Mobile and Forensics

Integration of Landscape Analysis, Mobile GIS and Ant Algorithm for Early Warning and Coastal Security Systems

Muhammad Aziz — 2025-06-01

Coastal areas are increasingly vulnerable to abrasion, flash floods, and seawater intrusion due to climate change and human activities. Addressing these hazards requires an integrated early warning system that enables timely and adaptive disaster mitigation. This study integrates landscape analysis, Mobile GIS technology, and the Ant algorithm into a unified early warning framework. Landscape analysis identifies geomorphological patterns and coastline changes. Mobile GIS supports real-time spatial data acquisition, while the Ant algorithm optimizes evacuation routes, sensor placement, and hazard prediction through agent-based modeling. The system incorporates spatial, topographic, oceanographic, and socio-economic data processed through spatial and computational methods. Validation using historical records and field data shows enhanced hazard detection, more efficient evacuation planning, and quicker response times. The Ant algorithm adapts routes in real time based on environmental changes. Sensor deployment is optimized for high-risk zones. Mobile GIS ensures continuous updates and spatial visualization. Real-time processing supports rapid decision-making and threat modeling. These integrated components demonstrate a strong potential to build a resilient, data-driven early warning system for coastal communities. In conclusion, the proposed system offers a precise, adaptive, and scalable approach to improve disaster preparedness in vulnerable coastal regions.

Keywords: early warning system, Mobile GIS, ant algorithm, landscape analysis, coastal disaster mitigation

Security Analysis of Two-Factor Authentication Applications: Vulnerabilities in Data Storage and Management

Syafrial Fachri Pane — 2025-07-14

In the digital era, two-factor authentication (2FA) is used as an additional security measure to protect user access to digital services. However, the storage methods of authentication data in 2FA applications have potential security vulnerabilities that can be exploited. This study analyzes five popular 2FA applications, namely Google Authenticator, 2FAS, Aegis Authenticator, Okta Verify, and TOTP Authenticator, focusing on how secret keys are stored and the potential exploitation risks. The experiment was conducted in a virtual Android environment using rooted BlueStacks 5. Data acquisition was performed using Media Manager and X-Plore File Manager, followed by data analysis with SQLite Browser and bypass with PyOTP. The results indicate that some applications store secret keys in plaintext or weak encryption, making authentication bypass possible by manually generating OTP codes. This study concludes that strengthening data storage security in 2FA applications is crucial to prevent unauthorized exploitation by malicious actors.

Implementation of Deep Learning for Personal Protective Equipment (PPE) Detection on Workers Using the YOLO Algorithm

Rendra Soekarta — 2025-07-21

Occupational accidents represent a major challenge in the construction and manufacturing industries. This study aims to develop a deep learning model for real-time detection of personal protective equipment (PPE) usage using the YOLOv5 algorithm. Utilizing a dataset that includes four classes (hardhat, no hardhat, coverall, and no coverall), the model was trained and evaluated based on precision, recall, and mean Average Precision (mAP) metrics. The results demonstrated that the model achieved a high accuracy level with an mAP of 0.91 and stable performance. The model can also rapidly and effectively detect safety attributes even in complex work environments, such as varied lighting conditions and numerous background objects. Based on usability testing results of 85.35% and satisfactory black box testing, this study produced a prototype web-based application enabling efficient and effective PPE monitoring. The application is designed to support the improvement of workplace safety across various industrial sectors in a more practical and adaptive manner. It is expected to increase PPE compliance, reduce accident risks, and contribute significantly to workplace safety in the industry. The conclusion indicates that the YOLOv5 algorithm holds great potential for implementation in technology-based safety monitoring systems and supports the development of a safer and more modern industry.

Cluster-Based Modeling of Internal Factors and FinTech Influence on Strategy: A Case Study of Bank BNI

Aisyah Aryandani — 2025-07-21

The rapid development of financial technology (FinTech) and shifting organizational dynamics have compelled banking institutions to reassess their internal capabilities and strategic positioning. This study aims to examine the influence of internal factors namely the Core Values of State-Owned Enterprises (AKHLAK), innovation culture, gratitude, employee commitment, and employee performance on the competitive strategy of Bank BNI, while also investigating the moderating role of FinTech. A quantitative research design was employed using a survey method, involving 200 employees of Bank BNI. Data were analyzed using Cluster Analysis and Structural Equation Modeling–Partial Least Squares (SEM–PLS) through WarpPLS software. The results indicate that AKHLAK core values, innovation culture, and gratitude have significant positive effects on employee commitment and performance. Furthermore, both employee commitment and performance significantly enhance the bank’s competitive strategy. FinTech was found to significantly moderate the relationship between employee-related factors and competitive strategy. In conclusion, this study presents an integrated model that highlights the strategic role of internal organizational values and behavior, enhanced by digital technology, in fostering competitive advantage within the banking sector.

Implementation of Mikhmon Server for Qos Optimization and Traffic Control on Mikhmon Hotspot Network at Amicom Net

M.Hasan Amir Husen — 2025-07-28

The increasing demand for stable and fair internet access, particularly on public networks like hotspots, presents challenges in maintaining Quality of Service (QoS) and ensuring balanced traffic distribution. This study aims to implement and evaluate the effectiveness of the Mikhmon Server as a solution for user management and traffic control on the Mikrotik hotspot network at AMICOM NET. A quantitative experimental approach was used by comparing network performance before and after the implementation of Mikhmon. Key QoS parameters measured include latency, throughput, and bandwidth fairness. Mikhmon, a web-based tool for managing Mikrotik users, enables administrators to set speed limits, distribute bandwidth evenly, and monitor user sessions in real-time. The results indicate a significant improvement in traffic management efficiency, with average latency reduced by 52.63% and more equitable bandwidth distribution among users. These findings demonstrate that Mikhmon is a practical and cost-effective tool for optimizing medium-scale hotspot networks.

Digital Forensics on APK Files: A Combined Approach Using MobSF and GHIDRA

Fariz Maulana Rizki — 2025-07-30

The rapid growth of Android smartphones has increased user convenience but also elevated the risk of cybercrime, especially malware attacks using complex obfuscation techniques that hinder detection and analysis. Traditional methods are often insufficient to address these evolving threats. This study integrates automated and manual analysis on APK files using Mobile Security Framework (MobSF) and GHIDRA through reverse engineering. MobSF performs automated static analysis to identify vulnerabilities and security indicators, while GHIDRA is used to decompile binary code into pseudocode for in-depth manual verification. The analysis of the “Pencairan Hadiah” (Prize Disbursement) application revealed dangerous permissions such as RECEIVE_SMS, READ_PHONE_STATE, and SYSTEM_ALERT_WINDOW. Manual inspection with GHIDRA confirmed API calls like getImei() and access to the Telegram API for automated data transmission. Although the bot token was inactive, the findings indicate an intent to exfiltrate sensitive data. The integration of MobSF and GHIDRA provides a deeper understanding and concrete evidence of malicious behavior in APK files, demonstrating the effectiveness of combining automated and manual approaches in digital forensic analysis.