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Ahmad Azhari
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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 5 Documents
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Search results for , issue "Vol. 5 No. 1 (2023)" : 5 Documents clear
Improved Breadth First Search For Public Transit Line Search Optimization Kartoirono, Suprihatin; Riadi, Imam; Furizal, Furizal; Azhari, Ahmad
Mobile and Forensics Vol. 5 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v5i1.7906

Abstract

People in general find it difficult to determine the transportation route, because to get to one destination there are many alternative paths that must be passed. This study aims to model the search for alternative bus route routes that are faster to produce routes that must be passed. The method used in this study is Improved Breadth first search by modifying BFS so that its performance is improved in producing route search completion. The improved BFS method is basically the same as BFS doing a level-by-level search stop if a false finish point is found. As the experiment above with a starting point of 175 and an end point of 54 the BFS algorithm takes 27 seconds 564 milliseconds, while the Improve BFS algorithm takes 171 milliseconds. The results showed that improved BFS can improve the performance of the BFS method. Research can be a model to be applied to other optimal route finding cases.
Forensic Artifact Discovery and Suspect Profiling through Google Assistant Rahaman, Abu Sayed Md. Mostafizur; Marzia, Saiyeda; Arnob, Tafsir Haque; Rahman, Md.Zahidur; Akhter, Jesmin
Mobile and Forensics Vol. 5 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v5i1.8046

Abstract

It has become impossible to imagine modern society without the internet and mobile devices dominating our daily lives. As a result, popular apps like Google Assistant, Gmail, Google Home, etc., are quietly entering our veins. People aren't even aware of how much of a digital footprint they leave behind, let alone the fact that it can be completely chronologized and used to put any criminal in jail. The data that we intentionally send to Google is fully retrievable from both the client side (a mobile device) and the cloud. Even if a suspect changes his mobile device, his previous digital footprint still follows him wherever he goes. In this project, we pinpointed the location of the primary database and the major repository for Google Assistant. Here, forensic artifacts of interest from our inquiry have been revealed, including the timeline and copies of previously traded audio chats, as well as a record of deleted data. In addition to that, we have applied the K-means clustering algorithm to isolate the suspect’s voice records and their chronological order among various call records stored in the cloud, where the cluster size is determined using the Silhouette score and the CH index. The findings of the research are to identify forensic artifacts and suspect profiling so that forensic investigators make it easier to conduct criminal investigations
Motion Capture Technique with Enhancement Filters for Humanoid Model Movement Animation Habibillah, Ahmad Yasin; Prahara, Adhi; Murinto, Murinto
Mobile and Forensics Vol. 5 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v5i1.6534

Abstract

The definition of 3D animation is a representation of objects that are made into animation using characters or objects to look more alive and real. Making 3D animation itself requires a long process and a large amount of funding. This is because most 3D animated films still use key-framing technology which causes the process to make an animation to take a lot of steps. In this research, a motion capture technique with an enhancement filter is proposed to make humanoid movement animation using Kinect 2.0. The method consists of several steps such as recording every skeleton joint of human movements using a Kinect sensor, filtering the movements to minimize the shakiness and jitter from Kinect data, mapping skeleton data to the bones of a rigged humanoid model, and recording each movement to make animation. The final result is in the form of a 3D animation of modern dance movements. The method is tested by measuring the similarity between the 3D humanoid model and the user movement. From the 10 animations of modern dance generated by the method and performed by the user, a questionnaire to measure the MRI and MSE value is distributed and the result achieves 4.27 on a scale of 5 for the averaged MRI score and 0.0539 for the MSE score. The MSE value is less than 5% which means the system is categorized as acceptable.
K-Means for Majoring Informatics Students' Interests Based on Brainwave Signals Robin, Qori Aulia; Azhari, Ahmad
Mobile and Forensics Vol. 5 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v5i1.6629

Abstract

This study investigates the potential of utilizing EEG (electroencephalogram) as a determinant for the specialization choices of Informatics students. EEG, measuring brain activity patterns, is employed to discern majors of interest among students. A questionnaire revealed that some students opt for specializations due to class availability and peer influence, leading to potential mismatches between their abilities and interests, consequently affecting their final project or thesis. EEG data from 30 respondents, recorded using NeuroSky Mindwave and MyndPlayer Pro software, were subjected to K-Means Clustering after feature extraction through PCA. However, the evaluation using Silhouette indicated a low score of 0.453, possibly due to significant distance between cluster data and centroids, minimal dataset size, and random respondent selection without considering their specific areas of interest. This suggests limitations in using EEG alone for determining specialization choices, necessitating further refinement and integration with additional factors for more accurate predictions.
Impact Analysis of Web Application Firewall on Website-Based Application Security (Case Study PPDB Kak Seto School Website) Pratama, Krisna Dewa; Anwar, Nuril
Mobile and Forensics Vol. 5 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v5i1.8914

Abstract

The swift advancement of web-based applications has posed security challenges. Insufficient security awareness among web developers has resulted in a surge of cybercrime incidents due to website vulnerabilities. To counter this, implementing a Web Application Firewall (WAF) is proposed for the vulnerable PPDB Sekolah Kak Seto website, aiming to mitigate threats in the public network. The WAF acts as a defense against potential cyber breaches. Employing an experimental approach, this research encompasses identification, observation, literature review, analysis of WAF system requirements, implementation, testing, and pre/post-implementation analysis using ModSecurity as the security system. The study analyzes the impact of WAF adoption and provides recommendations for enhancing security. Findings demonstrate WAF's effectiveness in fortifying the Kak Seto School web application by efficiently identifying and blocking potential attacks, thereby reducing breach success rates. Post-WAF implementation, Pingdom tests show a slight drop in Performance Grade (70 to 69) and a minor increase in Load Time (2.76 to 3.23 seconds). GTmetrix tests reveal a Grade downgrade from B to C and an increase in Largest Contentful Paint time (2.2 to 2.7 seconds). In conclusion, despite minor performance effects, WAF significantly enhances security, as evident in improved loading times during tests.

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