LightGBM-Based Classification of Customer Feedback in Restaurant X
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This research aims to classify customer feedback from Restaurant X using the LightGBM model to enhance service quality and customer satisfaction amidst growing industry competition. Customer feedback, collected through surveys and online platforms, is analyzed to uncover patterns and trends related to various aspects of the dining experience. The methodology encompasses data collection, preprocessing, model training, and evaluation. LightGBM, renowned for its efficiency and accuracy with large datasets, serves as the primary tool for building a robust classification model. Analysis reveals that key features such as food quality, service, and cleanliness significantly influence customer satisfaction. The model demonstrates high classification accuracy, providing actionable insights for Restaurant X management. These insights enable targeted strategies for improving specific areas of service, fostering better customer experiences and driving loyalty. The research underscores the importance of leveraging advanced machine learning models like LightGBM for data-driven decision-making in the restaurant industry.
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Sri Murdhani, I. D. A., & B, M. (2025). LightGBM-Based Classification of Customer Feedback in Restaurant X. Jurnal Sistem Informasi Dan Komputer Terapan Indonesia (JSIKTI), 6(4), 195-204. https://doi.org/10.33173/jsikti.236
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
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[2] Mishra, R. (2018). Logistic regression for customer feedback analysis. Kaggle. https://www.kaggle.com/code/ranjitmishra/logistic-regression-customer-feedback.
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[13] Jung, M., & Lee, Y. (2018). Classifying customer feedback using machine learning techniques. UBC Master of Data Science. https://ubc-mds.github.io/Customer-Feedback-Classification/.
[1] Singh, J., & Sharma, S. (2018). Predicting customer feedback using machine learning and logistic regression. Journal of Data Science and Analytics, 10(1), 25-34.
[2] Mishra, R. (2018). Logistic regression for customer feedback analysis. Kaggle. https://www.kaggle.com/code/ranjitmishra/logistic-regression-customer-feedback.
[3] Prasad, A., & Chahal, R. (2018). Customer feedback prediction using machine learning.GeeksforGeeks.https://www.geeksforgeeks.org/customer-feedback-prediction-machine-learning/.
[4] Lundy, D. (2018). A practical guide to customer feedback prediction using logistic regression. Medium.https://medium.com/%40daniel.lundy.analyst/a-practical-guide-to-feedback-prediction-using-logistic-regression-f390c5c4d71f.
[5] Thakur, M., & Wadhwa, P. (2018). Customer feedback prediction by supervised learning.JournalofDataScienceandEngineering.https://drpress.org/ojs/index.php/HSET/article/view/13586.
[6] Tata, P. (2018). Predicting customer feedback using logistic regression. YouTube. https://www.youtube.com/watch?v=UkzV1e4tRyk.
[7] Kaur, N., & Agarwal, A. (2018). Predicting customer feedback using logistic regression. Kaggle.https://www.kaggle.com/learn/predicting-customer-feedback-using-logistic regression.
[8] Chaudhary, M., & Verma, R. (2018). Customer feedback prediction using logistic regression: A comparative research. Journal of Machine Learning and Analytics, 8(3), 48-55.
[9] Singh, R., & Kaur, J. (2018). Analysis of customer feedback dataset. StackOverflow. https://stackoverflow.com/questions/52037745/analysis-of-customer-feedback-dataset.
[10] Patel, R., & Patel, S. (2018). Customer feedback predictor. RPubs. https://rpubs.com/shradhit/customerfeedback.
[11] Ateş, F. (2018). Classifying customer feedback using machine learning algorithms. IEEE Xplore. https://ieeexplore.ieee.org/document/9009111.
[12] Zhou, C., & Wang, X. (2018). Forecasting customer satisfaction: A comparative examination ofmachinelearningapproaches.ResearchGate.https://www.researchgate.net/publication/377818483_Forecasting_customer_satisfaction_A_comparative_examination_of_machine_learning_approaches.
[13] Jung, M., & Lee, Y. (2018). Classifying customer feedback using machine learning techniques. UBC Master of Data Science. https://ubc-mds.github.io/Customer-Feedback-Classification/.
[2] Mishra, R. (2018). Logistic regression for customer feedback analysis. Kaggle. https://www.kaggle.com/code/ranjitmishra/logistic-regression-customer-feedback.
[3] Prasad, A., & Chahal, R. (2018). Customer feedback prediction using machine learning.GeeksforGeeks.https://www.geeksforgeeks.org/customer-feedback-prediction-machine-learning/.
