Analisis Sentimen Wisatawan terhadap Taman Nasional Bunaken dan Top 10 Hotel Rekomendasi Tripadvisor Menggunakan Algoritma SVM dan DT berbasis CRISP-DM

Singgalen, Yerik Afrianto

doi:10.47065/josyc.v4i2.3092

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Closing this research gap is essential for informing evidence-based policies, sustainable business practices, and holistic strategies to foster responsible technological innovation and inclusive tourism development. The contribution of this research, which focuses on conducting a comparative analysis of sentiment classification models using Naive Bayes classifier and Support Vector Machine (SVM) on robot hotel content, is significant in bridging the gap in sentiment analysis methodologies within the context of digital tourism [39], [40]. By systematically evaluating the performance of these two widely-used classifiers, the study provides valuable insights into their effectiveness in accurately categorizing public sentiments towards robot hotels.…”

Section: Research Gap and Trends Mapping: Climate Change And Tourismmentioning

confidence: 99%

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Singgalen

2024

JoSYC

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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.

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Section: Research Gap and Trends Mapping: Climate Change And Tourismmentioning

confidence: 99%

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Singgalen

2024

JoSYC

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“…The CRISP-DM technique can be Comparing the CRISP-DM approach to other data analysis frameworks, there are several benefits. Initially, it is a thorough and systematic process that directs analysts through every phase of the data analysis process [11]. Second, it is adaptable and can be used for different data analysis tasks, making it appropriate for many sectors and areas [12].…”

Section: Tripadvisor Webharvy Figure 2 Scraping Data From Tripadvisor...mentioning

confidence: 99%

Sentiment Analysis of Raja Ampat Tourism Destination Using CRISP-DM: SVM, NBC, DT, and k-NN Algorithm

Laturiuw

Singgalen

2023

J. Inf. Syst. Informatics

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This study presents a sentiment analysis of tourists' opinions on Raja Ampat Tourism Destination using data mining techniques. The study collected data from Tripadvisor and processed it through sentiment classification. The algorithms used in the analysis were Support Vector Machine, Naive Bayes Classifier, Decision Tree, and k-Nearest Neighbor. The study followed the Cross-Industry Standard for Data Mining methodology and went through several stages such as business and data comprehension, data preparation and cleaning, feature selection, modeling, model evaluation, result presentation, deployment, and maintenance. The study's findings revealed that visitors generally had positive opinions about Raja Ampat's tourism attractions, particularly cultural diversity, and undersea beauty. The Decision Tree algorithm showed the highest accuracy value of 99.12%, precision of 98.96%, recall of 99.34%, AUC of 0.991, and f-measure of 99.13%. SVM also had excellent performance with an accuracy value of 100%, precision of 100%, recall of 100%, AUC of 1.000, and f-measure of 100%. The study concludes that Decision Tree and SVM, with the assistance of SMOTE operators, are the best algorithms for sentiment analysis in this context.

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“…Berdasarkan hasil penelusuran ilmiah, belum ditemukan kajian yang secara spesifik membahas tentang analisis sentimen dan sistem pendukung keputusan menggunakan metode Cross Industry Standard Process for Data Mining (CRISP-DM) dan Simple Additive Weighting (SAW) berdasarkan data hotel dari Tripadvisor yang diseleksi berdasarkan perankingan wisatawan (traveler ranked, best value, dan distance to city center) serta perbandingan hasil perankingan berdasarkan location, cleanliness, services, dan value. Penelitian ini merupakan pengembangan dari hasil penelitian terdahulu yang lebih menekankan pada metode, algoritma, serta bobot kriteria dan alternatif dalam sistem pendukung keputusan [5]- [11]. Mempertimbangkan hal tersebut maka penelitian ini menawarkan gagasan untuk megelaborasi model klasifikasi sentimen CRISP-DM dengan sistem pendukung keputusan SAW.…”

Section: Pendahuluanunclassified

Analisis Sentimen dan Sistem Pendukung Keputusan Menginap di Hotel Menggunakan Metode CRISP-DM dan SAW

Singgalen

2023

josh

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Research combining sentiment analysis approaches with decision support systems in the hospitality industry is still very limited. This study aims to analyze hotel guest sentiment based on location, cleanliness, service, and value criteria using Cross Industry Standard Process for Data Mining (CRISP-DM) to identify five and ten famous words based on sentiment data and the sentiment classification model with the best performance. Furthermore, the results of identifying five and ten famous words based on sentiment data related to location, cleanliness, service, and value ratings were processed using the Simple Additive Weighting (SAW) method. The results of this study show that the performance of SVM algorithms using SMOTE operators has accuracy values (99.74%), precision values (100%), recall values (99.48%), f-measure values (99.74%), and AUC (100%). This shows that sentiment classification results using the SVM algorithm with SMOTE operators can be used as a sentiment data classification model with 30% training and 70% test data. This model is relevant to be used as a model for the classification process of 1680 review data obtained from the Tripadvisor website at Grand Zuri Malioboro Hotel Yogyakarta, Tentrem Hotel Yogyakarta, Grand Ambarukmo Hotel, El Royale Yogyakarta Malioboro, and Ceria Hotel @alun alun Yogyakarta. Specifically, aspects that concern guests relate to staff, breakfast, room, and service. In addition, the results of the implementation of the SAW method show that Grand Zuri Malioboro Hotel Yogyakarta ranks first with a total value of 0.907663917. Otherwise, Ceria Hotel @alun Yogyakarta Square ranks second with a total value of 0.897193878. Meanwhile, Hotel Tentrem Yogyakarta ranks third with a total value of 0.861265289. This shows that hotel guests who assess the criteria of location, cleanliness, service, and value by giving value weights as rated on the Tripadvisor website and provide importance weights with similar values, where location (0.20), cleanliness (0.25), service (0.30), value (0.25) then the alternative with the highest weight is Hotel Grand Zuri Malioboro Yogyakarta.

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Analisis Sentimen Wisatawan terhadap Taman Nasional Bunaken dan Top 10 Hotel Rekomendasi Tripadvisor Menggunakan Algoritma SVM dan DT berbasis CRISP-DM

Cited by 3 publications

References 27 publications

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Sentiment Analysis of Raja Ampat Tourism Destination Using CRISP-DM: SVM, NBC, DT, and k-NN Algorithm

Analisis Sentimen dan Sistem Pendukung Keputusan Menginap di Hotel Menggunakan Metode CRISP-DM dan SAW

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