Comparative Analysis of Naïve Bayes Variants for Best-Seller Status Determination in the Air Conditioning Industry


Sasa Ani Arnomo(1*); Zada Alzena(2); Heri Nuryanto(3); Siti Fairuz Nurr Sadikan(4);

(1) Institut Teknologi Batam
(2) Institut Teknologi Batam
(3) Politeknik Pariwisata Batam
(4) Universiti Teknologi MARA
(*) Corresponding Author

  

Abstract


The determination of product status such as the best seller predicate for Air Conditioning products is a fundamental sales promotion strategy. Leveraging machine learning to analyze sales data is essential for maximizing business progress and market positioning within the modern electronics industry. This study aims to evaluate and compare the performance and accuracy of Naïve Bayes (NB) classification models in determining AC product status. The goal is to identify the most effective variant among BernoulliNB, GaussianNB, and MultinomialNB for this specific application. A quantitative comparative analysis was conducted using various data record sizes. The primary features analyzed included brand, inverter type, electrical power cooling capacity, address, price, and sales status. In the first scenario, testing across varying data record sizes revealed that the MultinomialNB variant achieved the highest average accuracy at 85.39 percent. In the second scenario, using the full dataset with an 80% training data split without cross-validation, the Naïve Bayes algorithm as a whole demonstrated robust classification ability, reaching a peak prediction accuracy of 99.312 percent. This peak performance significantly outperformed alternative methods such as SVM and KNeighbors Classifier within those specified parameters. This performance significantly outperformed alternative methods such as SVM and KNeighbors Classifier within the specified parameters. The study concludes that the Naïve Bayes model is highly effective for product status classification in the electronics industry. The MultinomialNB variant is identified as the most consistently reliable model for these specific datasets. Future research should consider incorporating cross-validation techniques to further validate model stability across more diverse data environments.


Keywords


Naïve Bayes Classifiers; Comparative Analysis; Air Conditioning Determination; Probability Distribution; Customer Categorization.

  
  

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doi  https://doi.org/10.33096/ilkom.v18i2.3509.393-403 		  

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Z. Jin, Y. Zheng, and Y. Zhang, “A novel method for building air conditioning energy saving potential pre-estimation based on thermodynamic perfection index for space cooling,” J. Asian Archit. Build. Eng., vol. 22, no. 4, pp. 2348–2364, 2023, doi: 10.1080/13467581.2022.2109645.

A. K. Shofa and W. Yustanti, “Implementation of Xception Algorithm with Convolutional Block Attention Module ( CBAM ) for Waste Type Detection in Visual Images,” J. Emerg. Inf. Syst. Bus. Intelligen, vol. 7, no. 1, pp. 82–90, 2026.

X. Zhang, C. Zhou, Q. Sun, Y. Liu, Y. Yang, and Z. Zhuang, “Prediction of Solid Soluble Content of Green Plum Based on Improved CatBoost,” Agric., vol. 13, no. 6, 2023, doi: 10.3390/agriculture13061122.

S. A. Arnomo and N. B. Ibrahim, “MT-Forward Algorithm for Prediction of Project Categories Based on Selection of Mutants,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 5, pp. 1938–1943, 2022, doi: 10.18517/ijaseit.12.5.14296.

Adriansyah, Desmarita Leni, and R. Sumiati, “Comparative analysis of energy-efficient air conditioner based on brand,” Polimesin, vol. 20, no. 2, pp. 121–127, 2023.

K. Devi, A. A. Juliet, and M. Rafic, “A contemporaneous sample on the consumer brand preferences for air conditioners in west Chennai,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 11 Special Issue, pp. 698–702, 2019, doi: 10.35940/ijitee.K1119.09811S19.

X. Kong, B. Sun, B. Sun, J. Zhang, S. Li, and Q. Yang, “Power Retailer Air-Conditioning Load Aggregation Operation Control Method and Demand Response,” IEEE Access, vol. 8, pp. 112041–112056, 2020, doi: 10.1109/ACCESS.2020.3003278.

