Karya
Judul/Title Customer Shopping Behavior Analysis Using RFID and Machine Learning Models
Penulis/Author Dr.Eng. Ir. Ganjar Alfian, S.T., M.Eng. (1); MUHAMMAD QOIS H O (2); FARHAN MUFTI HILMY (3); Rachma Aurya Nurhaliza (4); Ir. Yuris Mulya Saputra, S.T., M.Sc., Ph.D., IPM., SMIEEE. (5); Divi Galih Prasetyo Putri, S.Kom., M.Kom. (6); Firma Syahrian, S.Kom., M.Cs. (7); Norma Latif Fitriyani (8); Fransiskus Tatas Dwi Atmaji (9); Umar Farooq (10); Dat Tien Nguyen (11); Muhammad Syafrudin (12)
Tanggal/Date 8 2023
Kata Kunci/Keyword
Abstrak/Abstract Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer–customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID technology on store shelves and machine learning models to analyze customer browsing activity in retail stores. The study uses RFID tags to track product movement and collects data on customer behavior using receive signal strength (RSS) of the tags. The time-domain features were then extracted from RSS data and machine learning models were utilized to classify different customer shopping activities. We proposed integration of iForest Outlier Detection, ADASYN data balancing and Multilayer Perceptron (MLP). The results indicate that the proposed model performed better than other supervised learning models, with improvements of up to 97.778% in accuracy, 98.008% in precision, 98.333% in specificity, 98.333% in recall, and 97.750% in the f1-score. Finally, we showcased the integration of this trained model into a web-based application. This result can assist managers in understanding customer preferences and aid in product placement, promotions, and customer recommendations.
Rumpun Ilmu Teknik Informatika
Bahasa Asli/Original Language English
Level Internasional
Status
Dokumen Karya
No Judul Tipe Dokumen Aksi
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