Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms
DOI:
https://doi.org/10.57152/predatecs.v2i1.1099Keywords:
Artificial Neural Network, Classification, K-Nearest Neighbor, Naive Bayes, Offline ShoppingAbstract
Advancements in technology have made online shopping popular among many. However, the use of offline marketing models is still considered a profitable and important way of business development. This can be seen in the 2022 Association of Retail Entrepreneurs of Indonesia (APRINDO), which states that 60% of Indonesians shop offline, and in 2023, more than 75% of continental European consumers will prefer to shop offline. This is because many benefits can be achieved through offline marketing that cannot be obtained from online marketing. Therefore, classification of patterns and trends is performed to compare the results of the algorithms under study. Furthermore, this research was conducted to help offline retailers understand consumption patterns and trends that affect purchases. The algorithms analyzed in this study are K-Nearest Neighbor (K-NN), Naive Bayes, and Artificial Neural Network (ANN). As a result, the ANN algorithm obtained the highest confusion matrix results with an Accuracy value of 96.38%, Precision of 100.00%, and Recall of 100.00%. Meanwhile, when the Naive Bayes algorithm was used, the lowest Accuracy value was 57.39%, the Precision value was 57.86%, and when the K-NN algorithm was used, the Recall value was as low as 92.00%. These results indicate that the ANN algorithm is better at classifying offline shopping image data than the K-NN and Naive Bayes algorithms
References
J. Ram and S. Sun, “Business benefits of online-to-offline ecommerce: A theory driven perspective,” J. Innov. Econ. Manag., vol. 33, no. 3, pp. 135–162, 2020, doi: 10.3917/jie.033.0135.
F. Rahadi, “Aprindo Sebut 60 Persen Masyarakat Kembali Belanja Luring, Rambla Diluncurkan,” REPUBLIKA.CO.ID, 2023. https://rejogja.republika.co.id/berita/rvjunc291/aprindo-sebut-60-persen-masyarakat-kembali-belanja-luring-rambla-diluncurkan (accessed Dec. 04, 2023).
W. All, “European: consumers shopping online vs offline Weekly 2023,” intellesvizz.net, 2023. https://intellesvizz.net/?_=%2Fstatistics%2F1257230%2Feuropean-consumers-that-shop-online-and-offline-each-week%2F%23KJWqMdlUlBn8PPpbQwnhk4LmbIAuGFCs (accessed Dec. 04, 2023).
D. Tighe, “Percentage of consumers that shop online and offline on a weekly basis in the U.S., UK, and Australia in 2023,” statista.com, 2023. https://www.statista.com/statistics/1257243/consumers-that-shop-online-and-offline-each-week/ (accessed Dec. 04, 2023).
V. Khangembam, “Consumers choice of small independent specialty stores in shopping centers during weekday extended trading hours: A qualitative study,” Cogent Bus. Manag., vol. 10, no. 1, 2023, doi: 10.1080/23311975.2023.2185071.
Y. Chen, C. Q. L. Xue, and C. Sun, “American shopping malls in China: a mosaic analysis of databases,” J. Asian Archit. Build. Eng., vol. 22, no. 6, pp. 3224–3243, 2023, doi: 10.1080/13467581.2023.2182639.
H. Sachdev and M. H. Sauber, “Employee–customer identification: Effect on Chinese online shopping experience, trust, and loyalty,” Cogent Bus. Manag., vol. 10, no. 3, 2023, doi: 10.1080/23311975.2023.2275369.
B. N. Swar and R. Panda, “Online Retail Service Quality: Scale Development and Validation,” Vis. J. Bus. Perspect., vol. 27, no. 3, pp. 376–385, Jun. 2023, doi: 10.1177/09722629211011282.
M. D. Vo and S. Van Nguyen, “Enhancing store brand equity through relationship quality in the retailing industry: evidence from Vietnam,” Cogent Bus. Manag., vol. 9, no. 1, 2022, doi: 10.1080/23311975.2022.2149150.
N. Suarna, Y. A. Wijaya, Mulyawan, T. Hartati, and T. Suprapti, “Comparison K-Medoids Algorithm and K-Means Algorithm for Clustering Fish Cooking Menu from Fish Dataset,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1088, no. 1, p. 012034, 2021, doi: 10.1088/1757-899x/1088/1/012034.
R. B. W. A. Z. Leon, V. A. I. Huvenne, N. M. A. Benoist, M. Ferguson, B. J. Bett, “Assessing the repeatability of automated seafloor classification algorithms, with application in marine protected area monitoring,” Remote Sens, vol. 12, no. 10, 2020, doi: 10.3390/rs12101572.