L. Kang, G. Wang, Y. Wang, and Q. An, “The Power Simulation of Water-Cooled Central Air-Conditioning System Based on Demand Response,” IEEE Access, vol. 8, pp. 67396–67407, 2020, doi: 10.1109/ACCESS.2020.2986309.

T. Jiang, P. Ju, C. Wang, H. Li, and J. Liu, “Coordinated Control of Air-Conditioning Loads for System Frequency Regulation,” IEEE Trans. Smart Grid, vol. 12, no. 1, pp. 548–560, 2021, doi: 10.1109/TSG.2020.3022010.

S. Maurya, V. Singh, N. K. Verma, and C. K. Mechefske, “Condition-Based Monitoring in Variable Machine Running Conditions Using Low-Level Knowledge Transfer with DNN,” IEEE Trans. Autom. Sci. Eng., vol. 18, no. 4, pp. 1983–1997, 2021, doi: 10.1109/TASE.2020.3028151.

K. Irshad, A. I. Khan, S. A. Irfan, M. M. Alam, A. Almalawi, and M. H. Zahir, “Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted with a Thermoelectric Air-Conditioning System,” IEEE Access, vol. 8, pp. 99709–99728, 2020, doi: 10.1109/ACCESS.2020.2985036.

Q. Wei, T. Li, and D. Liu, “Learning Control for Air Conditioning Systems via Human Expressions,” IEEE Trans. Ind. Electron., vol. 68, no. 8, pp. 7662–7671, 2021, doi: 10.1109/TIE.2020.3001849.

Y. Chen, G. Fu, and X. Liu, “Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning,” IEEE Access, vol. 8, pp. 123673–123682, 2020, doi: 10.1109/ACCESS.2020.2994119.

A. Muis, Z. Razilu, U. R. Senga, H. Wulandari, and R. Andriyani, “Comparative Analysis of Naïve Bayes Variants for Predicting Stunting-Risk Families,” J. Inform., vol. 13, no. 1, pp. 70–78, 2026.

M. Alenazi and S. Mishra, “Cyberatttack Detection and Classification in IIoT systems using XGBoost and Gaussian Naïve Bayes: A Comparative Study,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 4, pp. 15074–15082, 2024, doi: 10.48084/etasr.7664.

W. Gunawan, R. A. Wiradiputra, A. P. Sari, D. Prayama, and E. R. Nainggolan, “Prediction of Cross-Platform and Native Apps Technology Opportunities for Beginner Developers Using C 4.5 and Naïve Bayes Algorithms,” Int. J. Informatics Vis., vol. 7, no. 4, pp. 2145–2153, 2023, doi: 10.30630/joiv.7.4.1514.

A. Lia, A. Hananto, B. Huda, A. Yuniar, and E. Novalia, “Determination of Training Participants in Community Work Training Centers Using the Naïve Bayes Classifier Algorithm,” vol. 8, no. September, pp. 1162–1167, 2024.

K. A. Ogudo and D. M. J. Nestor, “Modeling of an Efficient Low Cost, Tree Based Data Service Quality Management for Mobile Operators Using in-Memory Big Data Processing and Business Intelligence use Cases,” 2018 Int. Conf. Adv. Big Data, Comput. Data Commun. Syst. icABCD 2018, pp. 1–8, 2018, doi: 10.1109/ICABCD.2018.8465410.

M. Saleem, M. Azam, Z. Mubeen, and G. Mumtaz, “Machine Learning for Improved Threat Detection: LightGBM vs. CatBoost,” J. Comput. Biomed. Informatics, vol. 7, no. 1, pp. 571–580, 2024, [Online]. Available: https://doi.org/10.56979/701/2024.

