach. The findings reveal that both DT and SVM models with SMOTE achieve high accuracy rates, with DT yielding an accuracy of 98.37% +/- 0.48% and SVM achieving 98.91% +/- 0.59%. These models effectively distinguish between positive and negative sentiments, as precision, recall, and f-measure scores indicate. Additionally, the AUC scores underscore the robustness of the models in sentiment analysis tasks. These results highlight the potential of CRISP-DM as a structured methodology for sentiment classification research, providing insights into the performance of different machine learning algorithms in handling imbalanced datasets. Based on these findings, it is recommended that future studies further explore the application of CRISP-DM in sentiment analysis tasks and investigate the scalability of DT and SVM models with SMOTE in larger datasets.
Sentiment classification of coral reef 101 content using decision tree algorithm through CRISP-DM Singgalen, Yerik Afrianto
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 Singgalen, Yerik Afrianto
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 Singgalen, Yerik Afrianto
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.
Sentiment Classification of Over-Tourism Issues in Responsible Tourism Content using Naïve Bayes Classifier Afrianto Singgalen, Yerik
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4904

Abstract

The research problem addressed in this study is the analysis of public sentiment regarding over-tourism issues. Utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and the Naive Bayes Classifier (NBC) algorithm, the study navigates through stages of business understanding, data processing, modeling, evaluation, and deployment. The central focus lies in understanding and classifying public sentiments surrounding the challenges associated with over-tourism. The findings reveal that the NBC algorithm, particularly when augmented with Synthetic Minority Over-sampling Technique (SMOTE), demonstrates superior performance metrics, showcasing an accuracy of 84.82%, precision of 91.69%, recall of 76.75%, f-measure of 83.47%, and AUC of 0.838. The comparison with NBC without SMOTE, which registers an accuracy of 78.16%, precision of 87.61%, recall of 74.56%, f-measure of 80.51%, and AUC of 0.745, underscores the significance of addressing class imbalance for improved predictive performance. Integrating CRISP-DM with the NBC algorithm and SMOTE proves instrumental in advancing sentiment analysis methodologies, providing nuanced insights into public perceptions and attitudes concerning the critical issue of over-tourism.
Sentiment Classification of Climate Change and Tourism Content Using Support Vector Machine Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4908

Abstract

This research aims to classify public sentiment regarding the issue of climate change and tourism. The research problem addressed in this study pertains to the classification of public sentiment concerning climate change within the tourism sector. Specifically, the study aims to explore and classify the public's sentiments regarding the impact of climate change on tourism activities.The methodology employed is CRISP-DM, which encompasses stages of business understanding, data understanding, modeling, evaluation, and deployment. Specifically, the SVM and SMOTE algorithms are utilized in the modeling stage to achieve optimal results. By leveraging this systematic approach and advanced algorithms, the study seeks to comprehensively analyze public sentiment towards climate change within the context of tourism, thus contributing valuable insights to academia and industry practitioners. Applying CRISP-DM methodology coupled with SVM and SMOTE algorithms enhances the rigor and effectiveness of sentiment analysis in addressing the complexities of climate change discourse in the tourism sector. The findings of this research demonstrate that the SVM and SMOTE algorithms yield promising results in sentiment classification, with an accuracy of 86.15% +/- 1.68% (micro average: 86.15%), precision of 85.17% +/- 2.16% (micro average: 85.11%) (positive class: Positive), recall of 87.64% +/- 3.39% (micro average: 87.64%) (positive class: Positive), f_measure of 86.34% +/- 1.79% (micro average: 86.35%) (positive class: Positive), and AUC of 0.923 +/- 0.012 (micro average: 0.923) (positive class: Positive). These metrics indicate the effectiveness and reliability of the SVM and SMOTE algorithms in accurately classifying sentiment toward climate change in the context of tourism. The high accuracy, precision, recall, f_measure, and AUC scores suggest that the models produced by these algorithms are robust and capable of capturing nuanced sentiment patterns, thereby contributing to the advancement of sentiment analysis techniques in climate change research within the tourism domain.
Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4924

Abstract

Sentiment analysis plays a pivotal role in comprehending public sentiment, notably within digital communication, where copious amounts of textual data are generated daily. This study delves into the efficacy of sentiment classification models, namely the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), within the imbalanced datasets commonly encountered in sentiment analysis tasks. Employing a comparative analysis methodology, a dataset comprising robot hotel reviews from online platforms is the basis for evaluation. Both NBC and SVM models undergo training and assessment, with and without the Synthetic Minority Over-sampling Technique (SMOTE), to rectify the class imbalance. Performance evaluation relies on critical metrics, including accuracy, recall, precision, f-measure, and Area Under Curve (AUC) to gauge model effectiveness. Findings demonstrate SVM's superiority over NBC in terms of accuracy (SVM: 76.88%, NBC: 67.43%), precision (SVM: 92.03%, NBC: 86.87%), recall (SVM: 58.88%, NBC: 41.00%), f-measure (SVM: 71.78%, NBC: 55.63%), and AUC (SVM: 0.907, NBC: 0.961). Incorporating SMOTE significantly enhances both models' performance, particularly in addressing class imbalance concerns. Although NBC exhibits a more balanced performance across precision and recall metrics, SVM demonstrates heightened accuracy and predictive capability in sentiment classification tasks. These findings underscore the pivotal role of algorithm selection and preprocessing techniques in optimizing sentiment analysis performance, thereby providing invaluable insights for practitioners and researchers alike.
Comprehensive Analysis of Sentiment Classification and Toxicity Assessment in Cultural Documentary Videos Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5150

Abstract

This research explores sentiment classification and toxicity assessment in cultural documentary videos through a systematic analysis framework based on the Cross-Industry Standard Process for Data Mining (CRISP-DM). The study evaluates the sentiment polarity of viewer comments by utilizing a diverse array of machine-learning algorithms, including k-NN, DT, NBC, and SVM. It identifies toxic language patterns across multiple videos. Additionally, the research employs SMOTE to address class imbalance issues and enhance model performance. The results reveal high accuracy rates ranging from 72.24% to 96.79% in sentiment classification, indicating the effectiveness of the proposed methodology. Moreover, toxicity analysis unveils varying degrees of toxic language prevalence, with toxicity scores ranging from 0.01270 to 0.09334 across different videos. Despite these achievements, the study acknowledges the inherent limitations of toxicity scoring algorithms in capturing contextual nuances. Overall, this research contributes to understanding sentiment dynamics and toxicity trends in cultural documentary content and underscores the importance of employing advanced machine learning techniques within a structured analytical framework for insightful data interpretation and decision-making.
Co-Authors A.Y. Agung Nugroho Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel 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 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 Heru Prasadja Heru Prasadja, Heru 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 Samuel Piolo Seingo, Martha Maraka Setiawan, Ruben William Siemens Benyamin Tjhang Sri Yulianto Joko Prasetyo Stephen Aprius Sutresno, Stephen Aprius Suharsono SUHARSONO Suni, Eugenius Kau Tabuni, Gasper Tharsini, Priya Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani