Analisis Sentimen Review Wisatawan Pada Objek Wisata Ubud Menggunakan Algoritma Support Vector Machine

Suryawan, I Wayan Budi; Utami, Nengah Widya; Fredlina, Ketut Queena

doi:10.51401/jinteks.v5i1.2242

Cited by 4 publications

(6 citation statements)

References 5 publications

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“…The Confusion Matrix can be seen in Table 9: = negative predicted and negative factual (true negative) To measure the performance of the classification process, precision, recall and f1-score are calculated. The formula for performance evaluation is as follows: F1-Score is a weighted average by taking recall values and precision to calculate the performance of classification method [20]. Here is the formula for calculating F1-Score in number 3:…”

Section: Discussionmentioning

confidence: 99%

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Sentiment Analysis of Practo App Reviews using KNN and Word2Vec

Farhan

Purbolaksono

Astuti

2023

bits

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The development of technology and communication is used by the community to facilitate daily activities, one of which is in the field of health services. Health services are good enough, but there are still some obstacles that are commonly found, including not allowing to leave the house or a short schedule of doctor consultations. With the presence of health service applications, one of which is Practo, it makes it easier for people to consult online. This convenience makes a lot of reviews regarding the Practo healthcare application. The diversity of opinions on the internet, makes Practo app reviews varied. Therefore, sentiment analysis of Practo app reviews is necessary. In this study, the algorithm used was KNN. The KNN algorithm was chosen because it is very effective if the amount of data is large and easy to implement. The feature extraction used in this study is Word2Vec. Word2Vec was chosen as a feature extraction because it was considered good enough to use because it represented each word with a vector. This research produced the best model built when using stemming with Word2Vec dimensions of 300 and K = 3 values on the KNN parammeter, capable of producing an f1-score of 77.30%.

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Section: Discussionmentioning

confidence: 99%

“…Precision is the comparison of the number of items correctly identified as positive with the number of items identified as positive [20]. Here is the formula for calculating Precision in number 4:…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Practo App Reviews using KNN and Word2Vec

Farhan

Purbolaksono

Astuti

2023

bits

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“…Model support Vector Machine sudah cukup baik dalam pemodelan sehingga mendapatkan hasil akurasi 90.72% seperti halnya dalam penelitian oleh jesika [14] dengan hasil sebesar 90.72% dengan class recall sebesar 90.22% dan class precission sebesar 91.14% dengan membandingan algoritma naïve bayes, SVM dan k-NN diperoleh hasil akurasi tertinggi yaitu algoritma Support Vector Machine (SVM). Penelitian oleh I Wayan Budi Suryawan [15] hasil akurasi sebesar 90.72% dengan class recall sebesar 90.22% dan class precission sebesar 91.14% menunjukkan model melakukan analisis dengan baik. Penelitian terdahulu oleh yang menggunakan teknik SMOTE menghasilkan nilai AUC sebesar 0,9545 artinya penerapan teknik SMOTE lebih baik dibandingan tanpa SMOTE dengan hasil akurasi sebesar 0,9088 [11]…”

Section: Kaitan Literaturunclassified

Penerapan Algoritma Support Vector Machine (Svm) Pada Ulasan Pengunjung Wisata Kabupaten Kuningan

Silviana,

Astuti,

Muhamad Basysyar

2024

jati

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Seiring berkembangnya industri pariwisata di Kabupaten Kuningan, semakin banyak pula pengguna Google Maps yang membagikan ulasan dan opininya tentang berbagai tempat wisata terutama ulasan negatif dari pengunjung dapat memengaruhi citra destinasi pariwisata dan kunjungan wisatawan di masa depan.Permasalahan ini penting karena ulasan negatif dapat berdampak besar pada industri pariwisata.Tujuan dari penelitian ini adalah melakukan analisis sentimen review pengguna google maps terhadap pariwisata di Kabupaten Kuningan.Data diperoleh dari ulasan tempat wisata yang ada di google maps dan telah mendapat persetujuan dari Dinas Pemuda Olahraga dan Pariwisata (DISPORAPAR) kabupaten Kuningan, Jawa Barat. Jumlah ulasan sebanyak 1042 ulasan teks yang diperoleh melalui teknik web scrapping menggunakan website apify dengan atribut rating, teks ulasan dan nama pengunjung yang kemudian diberi label positif dan negatif. Metode yang digunakan adalah support vector machine (SVM untuk mengklasifikasikan ulasan pengunjung wisata kabupaten kuningan.pemodelan menggunakan algoritma Support Vector Machine (SVM) dengan tools yaitu rapidminer yang kemudian dilakukan perhitungan tingkat akurasi, presisi dan recall dari data ulasan tersebut.Hasil klasifikasi sentimen dengan nilai number of fold=10 di dapatkan akurasi sebesar 90.72% dengan class recall sebesar 90.22% dan class precission sebesar 91.14% menunjukkan pemodelan sudah cukup baik dalam menganalisis sentimen ulasan pengunjung wisata kabupaten Kuningan.

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“…The results of the traveler sentiment classification are generated as recommendations for the development of goods and services in tourist places, according to several research. [26] utilizes the Support Vector Machine (SVM) method to identify preferences for goods and services when traveling, using the sentiment approach of travelers visiting Ubud tourism destinations. On the other hand, [27] demonstrates how the branding of popular tourist spots is examined using the lexicon and pivot methodologies.…”

Section: Figure 5 Number Of Resorts Hotels and Homestay In Raja Ampat...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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Analisis Sentimen Review Wisatawan Pada Objek Wisata Ubud Menggunakan Algoritma Support Vector Machine

Cited by 4 publications

References 5 publications

Sentiment Analysis of Practo App Reviews using KNN and Word2Vec

Sentiment Analysis of Practo App Reviews using KNN and Word2Vec

Penerapan Algoritma Support Vector Machine (Svm) Pada Ulasan Pengunjung Wisata Kabupaten Kuningan

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

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