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.

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.

scholarly journals Perbandingan Metode Klasifikasi Berita Hoaks Berbahasa Indonesia Berbasis Pembelajaran Mesin

Repositor ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 675
Author(s):  
Muhammad Athaillah ◽  
Yufiz Azhar ◽  
Yuda Munarko
Keyword(s):  
Test Data ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Features Selection ◽  
Comparison Of Algorithms ◽  
Bayes Algorithm

AbstrakKlasifiaksi berita hoaks merupakan salah satu aplikasi kategorisasi teks. Berita hoaks harus diklasifikasikan karena berita hoaks dapat mempengaruhi tindakan dan pola pikir pembaca. Dalam proses klasifikasi pada penelitian ini menggunakan beberapa tahapan yaitu praproses, ekstraksi fitur, seleksi fitur dan klasifikasi. Penelitian ini bertujuan membandingkan dua algoritma yaitu algoritma Naïve Bayes dan Multinomial Naïve Bayes, manakah dari kedua algoritma tersebut yang lebih efektif dalam mengklasifikasikan berita hoaks. Data yang digunakan dalam penelitian ini berasal dari www.trunbackhoax.id untuk data berita hoaks sebanyak 100 artikel dan data berita non-hoaks berasal dari kompas.com, detik.com berjumlah 100 artikel. Data latih berjumlah 140 artikel dan data uji berjumlah 60 artikel. Hasil perbandingan algoritma Naïve Bayes memiliki nilai F1-score sebesar 0,93 dan nilai F1-score Multinomial Naïve Bayes sebesar 0,92. Abstarct Classification hoax news is one of text categorizations applications. Hoax news must be classified because the hoax news can influence the reader actions and thinking patterns. Classification process in this reseacrh uses several stages, namely  preprocessing, features extraxtion, features selection and classification. This research to compare Naïve Bayes algorithm and Multinomial Naïve Bayes algorithm, which of the two algorithms is more effective on classifying hoax news. The data from this research  from  turnbackhoax.id as hoax news of 100 articles and non-hoax news from kompas.com, detik.com of 100 articles. Training data 140 articles dan test data 60 articles. The result of the comparison of algorithms  Naïve Bayes has an F1-score value of 0,93 and Naïve Bayes has an F1-score value of  0,92.

scholarly journals Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City

2021 ◽  
Vol 328 ◽  
pp. 04011
Author(s):  
Alwin Ali ◽  
Amal Khairan ◽  
Firman Tempola ◽  
Achmad Fuad
Keyword(s):  
Test Data ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Calculation Results ◽  
Bayes Algorithm ◽  
Correct Data ◽  
Rain Data ◽  
Naive Bayes Method

The amount of rainfall that occurs cannot be determined with certainty, but it can be predicted or estimated. In predicting the potential for rain, data mining techniques can be used by classifying data using the naive Bayes method. Naïve Bayes algorithm is a classification method using probability and statistical methods. The purpose of this study is how to implement the naive Bayes method to predict the potential for rain in Ternate City, and be able to calculate the accuracy of the Naive Bayes method from system created. The highest calculation results with new data with a total of 400 training data and 30 test data, obtained 30 correct data with 100% precision, 100% recall and 100% accuracy and the lowest calculation results with new data with a total of 500 training data and 50 test data, obtained 38 correct data and 12 incorrect data with a percentage of precision 61.29%, recall 100% and accuracy 76%.

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 Penerapan Metode Naive Bayes dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen

2019 ◽  
Vol 1 ◽  
pp. 287
Author(s):  
Desi Ratna Sari ◽  
Dedy Hartama ◽  
Irfan Sudahri Damanik ◽  
Anjar Wanto
Keyword(s):  
Higher Education ◽  
Student Satisfaction ◽  
Teaching Methods ◽  
Naive Bayes ◽  
Teaching Method ◽  
Higher Education Institutions ◽  
Naïve Bayes ◽  
Training Data ◽  
Testing Data ◽  
Bayes Algorithm

This research aims to classify in determining student satisfaction with teaching methods at STIKOM Tunas Bangsa. Data obtained from the results of the 2015 and 2016 semester student questionnaires were odd, with a sample of 80 students. Attributes used are 4, namely communication (C1), Building learning atmosphere (C2), Assessment of students (C3) and delivery of material (C4). The method used in this study is the Naïve Bayes Algorithm and is processed using RapidMiner studio 5.3 software to determine student satisfaction with teaching methods. Training data used 100 data while testing data used in manual calculations as much as 5 data. From the results of data testing the five data expressed satisfaction with the way teaching lecturers at STIKOM Tunas Bangsa. While the training data that is processed with RapidMiner has an accuracy of 92.00%. With this analysis, it is expected to be able to help higher education institutions to evaluate the performance of lecturers, especially in evaluating one of the three triharma colleges, namely the teaching method of lecturers.

scholarly journals Application of the Naïve Bayes Algorithm for Student Graduation Analysis

2018 ◽  
Vol 7 (4.15) ◽  
pp. 421
Author(s):  
Erick Akhmad Fahmi Alfa’izy ◽  
Khairil Anam ◽  
Naidah Naing ◽  
Rosanita Tritias Utami ◽  
Nur Anim Jauhariyah ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
College System ◽  
Student Graduation ◽  
Bayes Algorithm ◽  
Using Data ◽  
Analysis System ◽  
Law Student

Design an analysis system to find out graduation by comparing previous data and existing data to overcome errors in a college system. By taking data records that are already available to be processed using the naïve Bayes algorithm. This research was conducted at Universitas Maarif Hasyim Latif. In this case, the object of research is to analyze the data of students with naïve Bayes algorithms to find out their graduation. For sampling the data taken is the previous Faculty of Law Student data to be used as training data, to retrieve the entire data using data records that are already available in the Directorate of Information Systems. That the naïve Bayes algorithm can be used in the classification of data in the form of a string or textual. This is based on researchers' trials in taking examples of calculations that have been done before. To compare the results of the classification of graduation analysis using the naïve Bayes algorithm testing is done with a sample of data in the form of training data compared to data testing. From the calculations that have been made, the accuracy is 77.78%. 