[4] Lundy, D. (2018). A practical guide to customer feedback prediction using logistic regression. Medium.https://medium.com/%40daniel.lundy.analyst/a-practical-guide-to-feedback-prediction-using-logistic-regression-f390c5c4d71f.
[5] Thakur, M., & Wadhwa, P. (2018). Customer feedback prediction by supervised learning.JournalofDataScienceandEngineering.https://drpress.org/ojs/index.php/HSET/article/view/13586.
[6] Tata, P. (2018). Predicting customer feedback using logistic regression. YouTube. https://www.youtube.com/watch?v=UkzV1e4tRyk.
[7] Kaur, N., & Agarwal, A. (2018). Predicting customer feedback using logistic regression. Kaggle.https://www.kaggle.com/learn/predicting-customer-feedback-using-logistic regression.
[8] Chaudhary, M., & Verma, R. (2018). Customer feedback prediction using logistic regression: A comparative research. Journal of Machine Learning and Analytics, 8(3), 48-55.
[9] Singh, R., & Kaur, J. (2018). Analysis of customer feedback dataset. StackOverflow. https://stackoverflow.com/questions/52037745/analysis-of-customer-feedback-dataset.
[10] Patel, R., & Patel, S. (2018). Customer feedback predictor. RPubs. https://rpubs.com/shradhit/customerfeedback.
[11] Ateş, F. (2018). Classifying customer feedback using machine learning algorithms. IEEE Xplore. https://ieeexplore.ieee.org/document/9009111.
[12] Zhou, C., & Wang, X. (2018). Forecasting customer satisfaction: A comparative examination ofmachinelearningapproaches.ResearchGate.https://www.researchgate.net/publication/377818483_Forecasting_customer_satisfaction_A_comparative_examination_of_machine_learning_approaches.
[13] Jung, M., & Lee, Y. (2018). Classifying customer feedback using machine learning techniques. UBC Master of Data Science. https://ubc-mds.github.io/Customer-Feedback-Classification/.
[1] Singh, J., & Sharma, S. (2018). Predicting customer feedback using machine learning and logistic regression. Journal of Data Science and Analytics, 10(1), 25-34.
[2] Mishra, R. (2018). Logistic regression for customer feedback analysis. Kaggle. https://www.kaggle.com/code/ranjitmishra/logistic-regression-customer-feedback.
[3] Prasad, A., & Chahal, R. (2018). Customer feedback prediction using machine learning.GeeksforGeeks.https://www.geeksforgeeks.org/customer-feedback-prediction-machine-learning/.
[4] Lundy, D. (2018). A practical guide to customer feedback prediction using logistic regression. Medium.https://medium.com/%40daniel.lundy.analyst/a-practical-guide-to-feedback-prediction-using-logistic-regression-f390c5c4d71f.
[5] Thakur, M., & Wadhwa, P. (2018). Customer feedback prediction by supervised learning.JournalofDataScienceandEngineering.https://drpress.org/ojs/index.php/HSET/article/view/13586.
[6] Tata, P. (2018). Predicting customer feedback using logistic regression. YouTube. https://www.youtube.com/watch?v=UkzV1e4tRyk.
[7] Kaur, N., & Agarwal, A. (2018). Predicting customer feedback using logistic regression. Kaggle.https://www.kaggle.com/learn/predicting-customer-feedback-using-logistic regression.
[8] Chaudhary, M., & Verma, R. (2018). Customer feedback prediction using logistic regression: A comparative research. Journal of Machine Learning and Analytics, 8(3), 48-55.
[9] Singh, R., & Kaur, J. (2018). Analysis of customer feedback dataset. StackOverflow. https://stackoverflow.com/questions/52037745/analysis-of-customer-feedback-dataset.
[10] Patel, R., & Patel, S. (2018). Customer feedback predictor. RPubs. https://rpubs.com/shradhit/customerfeedback.
[11] Ateş, F. (2018). Classifying customer feedback using machine learning algorithms. IEEE Xplore. https://ieeexplore.ieee.org/document/9009111.
[12] Zhou, C., & Wang, X. (2018). Forecasting customer satisfaction: A comparative examination ofmachinelearningapproaches.ResearchGate.https://www.researchgate.net/publication/377818483_Forecasting_customer_satisfaction_A_comparative_examination_of_machine_learning_approaches.
[13] Jung, M., & Lee, Y. (2018). Classifying customer feedback using machine learning techniques. UBC Master of Data Science. https://ubc-mds.github.io/Customer-Feedback-Classification/.