F. Paquin, J. Rivnay, A. Salleo, N. Stingelin, and C. Silva, “Multi-phase semicrystalline microstructures drive exciton dissociation in neat plastic semiconductors,” J. Mater. Chem. C, vol. 3, pp. 10715–10722, 2015, doi: 10.1039/b000000x.
G. Alfian et al., “Customer Shopping Behavior Analysis Using RFID and Machine Learning Models,” Inf., vol. 14, no. 10, 2023, doi: 10.3390/info14100551.
J. Soni, U. Ansari, D. Sharma, and S. Soni, “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction,” Int. J. Comput. Appl., vol. 17, no. 8, pp. 43–48, 2011, doi: 10.5120/2237-2860.
M. Dasoomi, A. Naderan, and T. Allahviranloo, “Predict the Shopping Trip (Online and Offline) Using a Combination of a Gray Wolf Optimization Algorithm (GWO) and a Deep Convolutional Neural Network: A Case Study of Tehran, Iran,” 2023, doi: 10.20944/preprints202309.0696.v1.
A. M. Collins and G. Maglaras, “Smart shopper feelings in the case of store brands: the role of human capital as a key antecedent and the implications for store loyalty,” Int. Rev. Retail. Distrib. Consum. Res., vol. 00, no. 00, pp. 1–21, 2023, doi: 10.1080/09593969.2023.2200965.
A. A. Salih and A. M. Abdulazeez, “Evaluation of Classification Algorithms for Intrusion Detection System: A Review,” J. Soft Comput. Data Min., vol. 02, no. 01, pp. 31–40, 2021, doi: 10.30880/jscdm.2021.02.01.004.
D. Sebastian, “Implementasi Algoritma K-Nearest Neighbor untuk Melakukan Klasifikasi Produk dari beberapa E-marketplace,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 1, pp. 51–61, 2019, doi: 10.28932/jutisi.v5i1.1581.
W. T. Wu et al., “Data mining in clinical big data: the frequently used databases, steps, and methodological models,” Mil. Med. Res., vol. 8, no. 1, pp. 1–12, 2021, doi: 10.1186/s40779-021-00338-z.
P. F. Pratama, D. Rahmadani, R. S. Nahampun, D. Harmutika, A. Rahmadeyan, and M. F. Evizal, “Random Forest Optimization Using Particle Swarm Optimization for Diabetes Classification,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 1, pp. 41–46, 2023, doi: 10.57152/predatecs.v1i1.809.
M. Müller, L. Longard, and J. Metternich, “Comparison of preprocessing approaches for text data in digital shop floor management systems,” Procedia CIRP, vol. 107, no. May, pp. 179–184, 2022, doi: 10.1016/j.procir.2022.04.030.
B. Çi??ar and D. Ünal, “Comparison of Data Mining Classification Algorithms Determining the Default Risk,” Sci. Program., vol. 2019, 2019, doi: 10.1155/2019/8706505.
W. Putri, D. Hastari, K. U. Faizah, S. Rohimah, and D. Safira, “Implementation of Naïve Bayes Classifier for Classifying Alzheimer’s Disease Using the K-Means Clustering Data Sharing Technique,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 1, pp. 47–54, 2023, doi: 10.57152/predatecs.v1i1.803.
A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, p. 115, 2020, doi: 10.33365/jti.v14i2.679.
H. Chen, S. Hu, R. Hua, and X. Zhao, “Improved naive Bayes classification algorithm for traffic risk management,” EURASIP J. Adv. Signal Process., vol. 2021, no. 1, 2021, doi: 10.1186/s13634-021-00742-6.
Rayuwati, Husna Gemasih, and Irma Nizar, “IMPLEMENTASI AlGORITMA NAIVE BAYES UNTUK MEMPREDIKSI TINGKAT PENYEBARAN COVID,” Jural Ris. Rumpun Ilmu Tek., vol. 1, no. 1, pp. 38–46, 2022, doi: 10.55606/jurritek.v1i1.127.
R. Jader and S. Aminifar, “Rasool Jader/ Fast and Accurate Artificial Neural Network Model for Diabetes Recognition,” no. August, 2022, doi: 10.14704/nq.2022.20.10.NQ55189.
Y. F. Yumi Wu, QiWei Xiao, ShouDong Wang, Huanfang Xu, “Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques,” J. Inflamm. Res., pp. 5667–5676, 2024, doi: https://doi.org/10.2147/JIR.S438838.
