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All Journal Jurnal Informasi dan Teknologi Escalate : Economics and Business Journal Al Urwah : Sharia Economics Journal
Setia Putra, Hari
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Religion Computer Science & IT Economics, Econometrics & Finance

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Implementation of Gradient Boosted Tree, Support Vector Machinery and Random Forest Algorithm to Detecting Financial Fraud in Credit Card Transactions Salomo Leuwol, Ferdinand; Ady Bakri, Asri; N. Bailusy, Muhsin; Setia Putra, Hari; Sukanti, Ni Ketut
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v5i3.386

Abstract

According to Google Trends data, machine learning-based credit card identification has grown over the last five years, at the very least, across all nations. In order to detect credit card fraud in this study, the authors will use machine learning methods such random forests, support vector machines, and gradient-boosted trees. The authors used the Synthetic Minority Oversampling Technique (SMOTE) and Random Under Sampling (RUS) sampling methods in each algorithm to compare because there was a class imbalance in this investigation. The research findings demonstrate that the author's algorithm and sample technique were successfully used, as shown by the AUC values obtained for each being > 0.7. The top score in RUS was 0.7835 using the Random Forest algorithm, whereas the greatest score in SMOTE was 0.73 with the Gradient Boosted Trees approach. The Random Forest algorithm and the Random Under Sampling (RUS) technique are developed as a result of this research, and they are useful for identifying fraudulent credit card transactions.
Ensuring Legal Protection for Consumers of Halal-Certified Minang Cuisine in Padang City Mardhika Adif, Riandy; Jefriyanto, Jefriyanto; Setia Putra, Hari
Al Urwah : Sharia Economics Journal Vol. 2 No. 01: Innovating Islamic Finance: Service Excellence, Digital Transformation, and Ethical E
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/alurwah.v2i01.46

Abstract

This study aims to understand legal protection for consumers in Padang City to establish a model for consumer protection practices in Indonesia. The research employs a normative descriptive qualitative method, focusing on business operators and consumers of traditional Minang products, with data collected through interviews. The study reveals that legal protection for consumers concerning halal labels on Minang food is stipulated in Law No. 08 of 1999. Several reasons contribute to producers not including halal labels: high processing costs, lack of awareness about the importance of halal certification, and consumers' inherent trust in authentic Minang producers, even if the shop lacks halal certification. The Indonesian Ulema Council (MUI) asserts that Muslim-owned businesses should pursue halal certification regardless of the owner's religion.
Exploring Business Potential Through the Application of Artificial Intelligence: Impact Analysis on Operational Efficiency, Decision Making, and Customer Experience Setia Putra, Hari; Badri, Juarsa; Irfani, Hadi
Escalate : Economics and Business Journal Vol. 1 No. 02: Driving Change and Innovation in the Digital Age
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/escalate.v1i02.24

Abstract

This article investigates the transformative impact of Artificial Intelligence (AI) on business dynamics through multiple linear regression analysis. Using data from PT. Samudera Indonesia Padang Branch, this research evaluates the influence of operational efficiency with the application of AI (X1), decision making supported by AI technology (X2), and efforts to improve customer experience through AI innovation (X3) on the Evaluation of the Impact of AI-Based Marketing Strategy variables (dependent) . The results show a robust model, with an R-square value of 0.783, indicating that approximately 78.3% of the variability in the dependent variable is explained by the AI-driven predictor variables. Each variable demonstrated a positive and substantial impact, emphasizing the importance of AI in improving operational processes, decision making and customer interactions. The high F-square value of 47.342 confirms the statistical significance of the entire model. This study contributes to understanding how AI adoption in business drives innovation and success, providing valuable insights for organizations looking to harness the potential of AI in a rapidly evolving business landscape.
Co-Authors Ady Bakri, Asri Badri, Juarsa Ferdinand Salomo Leuwol Irfani, Hadi Jefriyanto, Jefriyanto Mardhika Adif, Riandy Muhsin N Bailusy Ni Ketut Sukanti