M. Mannan and S. G. Al-Ghamdi, “Indoor air quality in buildings: A comprehensive review on the factors influencing air pollution in residential and commercial structure,” Int. J. Environ. Res. Public Health, vol. 18, no. 6, pp. 1–24, 2021, doi: 10.3390/ijerph18063276.

F. R. Amalia, “Green Purchase Behavior Review Of The Role Of Price, Green Product, And Lifestyle,” J. Mark. Bus. Res., vol. 2, no. 1, pp. 11–22, 2022, doi: 10.56348/mark.v1i2.35.

M. Aibin, “The Weather Impact on Heating and Air Conditioning with Smart Thermostats,” Can. J. Electr. Comput. Eng., vol. 43, no. 3, pp. 190–194, 2020, doi: 10.1109/CJECE.2020.2978459.

A. Wijaya, H. A. Tan, R. M. Tanoro, B. Suwarno, and S. Noor, “Air Conditioner Customer Retention Based on Satisfaction and Loyalty with A Marketing Mix Analysis,” Int. J. Sci. Environ. Air, vol. 6, no. 1, pp. 821–834, 2026.

Tri Wahjoedi, Wulandari Harjanti, and Sri Rahayu, “Product quality and price perception on customer loyalty mediated by customer satisfaction,” World J. Adv. Res. Rev., vol. 14, no. 2, pp. 14–2, 2022, doi: 10.30574/wjarr.2022.14.2.0486.

T. Khonglumtan and P. Srisattayakul, “Analysis of Perceived Determinants in Customer Service Quality: Focused on Automotive Air Conditioning Service Centers,” Glob. Bus. Financ. Rev., vol. 28, no. 3, pp. 102–116, 2023, doi: 10.17549/gbfr.2023.28.3.102.

S. C. Putri, D. Lusianti, and D. Wismar’ein, “Enhancing Customer Satisfaction: The Impact of Service Quality and Store Atmosphere on Shopping Convenience,” J. Appl. Bus. Technol., vol. 6, no. 1, pp. 1–10, 2025, doi: 10.35145/jabt.v5i3.164.

F. Yunita and E. Puspitaloka Mahadewi, “The Influence of Promotion, Service Quality, Brand Image On Indomaret Customer Satisfaction and Loyalty,” Int. J. Sci. Technol. Manag., vol. 6, no. 3, pp. 491–496, 2025, doi: 10.46729/ijstm.v6i3.1299.

B. Ankrah, L. Brew, and J. Acquah, “Multi-Class Classification of Genetic Mutation Using Machine Learning Models,” Comput. J. Math. Stat. Sci., vol. 3, no. 2, pp. 280–315, 2024, doi: 10.21608/cjmss.2024.267064.1040.

M. Ismail, N. Hassan, and S. S. Bafjaish, “Comparative Analysis of Naive Bayesian Techniques in Health-Related for Classification Task,” J. Soft Comput. Data Min., vol. 1, no. 2, pp. 1–10, 2020, doi: 10.30880/jscdm.2020.01.02.001.

B. Spoorthi, S. S. Kumar, A. P. Rodrigues, R. Fernandes, and N. Balaji, “Comparative Analysis of Bank Loan Defaulter Prediction Using Machine Learning Techniques,” 2021 IEEE Int. Conf. Distrib. Comput. VLSI, Electr. Circuits Robot. Discov. 2021 - Proc., no. November, pp. 24–29, 2021, doi: 10.1109/DISCOVER52564.2021.9663662.

A. S. Dawood, “Performance Evaluation of Machine Learning Nave Bayes Algorithms for Network Traffic Classification,” Tech. Rom. J. Appl. Sci. Technol., vol. 13, pp. 12–26, 2023, doi: 10.47577/technium.v13i.9473.

C. Brooks, V. Kovanović, and Q. Nguyen, “Predictive modeling of student success Using Machine Learning,” Handb. Artif. Intell. Educ., vol. 17, no. 1, pp. 350–369, 2023, doi: 10.4337/9781800375413.00027.


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