Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Artificial Intelligence Research Journal of Artificial Intelligence and Digital Business
Adiguna, Vinsent Brilian
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Improving Performance Sentiment Analysis Movie Review Film using Random Forest with Feature Selection Information Gain Adiguna, Vinsent Brilian; Aqqad, Muslihul; Purwanto, Purwanto; Jaluanto Sunu, Jaluanto Sunu; Honorata Ratnawati, Honorata Ratnawati
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1227

Abstract

Sentiment analysis in film reviews is an important task to understand the audience's opinion towards a cinematic work. However, the complexity and subjectivity of language in film reviews pose a challenge. This research explores the application of Random Forest algorithm, an ensemble learning method, to perform sentiment classification on film reviews. Random Forest is built from a set of decision trees, each of which provides a prediction, and the final result is obtained from majority voting. This approach has the advantage of handling overfitting data. This research uses 500 review datasets along with positive and negative sentiment labels. The review text is represented as Information Gain and TF-IDF features to model the weight of each word. The Random Forest model is then trained using these features to predict sentiment labels. The performance of the model is evaluated using metrics such as accuracy, precision, recall and f1-score. The experimental results show that Random Forest is able to achieve 95.20% accuracy in sentiment classification of film reviews, surpassing the Support Vector Machine classification algorithm which in previous studies only achieved 92%. These findings provide a new perspective on the benefits of ensemble learning in sentiment analysis and its potential application in other domains such as marketing and public opinion analysis.
Cybersecurity Vulnerabilities in Digital Business: Challenges, and Novel Directions for Resilience Adiguna, Vinsent Brilian; Inabah, Sekar Farahdila; Rachel, Rona
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.3200

Abstract

Digital transformation has profoundly reshaped business ecosystems by embedding advanced technologies into operations, customer engagement, and decision-making processes. However, this transformation simultaneously amplifies cybersecurity vulnerabilities that endanger organizational resilience, data integrity, and consumer trust. This study provides a comprehensive analysis of cybersecurity vulnerabilities in digital business environments based on an extensive review of open-access literature and real-world case studies. The findings indicate that 89% of companies face web application vulnerabilities, 93% experience hosting and configuration issues, and 46% of employees reuse breached passwords—revealing persistent weaknesses in digital infrastructures. These vulnerabilities result in financial losses, reputational damage, and regulatory non-compliance that may threaten long-term business continuity. Artificial Intelligence (AI) has emerged as both a catalyst for advanced cyberattacks and a strategic defense enabler. AI-driven threat intelligence, anomaly detection, and automated response mechanisms significantly enhance organizational capacity to predict, prevent, and mitigate cyber incidents. The case of SAP NetWeaver zero-day exploitation (CVE-2025-31324) demonstrates the urgency of adaptive defense frameworks, while the implementation of AI-based zero-trust architectures highlights the importance of continuous verification and risk-based access control. This research recommends adopting AI-augmented cybersecurity solutions, strengthening supply chain security, and fostering cyber-aware organizational cultures to ensure sustainable digital resilience. Additionally, it underscores the necessity of aligning cybersecurity with business strategies through cross-disciplinary collaboration that integrates technology, management, human behavior, and policy. By linking cyber resilience directly with organizational performance and strategic competitiveness, this study contributes a holistic framework for maintaining trust and stability in the rapidly evolving landscape of digital business.
K-Means Clustering Analysis For Identifying Product Purchase Patterns Based On Country On E-Commerce Platforms Pramudya, Ryan Arya; Adiguna, Vinsent Brilian
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3296

Abstract

E-commerce sites get a lot of transaction data from people in different countries who like different kinds of products. It is very important to know how people buy things based on their country and the type of product they buy in order to come up with better and more efficient marketing plans. This study seeks to discern product purchasing patterns by country through the application of the K-Means clustering algorithm on international e-commerce transaction data. This study utilized a dataset comprising 6,000 e-commerce transaction records, characterized by two primary variables: country and product category. Several methods were used in the preprocessing stage. For example, missing values were replaced to deal with missing data, nominal data was changed to numerical data to change categorical data into numerical data, and Z-transformation was used to normalize the data so that it was all on the same scale. We used the K-Means algorithm to group data into clusters with different k values, such as k=2, 5, 10, 15, 20, and 25. We then used the average within centroid distance metric and the elbow method to find the best number of clusters. The elbow method analysis showed that the best number of clusters was k=10, which showed a big drop in the average within centroid distance value. The ten clusters with algorithms K-Means that were made show very specific market segmentation, with each cluster having its own set of countries and product categories that are most popular.
Co-Authors Aqqad, Muslihul Honorata Ratnawati, Honorata Ratnawati Inabah, Sekar Farahdila Jaluanto Sunu, Jaluanto Sunu Purwanto, Purwanto Rachel, Rona Ryan Arya Pramudya, Ryan Arya