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Ahmad Azhari
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Magister Teknik Informatika Jl. Prof. Dr. Soepomo SH, Janturan, Warungboto, Yogyakarta 55164
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Mobile and Forensics
Published by Universitas Ahmad Dahlan
ISSN : 26566257     EISSN : 27146685     DOI : https://doi.org/10.12928/mf
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Neuroscience
Mobile and Forensics (MF) adalah Jurnal Nasional berbasis online dan open access untuk penelitian terapan pada bidang Mobile Technology dan Digital Forensics. Jurnal ini mengundang seluruh ilmuan dan peneliti dari seluruh dunia untuk bertukar dan menyebarluaskan topik-topik teoritis dan praktik yang berorientasi pada kemajuan teknologi mobile dan digital forensics.
Arjuna Subject : Teknik (semua kategori) - Teknologi Media
Articles 99 Documents
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Embedded System for Automatic Mask Detection using YOLOv4 Deep Learning and PyQt5 Interface Fadllullah, Arif; Langi, Nelson Mandela Rande; Maulana, Ikhsan; Meilindy, Laura Nur; Akbar, Muhammad Adhiya Yudhistira; Rahman, Mukti Dika
Mobile and Forensics Vol. 7 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i1.11951

Abstract

The use of masks remains crucial, especially in high-risk areas for disease transmission, such as airports, schools, hospitals, and crowded places. However, some individuals continue to neglect wearing masks in these locations, leaving the area vulnerable to disease spread, including COVID-19. Therefore, this study proposes the development of an embedded system based on Raspberry Pi 4 for automatic mask detection using YOLOv4 deep learning and a PyQt5 interface. The system is designed to be simple and compact, featuring a user-friendly GUI to effectively detect mask usage on multiple faces in a single detection. Experimental results on 40 samples captured in real-time, with 4 samples taken per image capture and various mask colors and three mask-wearing angles, demonstrated that the average precision, recall, and F1_score rates were each 100%. This outcome proves that the proposed embedded system successfully detects masks on multiple faces with different colors and angles in a single detection with excellent accuracy.
Forensic Analysis of Mobile Application Security Using the IDFIF v2 Framework Setiawan, Abdul Aziz; Sutanto, Imam
Mobile and Forensics Vol. 7 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i1.12660

Abstract

Mobile application security has become an important issue in the midst of increasing cyber attacks, especially on the Android platform. This research aims to analyse the vulnerability of mobile applications using the Integrated Digital Forensics Investigation Framework (IDFIF) version 2 framework with a focus on the Laboratory Process stage. The tool used is Mobile Security Framework (MobSF) for static and dynamic analysis, supported by Genymotion emulator.The results show that the tested application has several vulnerabilities, such as malicious permissions (READ_EXTERNAL_STORAGE and WRITE_EXTERNAL_STORAGE), the use of v1 signature schemes that are vulnerable to Janus attacks, as well as the ability to manipulate the application through bypass debugging. However, no vulnerabilities were found in the SSL Pinning process. These findings provide important insights into security mitigation measures, such as removing malicious permissions, updating certificate mechanisms, and encrypting sensitive data.The application of IDFIF v2 in this investigation demonstrates its effectiveness in systematically detecting and analysing mobile application vulnerabilities, contributing to the development of better security protocols in the future.
K-Nearest Neighbors for Fast and Accurate Qibla Direction Determination Wardoyo, Girindra Sulistiyo; Azhari, Ahmad
Mobile and Forensics Vol. 7 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i1.12781

Abstract

Determining the correct direction of the Qibla is essential for the validity of prayer, but in areas far from the Kaaba, this can be a challenge. While calculating the Qibla azimuth using latitude and longitude is relatively straightforward, traditional methods for obtaining the Qibla direction, such as those provided by the Muhammadiyah Central Leadership’s Tarjih Center, are time-consuming and require specialized teams. This paper proposes a recommendation system that uses the K-Nearest Neighbors (K-NN) algorithm to provide an efficient and automated solution for determining the Qibla direction. The system leverages the Google Maps API to obtain geographic coordinates and calculates the Qibla azimuth by applying the Euclidean distance formula between latitude and longitude points. The K-NN method is employed to recommend the nearest mosque or prayer room that is aligned with the correct Qibla direction, based on proximity and geographic data. This approach eliminates the need for a dedicated team and significantly reduces the time required for users to find the correct direction. The system's performance was tested through Black Box testing to ensure all features functioned as expected. User acceptance was measured using the System Usability Scale (SUS), resulting in an average score of 76.33, indicating good usability. Additionally, accuracy testing compared the recommended Qibla direction from 28 mosques and prayer rooms with another established system, yielding an accuracy of 78.57%. These results demonstrate that the proposed K-NN-based recommendation system is both effective and efficient for determining the Qibla direction.
Integration of Landscape Analysis, Mobile GIS and Ant Algorithm for Early Warning and Coastal Security Systems Aziz, Muhammad
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.13176

Abstract

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 Pane, Syafrial Fachri; Haq, Dzikri Izzatul; Siregar, M. Amran Hakim
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.13312

Abstract

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 Soekarta, Rendra; Yusuf, Muhammad; Visman, Javan; Hasa, Muh. Fadli; Firdaus, Asno Azzawagama
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.13884

Abstract

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 Aryandani, Aisyah; Firdaus, Asno Azzawagama
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.14066

Abstract

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 Husen, M.Hasan Amir; Firmansyah, Firmansyah
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.14076

Abstract

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; Mukhlis Prasetyo Aji; Ermadi Satriya Wijaya; Harjono
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.14088

Abstract

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.

Page 10 of 10 | Total Record : 99

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