SISTEM REKOMENDASI TEMPAT WISATA MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN NAÏVE BAYES

2022 ◽  
Vol 10 (2) ◽  
pp. 227
Author(s):  
Ida Bagus Gede Dwidasmara ◽  
I Gusti Ngurah Agung Widiaksa Putra ◽  
I Made Widiartha ◽  
I Wayan Santiyasa ◽  
Ida Bagus Made Mahendra ◽  
...  
Keyword(s):  
Recommendation System ◽  
Naive Bayes ◽  
Cultural Tourism ◽  
Naïve Bayes ◽  
Tourist Attractions ◽  
Tourist Destinations ◽  
Personal Motivation ◽  
Tourism Sector ◽  
Bayes Algorithm ◽  
Insertion Heuristic

Bali is one of the best tourism areas in Indonesia, as evidenced in 2016 Bali received a number of awards on the TripAdvisor Travelers Choice Award in global and Asian scope. However, the Corona virus outbreak from 2019, caused the tourism sector in Bali to decline, thus a solution is needed to restore the tourism sector in Bali, where one solution is to increase cultural tourism to the maximum, as the main attraction of tourist destinations in Bali. Bali. So the author proposes a tourism recommendation system, which aims to recommend tourist attractions that are suitable for tourists, which in this recommendation system is also recommended cultural tourism destinations that are directly recommended by the community, and there is also a mapping of tourist attractions as part of a tourist recommendation system, mapping of tourist attractions public and cultural attractions. In this tourism recommendation system, using the Naïve Bayes Algorithm to recommend general tourist destinations based on the personal motivation of tourists, which is based on the attributes of age, gender, natural interest, artificial interest, cultural interest of tourists, using 200 training data consisting of 14 classes of tourist attractions. . In addition, this tourist recommendation system is equipped with recommendations for routing tourist attractions using the Cheapest Insertion Heuristic Algorithm, to arrange a list of tourist attractions. Keywords: Recommendation System, Naïve Bayes Algorithm, Cheapest Insertion Heuristic Algorithm, Personal Motivation, Place Mapping.

Optimization of Bali Tourism Recommendations Based on Personal Motivation of Tourists Using the Naive Bayes Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 83
Author(s):  
I Gusti Ngurah Agung Widiaksa Putra ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Tourist Attractions ◽  
Travel Agents ◽  
Tourist Destinations ◽  
Personal Motivation ◽  
Tourism Sector ◽  
Bayes Algorithm

In the recovery of the tourism sector in Bali due to COVID-19, a solution is needed with the aim of making tourists more interested in having a vacation in Bali. One of the solutions that can be offered is optimizing the tourist recommendations on the island of Bali, because so far tourists only get travel recommendations from travel agents and guides who usually recommend favorite tourist destinations, and sometimes guides recommend tourist attractions according to their personal wishes or goals. making tourists less optimal in enjoying tourist attractions in Bali. Optimization of Bali Tourism Recommendations Based on Tourist Personal Motivation Using the Naive Bayes Algorithm, is one solution to optimize tourism recommendations in Bali, where tourist recommendations are taken based on tourist characteristics using the Naïve Bayes Algorithm. In this study the authors used 180 training data, and the results of this study indicate that the personal motivation of tourists who are processed using the Naïve Bayes algorithm is feasible to use for tourism recommendations in Bali. Keywords: Recommendation Optimization, Personal Motivation, Naïve Bayes.

scholarly journals Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor

2020 ◽  
Vol 7 (4) ◽  
pp. 737
Author(s):  
Sitti Aliyah Azzahra ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Emotional Expressions ◽  
Tourist Attraction ◽  
Test Results ◽  
Tourist Attractions ◽  
Bayes Algorithm ◽  
Labeling Method ◽  
The City

