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All Journal ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) Blockchain Frontier Technology (BFRONT) BEAM
Kgomotso Moyo
Mfinitee incorporation, South Africa
Humanities Computer Science & IT Control & Systems Engineering Education Library & Information Science Social Sciences

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Implements Midtrans Payment Gateway for Digital Payments on the Hadirku Application Mulyati Mulyati; Ahmad Herkal Taqyudin; Rini Septiowati; Kgomotso Moyo; Sularso Budilaksono
ADI Pengabdian Kepada Masyarakat Vol 6 No 1 (2025): ADI Pengabdian Kepada Masyarakat
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/adimas.v6i1.1342

Abstract

The development of financial technology has driven the need for efficient, secure, and integrated digital payment systems within educational environments. However, the payment process for academic activities in the Hadirku Application at University of Raharja was previously conducted manually, often causing delays in payment confirmation and potential data entry errors. Through this community service program, a team of lecturers and students collaborated with the partner the administrators and users of the Hadirku Application to implement an automated payment system using the Midtrans Payment Gateway. The activities were carried out through several stages, including partner needs analysis, system design, API integration of Midtrans, and user training for both administrators and end users. The implementation results demonstrated a significant improvement in transaction efficiency. The average payment confirmation time, which previously took about two hours, has now been reduced to less than one minute through an automated process. In addition, the partner’s understanding of digital payment systems increased notably, and all transaction activities can now be monitored in real time through the Midtrans dashboard. This community service activity not only succeeded in automating the payment process within the Hadirku Application but also contributed to enhancing the digital literacy of the academic community and supporting the realization of a sustainable smart campus implementation at University of Raharja.
Data Mining Techniques in Blockchain Using Machine Learning Algorithms Ankur Singh Bist; Aswadi Jaya; Agung Rizky; Maulana Arif Komara; Kgomotso Moyo
Blockchain Frontier Technology Vol. 6 No. 2 (2027): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i2.1169

Abstract

The rapid advancement of blockchain technology has generated an enormous volume of complex transaction data, creating significant challenges in data analysis, particularly in terms of scalability, noise, and anonymity. This study aims to identify effective data mining techniques and implement machine learning algorithms to enhance the performance of blockchain data analysis. A quantitative approach was employed by utilizing data mining techniques and machine learning algorithms, including Random Forest, K-Means, and Neural Network. These methods were applied to blockchain datasets obtained from Ethereum, Bitcoin, and OpenSea through several stages, namely preprocessing, feature engineering, model training, and evaluation using accuracy, precision, recall, F1-score, and Root Mean Square Error metrics. The results indicate that Random Forest demonstrates stable performance with high classification accuracy, Neural Network excel at capturing complex patterns in non-linear data, while K-Means is effective in identifying patterns through clustering. These findings suggest that each algorithm offers distinct advantages depending on the characteristics of the data and the objectives of the analysis. This study concludes that the integration of data mining techniques and machine learning algorithms can significantly improve the effectiveness of blockchain data analysis compared to traditional methods. Furthermore, the proposed integrated framework can serve as a reference for the future development of blockchain-based data analytics systems. Rather than providing a quantitative benchmark against conventional analytical approaches, this study proposes a standardized methodological framework intended to support consistent implementation, evaluation, and future comparative validation across heterogeneous blockchain analytics applications.
Real Time Audience Analytics Using Machine Learning to Measure Listener and Viewer Cultural Engagement Asep Sutarman; Felix Sutisna; Dimas Aditya Prabowo; Kgomotso Moyo
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid evolution of digital media has transformed audience interaction, yet traditional metrics like views and likes fail to capture the nuanced emotional and cultural dynamics of broadcast content. This study develops a real-time audience analytics framework using machine learning to measure deep cultural engagement and emotional resonance within digital media environments. Adopting a hybrid methodological approach, the research integrates Natural Language Processing (NLP) with qualitative interpretation. The system processes live interaction data, employing sentiment analysis and pattern recognition to categorize audience responses into complex emotional and cultural engagement tiers beyond simple polarity. Findings demonstrate that the machine learning model effectively identifies real-time shifts in audience sentiment, revealing how specific cultural cues trigger heightened engagement and collective emotional responses. This research advances audience analytics by bridging the gap between computational speed and qualitative depth, offering a scalable model for broadcasters and researchers to understand the cultural impact of digital content as it happens.
Co-Authors Agung Rizky Ahmad Herkal Taqyudin Ankur Singh Bist Asep Sutarman Aswadi Jaya Dimas Aditya Prabowo Felix Sutisna Maulana Arif Komara Mulyati Mulyati Rini Septiowati Sularso Budilaksono