IMPLEMENTASI SMOTE DAN ALGORITMA MACHINE LEARNING UNTUK MENINGKATKAN AKURASI REKOMENDASI HOTEL

Candra Agustina, Eka Rahmawati, Denny Irawan, Vriska wahyu Tristanti

Abstract


Pariwisata memiliki peran penting dalam perekonomian global, dengan destinasi seperti Candi Borobudur menarik berbagai jenis pengunjung. Untuk meningkatkan pengalaman wisatawan, rekomendasi hotel yang akurat menjadi sangat penting. Namun, data yang tidak seimbang, seperti ulasan positif yang terlalu dominan, sering kali mengurangi kinerja model machine learning yang digunakan untuk rekomendasi. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan Synthetic Minority Over-sampling Technique (SMOTE) guna menyeimbangkan dataset dan meningkatkan akurasi rekomendasi hotel. Beragam algoritma machine learning, termasuk Random Forest, Support Vector Machines, dan Neural Networks, diterapkan dan dievaluasi. Hasil penelitian menunjukkan bahwa penerapan SMOTE secara signifikan meningkatkan kinerja semua model, dengan Random Forest memberikan hasil terbaik. Studi ini menunjukkan bahwa SMOTE, dalam kombinasi dengan teknik machine learning, memberikan solusi yang kuat terhadap ketidakseimbangan kelas pada sistem rekomendasi hotel, sehingga menghasilkan rekomendasi yang lebih andal dan relevan bagi wisatawan. Temuan ini memiliki implikasi penting bagi manajemen hotel dan sektor pariwisata secara keseluruhan.

Keywords


SMOTE; rekomendasi hotel;algoritma machine learning

Full Text:

PDF

References


A. Maulina, I. Sukoco, B. Hermanto, and N. Kostini, “Tourists’ Revisit Intention and Electronic Word-of-Mouth at Adaptive Reuse Building in Batavia Jakarta Heritage,” Sustainability (Switzerland), vol. 15, no. 19, Oct. 2023, doi: 10.3390/su151914227.

C. Martin-Duque, J. J. Fernández-Muñoz, J. M. Moguerza, and A. Ruiz-Rua, “An empirical study on the imbalance phenomenon of data from recommendation questionnaires in the tourism sector,” Journal of Tourism Futures, 2023, doi: 10.1108/JTF-09-2022-0228.

B. Ray, A. Garain, and R. Sarkar, “An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews,” Appl Soft Comput, vol. 98, Jan. 2021, doi: 10.1016/j.asoc.2020.106935.

A. A. Nababan, M. Jannah, and A. H. Nababan, “PREDICTION OF HOTEL BOOKING CANCELLATION USING K-NEAREST NEIGHBORS (K-NN) ALGORITHM AND SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE (SMOTE),” Jurnal Infokum, vol. 10, no. 3, Aug. 2021, [Online]. Available: http://infor.seaninstitute.org/index.php/infokum/index

M. Adil, M. F. Ansari, A. Alahmadi, J. Z. Wu, and R. K. Chakrabortty, “Solving the problem of class imbalance in the prediction of hotel cancelations: A hybridized machine learning approach,” Processes, vol. 9, no. 10, Oct. 2021, doi: 10.3390/pr9101713.

G. Wei, W. Mu, Y. Song, and J. Dou, “An improved and random synthetic minority oversampling technique for imbalanced data,” Knowl Based Syst, vol. 248, Jul. 2022, doi: 10.1016/j.knosys.2022.108839.

H. Sahlaoui, E. A. A. Alaoui, S. Agoujil, and A. Nayyar, “An empirical assessment of smote variants techniques and interpretation methods in improving the accuracy and the interpretability of student performance models,” Educ Inf Technol (Dordr), vol. 29, no. 5, pp. 5447–5483, Apr. 2024, doi: 10.1007/s10639-023-12007-w.

M. Umer et al., “Scientific papers citation analysis using textual features and SMOTE resampling techniques,” Pattern Recognit Lett, vol. 150, pp. 250–257, Oct. 2021, doi: 10.1016/j.patrec.2021.07.009.

J. H. Joloudari, A. Marefat, M. A. Nematollahi, S. S. Oyelere, and S. Hussain, “Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks,” Applied Sciences (Switzerland), vol. 13, no. 6, Mar. 2023, doi: 10.3390/app13064006.

