IMPLEMENTATION OF THE K-NN ALGORITHM TO DETERMINE PATTERNS OF TOURIST DESTINATION RECOMMENDATIONS
Abstract
In this digital era, the recommendation system is an important tool in helping users to make decisions, including choosing tourist destinations. This study aims to implement the K-Nearest Neighbors (k-NN) algorithm in determining the recommendation pattern for tourist destinations. The k-NN algorithm was chosen because of its ability to classify based on the closest neighbor of a point, so that it can obtain recommendation patterns that are more personal and relevant to user preferences. This implementation method involves data collection, data preparation, model building, classification process, model evaluation, and finally implementation of the model in a recommendation system. The results of this study indicate that a recommendation system based on the k-NN algorithm is able to provide suggestions for tourist destinations that are more in line with user preferences, thus potentially increasing user satisfaction and making a positive contribution to the tourism industry. However, the effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is very important in the implementation of this algorithm. This research opens opportunities for further research and development in the field of tourist destination recommendation systems using other algorithms or combining several algorithms to obtain more optimal results. The effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is essential in the implementation of this algorithm. This research opens up opportunities for further research and development in the field of tourist destination recommendation systems by using other algorithms or combining several algorithms to get more optimal results. The effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is very important in the implementation of this algorithm. This research opens up opportunities for further research and development in the field of tourist destination recommendation systems by using other algorithms or combining several algorithms to get more optimal results.
Downloads
References
MJ Hadi, "Study of the Potential of New Jurit Village to Become an Educational Tourism Destination,"Alamtana J. Devoted. Mass., vol. 1, no. 1, 2020.
ADP SIAHAAN, "Analysis of the Competitiveness of the Tourism Industry in Medan City,"Univ. North Sumatra, vol. 16, 2020.
Yusuf Fadlila Rachman, Darmawan Lahru Riatma, M. Masbahah, Trisna Ari Roshinta, Fendi Aji Purnomo, and Putri Nugrahaningsih, "PRIORITY RECOMMENDATION SYSTEM FOR TOURISM DESTINATION DEVELOPMENT IN MADIUN DISTRICT,"NAWASENA J. Ilm. Tourist, vol. 1, no. 2, 2022, doi: 10.56910/nawasena.v1i2.231.
Dwi Putra Githa and DP Singgih Putri, "DESIGN OF A TOURISM DESTINATION RECOMMENDATION SYSTEM IN BALI,"SINTECH (Science Inf. Technol. J., vol. 2, no. 2, 2019, doi: 10.31598/sintechjournal.v2i2.388.
W. Septiandari, S. Hidayatullah, S. Alvianna, A. Rachmadian, and DH Sutanto, "The Influence of Image of Tourist Destinations and Service Quality on Visitor Satisfaction at Tiga Warna Beach,"Competence J. Manag. Studs., vol. 15, no. 2, 2021, doi: 10.21107/kompetensi.v15i2.12512.
W. Hadi and A. Yulianto, "Implications of Tourist Attraction, Recommendations for Tourism and Destination Image for Tourist Visits to Sambisari Temple Tourism Object,"Khasanah Ilmu - J. Tourism and Culture, vol. 13, no. 2, 2022, doi: 10.31294/khi.v13i2.13307.
RM Wirakusuma, "Character Analysis of International Tourists on Tourism Facilities in the Prawirotaman Area,"J. Tourism, vol. 1, no. 1, 2014.
CHP Panjaitan, LJ Pangaribuan, and CI Cahyadi, "Analysis of the K-Nearest Neighbor Method Using Rapid Miner for the Recommendation System for Tourist Attractions in Labuan Bajo,"Remik, vol. 6, no. 3, 2022, doi: 10.33395/remik.v6i3.11701.
F. Sologia, R. Aurachman, and PGK Artha, "Tourist Route Recommendations Using the Traveling Salesman Problem Method with the K-Nearest Neighbor Algorithm (Case Study: North Toraja),"e-Proceedings Eng., vol. 7, no. 2, 2020.
R. Alamsyah, AD Wiranata, and R. Rafie, "Defect Detection in Ceramic Tiles Using the K-Nearest Neighbor Method,"Techno.Com, vol. 18, no. 3, 2019, doi: 10.33633/tc.v18i3.2459.
NRA Candra DA, "Branding: Instagramable Videos for Pokdarwis Dharma Jati & the Thinthir Art Community in the Development of 'AB-S' (Nature–Culture–Spiritual) Tourism Villages in Anggrasmanis Village, Karanganyar Regency,"Servant of Arts, vol. 11, no. 1, 2020, doi: 10.33153/abdiseni.v11i1.3128.
A. Pradana and R. Ridwansyah, "Classification of Thesis Topics Based on Meaning with Web Semantic Approach,"J. Comput. Apply., no. Vol. 7 No. 1 (2021), 2021, doi: 10.35143/jkt.v7i1.4603.
SF Mentari, AB Kusdinar, and GP Hartawan, "Indications of Forms of Sexual Deviance Using Web-Based K-Nearest Neighbor,"J. Technol. inform. and Comput., vol. 7, no. 2, 2021, doi: 10.37012/jtik.v7i2.624.
MS Fajri, N. Septian, and E. Sanjaya, "Evaluation of the Implementation of the K-Nearest Neighbors (kNN) Machine Learning Algorithm on Low Resolution Gamma Spectroscopy Data,"Al-Fiziya J. Mater. sci. Geophys. Instrum. Theor. Phys., vol. 3, no. 1, 2020, doi: 10.15408/fiziya.v3i1.16180.
SR Durugkar, R. Raja, KK Nagwanshi, and S. Kumar, “Introduction to data mining,”Data Mining and Machine Learning Applications. 2022, doi: 10.1002/9781119792529.ch1.
B. Devipriya and Y. Kalpana, "Evaluation of sentiment data using classifier model in rapid miner tool,"int. J.Eng. Adv. Technol., vol. 9, no. 1, 2019, doi: 10.35940/ijeat.A1323.109119.
D. Novianti, "Implementation of the Naïve Bayes Algorithm on Hepatitis Data Sets Using Rapid Miner,"Paradig. - J. Comput. and Inform., vol. 21, no. 1, 2019, doi: 10.31294/p.v21i1.4979.
