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All Journal Jurnal Sistim Informasi dan Teknologi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Kadarsih
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Computer Science & IT Decision Sciences, Operations Research & Management

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Comparative Analysis of Customer Acceptance of Digital Wallet Gopay, Dana and Ovo Using Unified Theory of Acceptance and Use of Technology Sudirjo, Frans; Ahmad Yani, David; Suroso, Amat; Kadarsih; Ar Rakhman Awaludin, Aulia
Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jsisfotek.v5i4.324

Abstract

Finding out what variables affect users' acceptance of server-based electronic wallets is the goal of this study. A quantitative methodology is used in this investigation. Using a questionnaire, a survey was conducted in order to collect data. Users of server-based electronic wallets make up the study's population. The purposive sampling strategy was employed by the author in this study. It was successful for the author to get 100 responses. Statistical analysis and demographic analysis are the two subcategories of data analysis. Using IBM SPSS, the author first analyzed demographic data. Second, SmartPLS was used by the author to perform statistical analysis. The following are the factors that affect user approval, according to the research findings: The impact of effort depletion on behavioral intention is substantial. An important factor influencing behavioral intention is social influence. The impact of perceived trust anticipation on behavioral intention is substantial. Behavior intention is significantly impacted by perceived risk anticipation. Encouraging circumstances behavioral intention is significantly influenced by expectations. Use behavior is significantly impacted by facilitating situations. Behavioral intention is significantly impacted by habit. The way we utilize things is greatly influenced by our habits. The intention behind one's behavior greatly influences how they use things. In the meanwhile, performance expectation has no discernible impact on behavioral intention, and the following criteria do not influence user approval. There is no discernible impact of hedonic motivation on behavioral intention. On behavioral intention, price value has no discernible impact.
Perbandingan Kinerja Isolation Forest Dan Local Outlier Factor (LOF) Dalam Deteksi Anomali Transaksi Digital Hartati, Sri; Pujianto, Defi; Kadarsih
BETRIK Vol. 17 No. 01 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/5w76ab74

Abstract

The rapid growth of digital transactions has increased the risk of anomalous activities such as fraud, particularly in highly imbalanced datasets where fraudulent transactions are significantly fewer than normal transactions. This imbalance presents a major challenge in anomaly detection, as models tend to be biased toward the majority class. This study aims to compare the performance of Isolation Forest and Local Outlier Factor (LOF) algorithms in detecting anomalies in digital transaction data.The research adopts an experimental approach using the Credit Card Fraud Detection dataset, which consists of 284,807 transactions, including 492 fraudulent cases. Data preprocessing involves feature normalization using StandardScaler, followed by a stratified train-test split with a ratio of 70:30. Model evaluation is conducted using confusion matrix, precision, recall, and F1-score metrics.The results show that Isolation Forest outperforms LOF. Isolation Forest successfully detects 37 out of 148 fraudulent transactions with a precision of 0.2824, recall of 0.25, and F1-score of 0.2652. In contrast, LOF detects only 2 fraudulent transactions, with a precision of 0.0137, recall of 0.0135, and F1-score of 0.0136. These findings indicate that isolation-based approaches are more effective and robust than density-based methods in handling highly imbalanced datasets.
Co-Authors Ahmad Yani, David Ar Rakhman Awaludin, Aulia Defi Pujianto Frans Sudirjo Sri Hartati Suroso, Amat