Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger
The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.
E. L. Widjaja, A. Aprilia and A. Harianto, "Analisa Pengaruh Kualitas Layanan Terhadap Kepuasan Penumpang Maskapai Penerbangan Batik Air," Jurnal Hospitality dan Manajemen Jasa, vol. 5, no. 2, pp. 118-132, 2017.
W. Ardhia, "Tingkat Kepuasan Penumpang Terhadap Layanan Maskapai Penerbangan PT. Lion Air Rute Menuju Jakarta," Jurnal Perhubungan Udara, vol. 41, no. 1, pp. 19-28, 2015.
M. D. Darus and K. Mahalli, "Analisis Tingkat Kepuasan Penumpang Terhadap Kualitas Pelayanan di Bandar Udara Internasional Kualanamu," Jurnal Ekonomi dan Keuangan, vol. 3, no. 6, pp. 408-420, 2015.
M. S. Garver, "Using Data Mining for Customer Satisfaction Research," Marketing Research, vol. 14, no. 1, pp. 8-17, 2002.
S. Moro, J. Esmerado, P. Ramos and B. Alturas, "Evaluating A Guest Satisfaction Model Through Data Mining," International Journal of Contemporary Hospitality Management, vol. 32, no. 4, pp. 1523-1538, 2019.
V. Gopalakrishnan and C. Ramaswamy, "Patient Opinion mining to Analyze Drugs Satisfaction Using Supervised Learning," Journal of Applied Research and Technology, vol. 15, no. 1, pp. 311-319, 2017.
Kaggle, "Kaggle.com," Mei 2020. [Online]. Available: https://www.kaggle.com/teejmahal20/airline-passenger-satisfaction. [Accessed 24 Maret 2021].
I. A. A. Amra and A. Y. A. Maghari, "Students Performance Prediction Using KNN and Naïve Bayesian," in The 8th International Conference on Information Technology (ICIT), Al-Zaytoonah University of Jordan, Jordan, 2017.
F. Osisanwo, J. Akinsola, O. Awodele, J. O. Hinmikaiye, O. Olakanmi and J. Akinjobi, "Supervised Machine Learning Algorithms: Classification and Comparison," International Journal of Computer Trends and Technology (IJCTT), vol. 48, no. 3, pp. 128-138, 2017.
E. N. Azizah, U. Pujianto, E. Nugraha and Darusalam, "Comparative Performance Between C4.5 and Naive Bayes Classifiers in Predicting Student Academic Performance in A Virtual Learning Environment," in The 4th International Conference on Education and Technology (ICET), Malang, Indonesia, 2018.
K. Madasamy and M. Ramaswami, "Data Imbalance and Classifiers: Impact and Solutions from A Big Data Perspective," International Journal of Computational Intelligence Research, vol. 13, no. 9, pp. 2267-2281, 2017.
E. M. Hassib, A. I. El-Desouky, E.-S. M. El-Kenawy and S. M. El-Ghamrawy, "An Imbalanced Big Data Mining Framework for Improving Optimization Algorithms Performance," Journal & Magazines, vol. 7, no. 1, pp. 170774-170795, 2019.
S. Chen, G. I. Webb, L. Liu and X. Ma, "A Novel Selective Naïve Bayes Algorithm," Knowledge-Based Systems, vol. 192, pp. 1-15, 2020.
L. Jiang, L. Zhang, L. Yu and D. Wang, "Class-Specific Attribute Weighted Naive Bayes," Pattern Recognition, vol. 88, no. 1, pp. 321-330, 2019.
S. Ernawati, R. Wati, N. Nuris, L. S. Marita and E. R. Yulia, "Comparison of Naïve Bayes Algorithm with Genetic Algorithm and Particle Swarm Optimization as Feature Selection for Sentiment Analysis Review of Digital Learning Application," Journal of Physics: Conference Series, vol. 1641, pp. 1-7, 2020.
X. Liu, Z. Liu, Z. Liang, S.-P. Zu, J. A. F. O. Correia and A. M. P. D. Jesus, "PSO-BP Neural Network-Based Strain Prediction of Wind Turbine Blades," Materials, vol. 12, no. 12, pp. 2-15, 2019.