<p class="Abstrak">Wisatawan seringkali mencari informasi tentang obyek wisata pada situs web seperti TripAdvisor. Situs web TripAdvisor memiliki fitur bagi penguna terdaftar untuk memberi ulasan tentang objek wisata dalam kategori kuliner dari berbagai negara. Ulasan tersebut bisa digunakan wisatawan sebagai pertimbangan sebelum mendatangi objek wisata kuliner yang ingin dituju. Komentar atau ulasan yang ada di situs TripAdvisor dapat dianalisis untuk mengetahui nilai sentimen dari suatu obyek wisata yang diulas. Hasil analisis itu dapat bermanfaat bagi pengelola tempat wisata, pengusaha kuliner maupun bagi wisatawan lain. Ada tantangan yang ditemukan saat analisis sentimen dilakukan pada kalimat ulasan yang mengandung ikon emosi atau <em>emoticon</em>, karena ulasan dapat mengandung arti sentimen yang berbeda antara kalimat dengan ekspresi emosi yang ada. Penelitian ini berisi analisis ulasan tentang kuliner kota Bandung pada situs TripAdvisor yang mengklasifikasi sentimen menjadi tiga kelas. Penelitian ini menggunakan teknik klasifikasi data mining dengan <em>algoritme Naïve Bayes</em> dikombinasi dengan metode pelabelan multi aspek yang disertai konversi ikon emosi pada teks ulasan. Selain itu, analisis dilakukan pada bobot ulasan berdasarkan jumlah kontribusi pemberi ulasan di web TripAdvisor. Hasil pengujian menunjukkan bahwa penggunaan seluruh kombinasi metode tersebut dalam proses klasifikasi sentimen mampu menghasilkan nilai akurasi sebesar 98,67%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Tourists often look for information about attractions on websites such as TripAdvisor. The TripAdvisor website has a feature for registered users to provide reviews about attractions in the culinary category from various countries. These reviews can be used by tourists as a consideration before visiting culinary attractions to be addressed. Comments or reviews on the TripAdvisor site can be analyzed to determine the sentiment value of a tourist attraction being reviewed. The results of the analysis can be useful for managers of tourist attractions, culinary entrepreneurs and for other tourists. There are challenges that are found when sentiment</em><em> </em><em>analysis is carried out on review sentences that contain emotion icons or emoticons, because reviews </em><em>may</em><em> contain different sentiment meanings between sentences and existing emotional expressions. This study contains a review of the culinary analysis of the city of Bandung on the TripAdvisor site which classifies sentiments into three classe</em><em>s</em><em>. This study uses data mining classification techniques with the Naïve Bayes algorithm combined with a multi-aspect labeling method accompanied by the conversion of emotional icons in the review text. In addition, the analysis is carried out on the weight of the review based on the number of contributing reviewers on the TripAdvisor web. The test results show that the use of all combinations of these methods in the sentiment classification process is able to produce an accuracy value of 98.67%.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals PEMANFAATAN DATA INSTAGRAM UNTUK MENGETAHUI REPUTASI TEMPAT WISATA DI LOMBOK

2020 ◽  
Vol 1 (1) ◽  
pp. 39-46
Author(s):  
Muhammad Azmi ◽  
Amiruddin Khairul Huda ◽  
Arief Setyanto
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tourism Industry ◽  
Bayes Classifier ◽  
Entertainment Media ◽  
Tourist Attractions ◽  
Tourist Destinations ◽  
Use Of Social Media ◽  
Share Information

Social media currently has an extraordinary influence in decision making, including in the tourism industry to find out the reputation and popularity of a tourist destination. At present the use and use of social media is not only limited to entertainment media but also as a medium or a place to share information both information that is considered important or not important and neglected. In this study, we utilize data scattered through Instagram posts related to tourist destinations to be used as references to find out the reputation of tourist destinations on the island of Lombok. The data used comes from Instagram with a total of 600 datasets, using three keywords namely beach, dyke and hill. The method used is the Naive Bayes Classifier which will be used to classify postings and Instagram comments by classifying into positive, negative and neutral posts to tourist destinations. The results of this study can show that the accuracy of naive bayes is 59%, while tourist attractions or tourist destinations which are categorized as popular for the beach are Kuta Beach with 85% percentage and for the Gili category that is 47% Gili Air namely, mountain Mount Rinjani with a percentage of 60%.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

IMPLEMENTASI TEOREMA NAÏVE BAYES PADA SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN REKOMENDASI TEMPAT WISATA TERBAIK DISULAWESI SELATAN

2019 ◽  
Vol 2 (1) ◽  
pp. 40-46
Author(s):  
Rikardo Chandra ◽  
Izmy alwiah Musdar ◽  
Junaedy .
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Programming Language ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Based ◽  
Tourist Attractions ◽  
South Sulawesi

This study aims to design and build web-based decision support system applications used to recommend the best tourist attractions in South Sulawesi to tourists. The expected benefit of this research is to help the user get the best tourist recommendation information available in South Sulawesi based on the conditions in input factors. The theorem or method used in this study, namely the theorem Naïve Bayes. The design of the system isimplemented using PHP programming language and MYSQL database. Based on the results of the research, the authors have successfully built the application of decision support system to determine the recommendation of tourist attractions in South Sulawesi with 65% accuracy based on 20 tests conducted.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

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