Mobile and Forensics

Analysis of Instagram Messages in Drug Cases using Nist Methods

Muhammad Raihandaffa Dzikrianasa — 2025-01-20

The advancement of communication technology has had a significant impact on all levels of society, particularly through the use of social media platforms such as Instagram. However, this application also has negative impacts, including its misuse by criminals to facilitate the distribution of drugs, leading to transactions where anti-forensics measures are employed by sellers during conversations. This research utilizes the National Institute of Standards and Technology (NIST) method, which consists of the steps of collection, examination, analysis, and reporting. This method is applied to identify and uncover evidence in drug transaction cases on Instagram. The researchers focus on messages between buyers and sellers, whether they have been deleted or not. The results of the study indicate that digital forensic steps using the NIST method can be applied in the process of retrieving digital evidence from the Instagram application on the smartphones of buyers and sellers. The digital forensic tools used successfully revealed relevant digital evidence on the Instagram application.

Embedded System for Automatic Mask Detection using YOLOv4 Deep Learning and PyQt5 Interface

Arif Fadllullah — 2025-03-05

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.

K-Nearest Neighbors for Fast and Accurate Qibla Direction Determination

Girindra Sulistiyo Wardoyo — 2025-03-17

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.

Forensic Analysis of Mobile Application Security Using the IDFIF v2 Framework

Abdul Aziz Setiawan — 2025-03-20

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.

A Security Development Life Cycle (SDLC)-Based Approach for Designing Intrusion Detection and Prevention Systems to Counter SQL Injection Attacks at MAN 2 Magetan

Muhammad Naufal Hafizh — 2025-03-20

Information security is a critical aspect of ensuring the validity, integrity, and availability of data while protecting users’ access to services. Inadequate security measures can expose systems to various threats, potentially compromising their functionality. One such threat is SQL Injection, a common attack vector targeting web applications. MAN 2 Magetan, an Islamic high school located in Purwosari, Magetan Regency, East Java, Indonesia, operates an online admission system on its website. However, this website contains input fields that are not properly validated, creating a vulnerability to SQL Injection attacks. This study aims to design and implement an Intrusion Detection and Prevention System (IDPS) to mitigate SQL Injection attacks using the Security Development Life Cycle (SDLC) methodology. The SDLC process for the system development consists of five stages: Analysis, Design, Implementation, Enforcement, and Enhancement. A hybrid system combining Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) was utilized to create an effective solution. The results of the research demonstrate that the developed IDPS successfully detects and prevents SQL Injection attacks, ensuring the security and integrity of the online admission system. The integration of IDS and IPS within the SDLC framework has proven to be an effective approach to enhancing web application security at MAN 2 Magetan.