scholarly journals Prediksi Angka Kelahiran Bayi Pada Desa Tridaya Sakti Dengan Menggunakan Algoritma Naive Bayes

2020 ◽  
Vol 1 (2) ◽  
pp. 77-88
Author(s):  
Nur Isnaini Parihah ◽  
Sari Hartini ◽  
Juarni Siregar
Keyword(s):  
Data Mining ◽  
Population Growth ◽  
Naive Bayes ◽  
Large Population ◽  
Naïve Bayes ◽  
Training Data ◽  
Birth Rates ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Infant Birth

The birth rate is something that can affect the increase in population growth. Large population is a burden for development. According to Malthus's Theory which states that a large population growth is not the welfare that is obtained but rather poverty will be encountered if the population is not well controlled. The number of baby births in Tridaya Sakti Village is increasing every year. Therefore Data Mining using the Naive Bayes algorithm can help in the calculation of predicting infant birth rates in Tridaya Sakti Village. Data Mining in predicting the number of infant birth rates aims to determine the number of infant birth rates for the coming year using the Naive Bayes algorithm. By looking at the prediction patterns of each variable and testing training data on testing data. It is hoped that the Naive Bayes algorithm can solve the problem in Tridaya Sakti Village in handling and overcoming the calculation of infant birth rates and can help the Tridaya Sakti Village in regulating population growth in the coming years. The results obtained from the data that have been taken and calculated by Data Mining using the Naive Bayes algorithm produce an information that can be used as a reference to find out the number of births. Performance and time in data processing are more effective and efficient as well as more accurate and accurate predictions of the number of baby births.   Keywords: Naive Bayes, Birth of a Baby, Prediction   Abstrak   Angka kelahiran merupakan suatu hal yang dapat mempengaruhi peningkatan pertumbuhan penduduk. Jumlah penduduk yang besar merupakan beban bagi pembangunan. Menurut Teori Malthus yang menyatakan bahwa pertumbuhan jumlah penduduk yang besar bukanlah kesejahteraan yang didapat tapi justru kemelaratan akan ditemui bilamana jumlah penduduk tidak dikendalikan dengan baik. Jumlah angka kelahiran bayi di Desa Tridaya Sakti setiap tahunnya semakin bertambah. Maka dari itu Data Mining dengan menggunakan algoritman Naive Bayes dapat membantu dalam perhitungan memprediksi angka kelahiran bayi di Desa Tridaya Sakti. Data Mining dalam memprediksi jumlah angka kelahiran bayi bertujuan untuk mengetahui jumlah angka kelahiran bayi tahun yang akan mendatang mengunakan algoritma Naive Bayes. Dengan melihat pola prediksi dari setiap variabel dan melakukan pengujian data training terhadap data testing. Diharapkan algoritma Naive Bayes ini dapat menyelesaikan permasalahan di Desa Tridaya Sakti dalam menangani dan mengatasi perhitungan angka kelahiran bayi dan dapat membantu pihak Desa Tridaya Sakti dalam mengatur pertumbuhan jumlah penduduk tahun yang akan mendatang. Hasil yang diperoleh dari data yang sudah diambil dan dihitung dengan Data Mining mengunakan algoritam Naive Bayes menghasilkan sebuah informasi yang dapat digunakan sebagai acuan untuk mengetahui jumlah angka kelahiran bayi. Kinerja dan waktu dalam proses pengolahan data lebih efektif dan efesien serta dari prediksi jumlah kelahiran bayi lebih tepat dan akurat. Kata Kunci: Naive Bayes, Kelahiran Bayi, Prediks  

scholarly journals Sistem Pendukung Pengambilan Keputusan Kesesuaian Beasiswa Menggunakan Naive Bayes

PRAXIS ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 187
Author(s):  
Tan, Yohanes Christianto Aryo Wicaksono ◽  
YB Dwi Setianto
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Decision Makers ◽  
Training Data ◽  
Web Based ◽  
Catholic University ◽  
Scholarship Recipients ◽  
Bayes Algorithm ◽  
To Receive ◽  
And Training

Until now there are no system that helps determine candidates who are eligible to receive the Sandjojo Foundation scholarship at Soegijapranata Catholic University. Therefore, scholarship recipients are sometimes not on target. This research can help solve problems by using the Naive Bayes algorithm as a medium for decision makers with criteria such as based on the student's GPA, the total income of parents, and must be an active student in the organization. Using previous registrant data, the Naive Bayes algorithm can study the data for each of the criteria entered to determine whether applicants qualify or not to receive scholarships from the Sandjojo Foundation. This research was made using a web-based application and has been tested four (4) times with a variety of test and training data, with an accuracy of around 50% to 65% and a time of less than three (3) seconds..

scholarly journals Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

2021 ◽  
Vol 5 (2) ◽  
pp. 640
Author(s):  
Mulkan Azhari ◽  
Zakaria Situmorang ◽  
Rika Rosnelly
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Svm Algorithm ◽  
Testing Data ◽  
C4.5 Algorithm ◽  
Bayes Algorithm ◽  
Using Data

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm

scholarly journals Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

2018 ◽  
Vol 3 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Arya Kusuma ◽  
De Rosal Ignatius Moses Setiadi ◽  
M. Dalvin Marno Putra
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Classifier ◽  
Nutritional Content ◽  
Classification Technique ◽  
Computational Speed ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Histogram Feature

Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracy

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