M. Z. Abedin, C. Guotai, P. Hajek, and T. Zhang, “Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk,” Complex and Intelligent Systems, vol. 9, no. 4, pp. 3559–3579, Aug. 2023, doi: 10.1007/s40747-021-00614-4.

C. Azad, B. Bhushan, R. Sharma, A. Shankar, K. K. Singh, and A. Khamparia, “Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus,” in Multimedia Systems, Springer Science and Business Media Deutschland GmbH, Aug. 2022, pp. 1289–1307. doi: 10.1007/s00530-021-00817-2.

L. Sha, M. Rakovic, A. Das, D. Gasevic, and G. Chen, “Leveraging Class Balancing Techniques to Alleviate Algorithmic Bias for Predictive Tasks in Education,” IEEE Transactions on Learning Technologies, vol. 15, no. 4, pp. 481–492, Aug. 2022, doi: 10.1109/TLT.2022.3196278.

Barbara. Jarmulska, Random forest versus logit models : which offers better early warning of fiscal stress? [European Central Bank], 2020.

B. Gaye, D. Zhang, and A. Wulamu, “Improvement of Support Vector Machine Algorithm in Big Data Background,” Math Probl Eng, vol. 2021, 2021, doi: 10.1155/2021/5594899.

C. Y. Lee, H. Hasegawa, and S. Gao, “Complex-Valued Neural Networks: A Comprehensive Survey,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 8, pp. 1406–1426, Aug. 2022, doi: 10.1109/JAS.2022.105743.

G. Xu, M. Liu, Z. Jiang, D. Söffker, and W. Shen, “Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning,” Sensors (Switzerland), vol. 19, no. 5, Mar. 2019, doi: 10.3390/s19051088.

Z. Jing, “Application of Random Forest-based supervised ensemble learning method for hail nowcasting in the Midwestern United States,” 2023.

A. Zaidi and A. S. M. Al Luhayb, “Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression,” Math Probl Eng, vol. 2023, no. 1, Jan. 2023, doi: 10.1155/2023/5525675.

P. Srisuradetchai and K. Suksrikran, “Random kernel k-nearest neighbors regression,” Front Big Data, vol. 7, 2024, doi: 10.3389/fdata.2024.1402384.

R. K. Halder, M. N. Uddin, M. A. Uddin, S. Aryal, and A. Khraisat, “Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications,” J Big Data, vol. 11, no. 1, Dec. 2024, doi: 10.1186/s40537-024-00973-y.

E. Wong, S. M. Rasoolimanesh, and S. Pahlevan Sharif, “Using online travel agent platforms to determine factors influencing hotel guest satisfaction,” Journal of Hospitality and Tourism Technology, vol. 11, no. 3, pp. 425–445, Oct. 2020, doi: 10.1108/JHTT-07-2019-0099.

A. Sarkar, T. Chowdhury, R. R. Murphy, A. Gangopadhyay, and M. Rahnemoonfar, “SAM-VQA: Supervised Attention-Based Visual Question Answering Model for Post-Disaster Damage Assessment on Remote Sensing Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023, doi: 10.1109/TGRS.2023.3276293.

H. Fang, D. Zhang, Y. Shu, and G. Guo, “Deep Learning for Sequential Recommendation,” ACM Trans Inf Syst, vol. 39, no. 1, Nov. 2020, doi: 10.1145/3426723.

J. Kim, D. Franklin, M. Phillips, and E. Hwang, “Online Travel Agency Price Presentation: Examining the Influence of Price Dispersion on Travelers’ Hotel Preference,” J Travel Res, vol. 59, no. 4, pp. 704–721, Apr. 2020, doi: 10.1177/0047287519857159.

D. Elreedy, A. F. Atiya, and F. Kamalov, “A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning,” Mach Learn, vol. 113, no. 7, pp. 4903–4923, Jul. 2024, doi: 10.1007/s10994-022-06296-4.

D. Chicco and G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,” BMC Genomics, vol. 21, no. 1, Jan. 2020, doi: 10.1186/s12864-019-6413-7.




DOI: http://dx.doi.org/10.21927/ijubi.v7i2.5141

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Indonesian Journal of Business Intelligence (IJUBI)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Lisensi Creative Commons
IJUBI by https://ejournal.almaata.ac.id/index.php/IJUBI is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

View My Stats
Indonesian Journal of Business Intelligence (IJUBI)
Department of Information System
Alma Ata University
Email: ijubi@almaata.ac.id