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All Journal Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Agung Rizky
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Computer Science & IT Control & Systems Engineering Education Library & Information Science Other

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Decentralization Of Information Using Blockchain Technology On Mobile Apps E-Journal Agung Rizky; Lutfiani, Ninda; Sri Mariyati, Wayan; Atika Sari, Apriani; Febrianto, Kurnia Rizky
Blockchain Frontier Technology Vol. 1 No. 2 (2022): B-Front(Blockchain Frontier Technology)
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v1i2.37

Abstract

Decentralization is the division of centralized power processes as a whole into separate ones. Blockchain technology is a basic technology that enables decentralization and gives all users the opportunity to become part of the validator or node for future transactions. The purpose of this research is to develop and develop a tamper-resistant E-Journal Mobile Application system using Blockchain Technology, which supports reliable and auditable computing using a decentralized network, not only that, this research can also develop and develop an E-Journal Application System. Cellular that is resistant to interference from hackers or system crashes. By using Blockchain technology, it can solve problems that are reliable and can be done and controlled using a decentralized network. with great hope that choosing this type of decentralized blockchain technology will be useful in preventing errors and errors in the system. This is one of the main strengths of Blockchain Technology. With this decentralized transaction log, it will not be easy and it is impossible for a database consisting of hundreds to thousands of computers to be hacked or tampered with easily. This research is a model that can be implemented as a Blockchain technology development on Mobile Apps E-journals. In this study will be made using 3 methods, namely the data analysis method, the analysis method using SWOT and the development method using a waterfall with a combination of the three methods will make this research more effective and efficient.
Optimizing Blockchain Digital Signature Security in Driving Innovation and Sustainable Infrastructure Agung Rizky; Nugroho, Rayhan Wahyu; Wahyu Sejati; Mumpuni; Omar Sy
Blockchain Frontier Technology Vol. 4 No. 2 (2025): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v4i2.717

Abstract

Indonesia experienced a significant spike in cyberattacks, averaging 158 attacks per second, indicating a high risk for digital systems, especially in the government and business sectors. The large number of attacks has caused financial losses and damaged public trust, indicating that data protection is not optimal. Traditional authentication systems based on centralized servers increase the risk of document leaks and forgery. To address this, this study proposes a Blockchain-based digital signature solution called AlphaSign. This technology strengthens data resilience and document verification reliability with an Agile Scrum approach, enabling iterations that are responsive to user needs. Alpha Sign aims to create a secure Blockchain-based digital signature system, prevent document forgery, and accelerate verification through QR code automation. This system supports the development of sustainable digital infrastructure, in line with the SDGs, especially Goal 9 (Infrastructure Innovation) and Goal 16 (Resilient Institutions). With Blockchain technology, Alpha Sign records each signature in a decentralized manner, reduces the risk of hacking, and enables real-time document verification through QR codes, providing security and transparency for large agencies and the public sector.
Utilizing the AlphaSign Website to Create Blockchain-Based or Online Digital Signatures Pauline H. Pattyranie Tan; Agung Rizky; Qurotul Aini; Dwi Nur Ramadhan; Thomas Green
Blockchain Frontier Technology Vol. 5 No. 1 (2025): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i1.785

Abstract

Blockchain is a decentralized system that can track and verify document authenticity, but its open nature poses challenges when dealing with confidential documents or those protected by personal data protection laws. The advancement of digital technology has spurred innovations in signature systems, particularly through the use of the AlphaSign website to create blockchain-based digital signatures.This study aims to enhance the security, transparency, and efficiency of signature issuance using blockchain technology, in alignment with the Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 16 (Peace, Justice, and Strong Institutions).The research method involves the development of a web-based system integrated with blockchain to validate digital signatures. By incorporating automatic validation and signature features, AlphaSign addresses the issues of forgery and the loss of conventional signatures.The findings reveal that implementing blockchain in digital signature systems can improve security by up to 90% and expedite the verification process by up to 70% compared to traditional methods. Additionally, AlphaSign contributes to sustainable digital infrastructure by reducing paper usage and supporting e-administration systems. There fore, this research supports the promotion of safe and efficient digital transformation, aligning with sustainable development principles. The integration of blockchain technology in digital signatures offers a scalable, secure, and efficient solution that advances industry innovation and legal transparency, crucial for the achievement of SDGs.
A Framework for Mining Customer Data in Management Information Systems Untung Rahardja; Lutfiani, Ninda; Agung Rizky; Yul Ifda Tanjung; Evans, Richard
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/m5qymx32

Abstract

The exponential growth of customer data within Management Information Systems (MIS) has generated an urgent need for structured analytical approaches capable of transforming raw information into valuable insights that support decision-making across various organizational processes. This study aims to develop a comprehensive and systematic framework for mining customer data in MIS by integrating preprocessing procedures, machine learning algorithms, and model evaluation techniques into a unified analytical workflow. Using the Design Science Research methodology, the framework was designed based on existing data mining standards, developed through iterative refinement, and demonstrated using a customer-behavior dataset processed with clustering, classification, and association rule mining techniques. The findings reveal that the proposed framework improves data quality, enhances segmentation accuracy, and strengthens predictive capability, enabling MIS to deliver deeper insights into customer behavior, purchasing tendencies, and potential churn risks. Experimental results show that combining K-Means, Random Forest, and Apriori algorithms yields more comprehensive and reliable patterns compared to using a single analytical technique. The outcomes of this research highlight the practical significance of applying an integrated data mining approach in MIS, allowing organizations to optimize marketing strategies, personalize services, and make more informed managerial decisions. Overall, this study contributes to the field by offering a scalable, adaptable, and effective framework for implementing customer data mining within real-world MIS environments.
Self Supervised Transformers for High Dimensional Time Series Anomaly Detection Aswadi Jaya; Derlina; Qurotul Aini; Agung Rizky; Richard Evans
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i1.1078

Abstract

This study addresses anomaly detection in high dimensional time series data within the context of Artificial Intelligence (AI) driven software development, where modern systems generate large temporal data streams and reliable monitoring remains difficult due to noise, complexity, and limited labeled anomalies. The objective of this research is to develop an effective and scalable anomaly detection framework based on self supervised transformer models that can learn meaningful temporal representations without heavy reliance on manual annotation. The proposed method applies self supervised pretraining through masked sequence reconstruction and contrastive temporal learning on large scale, unlabeled multivariate time series datasets, followed by transformer based attention mechanisms to capture long range dependencies and compute anomaly scores. Experiments are conducted using benchmark datasets and real world system log data implemented with Python based deep learning tools and transformer architectures to evaluate detection performance. The results indicate that the proposed approach improves detection accuracy and reduces false positive rates compared to traditional statistical techniques and supervised deep learning models, particularly in high dimensional and low label settings. In conclusion, integrating self supervised learning with transformer architectures provides a robust and generalizable solution for time series anomaly detection, contributing to software analytics and monitoring systems by lowering labeling costs and improving adaptability across application domains.
Co-Authors Aswadi Jaya Atika Sari, Apriani Derlina Dwi Nur Ramadhan Evans, Richard Febrianto, Kurnia Rizky Mumpuni Ninda Lutfiani Nugroho, Rayhan Wahyu Omar Sy Qurotul Aini Richard Evans Sri Mariyati, Wayan Tan, Pauline H. Pattyranie Thomas Green Untung Rahardja Wahyu Sejati Yul Ifda Tanjung