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All Journal Jurnal Manajemen, Bisnis dan Kewirausahaan Software Engineering in Computing Systems
Mujito Mujito
Institut Teknologi dan Bisnis Dewantara
Decision Sciences, Operations Research & Management

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Peran Sistem Informasi dan Pemasaran Digital terhadap Keputusan Pembelian pada Marketplace Tokopedia Dedy Tri Cahyono; Jaja Miharja; Mujito Mujito; Andri Catur Trissetianto
Jurnal Manajemen, Bisnis dan Kewirausahaan Vol. 5 No. 3 (2025): Desember : Jurnal Manajemen, Bisnis dan Kewirausahaan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jumbiku.v5i3.6109

Abstract

This study aims to analyze the influence of information systems and digital marketing on Tokopedia's consumer purchasing decisions. The background of this research is based on the increasing use of e-commerce platforms in Indonesia, especially Tokopedia, which demands the optimization of information systems and digital marketing strategies to drive purchase decisions. The method used is a quantitative approach with an accidental sampling technique of 100 Tokopedia user respondents. Data collection was carried out through an online questionnaire, and data analysis used multiple linear regression to test the partial and simultaneous influence of the two independent variables on the dependent variables. The results of the study show that both information systems and digital marketing have a positive and significant influence on purchasing decisions, both partially and simultaneously. Information systems are proven to be the dominant variable with the highest regression coefficient values, showing their important role in shaping user perception and comfort when transacting. Simultaneously, both variables contributed 83.8% to the purchase decision, while the rest were influenced by other factors outside the scope of the study. These findings affirm the importance of strengthening information systems and digital marketing strategies in increasing the effectiveness of e-commerce platforms and encouraging consumer purchasing behavior.  
A Comparative Study of Software Testing Techniques and Quality Metrics for Predicting Failure Rates in Scalable Cloud Native Software Systems Winny Purbaratri; Mujito Mujito; Sayyid Jamal Al Din
Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i1.50

Abstract

Cloud-native systems are essential for modern software development, offering enhanced scalability, flexibility, and resilience through cloud computing environments. However, ensuring the reliability and performance of these systems presents a challenge due to their dynamic and distributed nature. Traditional testing methods, such as unit and integration testing, while valuable for detecting individual component defects and interactions, are insufficient for predicting failure rates in complex, cloud-native applications. This study explores the effectiveness of various testing techniques and quality metrics in predicting failure rates within scalable cloud-native systems. A comparative experimental study was conducted using three primary testing techniques: unit testing, integration testing, and chaos testing. The results indicate that chaos testing, when combined with advanced quality metrics such as migration rate and mismigration rate, significantly outperforms traditional methods in predicting failure rates and evaluating system resilience. These findings suggest that chaos testing offers a more comprehensive evaluation, simulating real-world disruptions to test system behavior under stress, which is essential for cloud-native environments where high availability and fault tolerance are critical. The study also highlights the importance of integrating predictive quality metrics, which improve the accuracy of failure predictions and enhance system reliability. The study concludes that for cloud-native systems, a combination of advanced testing techniques and predictive metrics is essential for ensuring high availability, scalability, and reliability in dynamic environments. Future research should focus on refining predictive testing approaches, developing standardized frameworks, and empirically validating new testing methods to address the growing complexity of cloud-native systems.
Co-Authors Andri Catur Trissetianto Dedy Tri Cahyono Jaja Miharja Purbaratri, Winny Sayyid Jamal Al Din