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All Journal Akasia: Artikel Ilmiah Sistem Informasi Akuntansi Profitabilitas Journal of Management and Informatics Journal of Technology Informatics and Engineering Jurnal Cendekia Ilmiah
Swastika, Rahayu
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Journal : Journal of Management and Informatics

 1 
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.
Co-Authors Akhromi, Ahmad Daniel Daniel Desy Tri Anggarini Diana Tambunan Eigis Yani Pramularso Handoko, Melyani Hartini, Dorit Haryanto, Wawan Hendra Kurniawan Hendra Lesmana Husni Mubarok Lidwan, Nanang Mawanti, Iis Melyani Melyani, Melyani Nurleni, Nurleni Reni Widyastuti Rizkiana Karmelia Shaura Sucipto, Rakhmat Hadi Velany, Mellamel Wahyu Indrarti