S. Srivastava, J. Gupta and M. Gupta, "PSO & Neural-Network Based Signature Recognition for Harmonic Source Identification," in IEEE Region 10 International Conference TENCON, Singapore, 2009.
M. Misdram, E. Noersasongko, A. Syukur, Purwanto, M. Muljono, H. A. Santoso and D. R. I. M. Setiadi, "Analysis of Imputation Methods of Small and Unbalanced Datasets in Classifications using Naïve Bayes and Particle Swarm Optimization," in International Seminar on Application for Technology of Information and Communication (ISemantic), Semarang, Indonesia, 2020.
I. Romli, T. Pardamean, S. Butsianto, T. N. Wiyatno and E. B. Mohamad, "Naive Bayes Algorithm Implementation Based on Particle Swarm Optimization in Analyzing the Defect Product," Journal of Physics: Conference Series, vol. 1845, no. 1, pp. 1-6, 2021.
J. Li, L. Ding and B. Li, "A Novel Naive Bayes Classification Algorithm Based on Particle Swarm Optimization," The Open Automation and Control Systems Journal, vol. 6, no. 1, pp. 747-753, 2014.
Y. Religia, A. Nugroho and W. Hadikristanto, "Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing," Jurnal Rekayasa Sistem dan Teknologi Informasi, vol. 5, no. 1, pp. 187-192, 2021.
A. Arwan and D. S. Rusdianto, "Optimization of Genetic Algorithm Performance Using Naïve Bayes for Basis Path Generation," Kinetik, vol. 2, no. 4, pp. 273-282, 2017.
E. Stripling, S. v. Broucke, K. Antonio, B. Baesens and M. Snoecka, "Profit Maximizing Logistic Model for Customer Churn Prediction Using Genetic Algorithms," Swarm and Evolutionary Computation, vol. 40, no. 1, pp. 116-130, 2018.
D. K. Choubey, S. Paul, S. Kumar and S. Kumar, "Classification of Pima Indian Diabetes Dataset Using Naive Bayes With Genetic Algorithm As An Attribute Selection," in The International Conference on Communication and Computing Systems (ICCCS), Ranchi, India, 2016.
L. G. P. Suardani, I. M. A. Bhaskara and M. Sudarma, "Optimization of Feature Selection Using Genetic Algorithm with Naïve Bayes Classification for Home Improvement Recipients," International Journal of Engineering and Emerging Technology, vol. 3, no. 1, pp. 66-70, 2018.
T. Horvat, L. Havaš and D. Srpak, "The Impact of Selecting a Validation Method in Machine Learning on Predicting Basketball Game Outcomes," Symmetry, vol. 12, no. 3, pp. 1-15, 2020.
S. Ruuskaa, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen and J. Mononen, "Evaluation of The Confusion Matrix Method in The Validation of An Automated System for Measuring Feeding Behaviour of Cattle," Behavioural Processes, vol. 148, no. 1, pp. 56-62, 2018.
B. Juba and H. S. Le, "Precision-Recall versus Accuracy and the Role of Large Data Sets," in The 3th Conference on Artificial Intelligence (AAAI), Washington, United States of America, 2019.
I. Romli, E. Pusnawati and A. Siswandi, "Comparison of NB and NB-PSO to Determine Level of Vehicles Sales," Journal of Physics: Conference Series, vol. 1764, no. 1, pp. 1-6, 2021.
N. A. Maori, "Perbandingan Metode ANN-PSO dan ANN-GA untuk Peningkatan Akurasi Prediksi Harga Emas Antam," Jurnal Disprotek, vol. 10, no. 2, pp. 101-106, 2019.
B. Chopard and M. Tomassini, "Particle Swarm Optimization," in An Introduction to Metaheuristics for Optimization, Springer, Cham, Natural Computing Series, 2018, p. 97–102.
E. Habibi, M. Salehi, G. Yadegarfar and A. Taheri, "Optimization of ANFIS Using A Genetic Algorithm for Physical Work Rate Classification," International Journal of Occupational Safety and Ergonomics, vol. 26, no. 3, pp. 436-443, 2020.
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