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Journal : journal of management and informatics

Analysis of Price and  Product Perceived Bolster Purchase Decision: Study Case in Fast Food Restaurant in East Bekasi Area Rahadjeng , Indra Riyana; Handoko, Melyani; Indrarti, Wahyu; Shaura, Rizkiana Karmelia; Rafik, Ahamd; Daniel; Emita, Isyana; Anwar, Dian Mohamad
Journal of Management and Informatics Vol. 4 No. 2 (2025): August Season | JMI : Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i2.202

Abstract

The increasing competition among fast food restaurants in urban areas has made consumers more selective in their purchasing decisions, with price and product quality emerging as key determinants. This study aims to analyze the influence of perceived price and perceived product quality on consumer purchasing decisions at Subway fast food restaurants in East Bekasi. A quantitative approach was applied using a structured questionnaire distributed to 50 consumers. Data were analyzed through multiple linear regression and classical assumption testing. The results indicate that price has a significant and positive effect on purchasing decisions, with a regression coefficient of 1.191 and a partial determination coefficient (R²) of 0.750, suggesting that price alone explains 75% of the decision variance. Similarly, perceived product quality also demonstrates a significant positive effect with a coefficient of 0.469 and R² of 0.724. When combined, both variables explain 72.4% of the variance in purchasing decisions, with the F-test result of 91.673 surpassing the critical value of 3.20. These findings suggest that consumers in East Bekasi prioritize both affordability and quality when choosing fast food. This research contributes to the literature by validating the dual importance of economic and experiential value in fast food marketing strategies, especially in highly competitive suburban environments.
The Effect of Quality Product, Price Perception, and Promotion onVivo Smartphone Purchase Decisions: A Study at the Archa PhoneCounter in Bekasi Melyani, Melyani; Swastika, Rahayu; Widyastuti, Reni; Shaura, Rizkiana Karmelia; Pramularso, Eigis Yani; Anggarini, Desy Tri; Tambunan, Diana
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.203

Abstract

This study investigates the influence of product quality, price perception, and promotion on Vivosmartphone purchase decisions. A quantitative survey was administered to 100 consumers at the ArchaPhone Counter in Bekasi using purposive sampling. Multiple linear regression analysis revealed thatall three variables have a significant positive partial effect on the purchase decision. The modelexplained 85% of the variance (Adjusted R² = 0.726), confirming their simultaneous influence. Productquality was identified as the most dominant predictive factor, validating its critical role in consumerchoice. 
The Effect of Compensation on Employee Performance Through Work Motivation as an Intermediary Variable (Case Study at Archa Beauty Clinic Bekasi) Shaura, Rizkiana Karmelia; Handoko, Melyani; Widyastuti, Reni; Swastika, Rahayu; Tambunan, Diana; Anggarini, Desy Tri; Kurniawan, Hendra
Journal of Management and Informatics Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v5i1.335

Abstract

The rapid growth of digital payment systems has increased the complexity of financial transactions, making credit card fraud detection more challenging, particularly due to evolving fraud patterns and highly imbalanced datasets. Conventional machine learning approaches often struggle to capture temporal dependencies and adapt to new fraud behaviors, while centralized data processing raises privacy concerns. This study proposes a hybrid fraud detection framework that integrates Bidirectional Long Short-Term Memory (BiLSTM), Autoencoder, and Federated Learning to improve detection performance while preserving data confidentiality. The BiLSTM component models sequential transaction behavior from both forward and backward directions, while the autoencoder identifies anomalies based on reconstruction errors. Federated Learning enables collaborative model training across multiple institutions without sharing sensitive data. Experimental evaluation using benchmark datasets shows that the proposed model achieves high classification performance, with improved precision, recall, and overall stability compared to traditional and standalone deep learning models. The framework effectively handles class imbalance and detects both known and emerging fraud patterns. This study contributes a scalable and privacy-preserving solution for real-world fraud detection, supporting secure collaboration and enhancing model generalization in distributed financial environments.