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Ahmad Azhari
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ahmad.azhari@tif.uad.ac.id
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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 4 Documents
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Search results for , issue "Vol. 6 No. 1 (2024)" : 4 Documents clear
Decision Support System For Election Suppliers of Goods Using The Simple Method Additive Weighting (SAW) Aprilandri, Gusti; Rumini, Rumini; Maulina, Dina
Mobile and Forensics Vol. 6 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v4i1.5522

Abstract

Selection of suppliers of goods is the main key to starting a retail business. The market will find it difficult to determine suppliers because it will have a big effect on the final selling price. The Diva Shop is a medium-sized business that is still growing in the city. The system run by the store is still running on experience. As a store that is ready to compete, the store needs a supporting design system in determining the selection of suppliers of goods. The system is run using the Simple Additive Weighting (SAW) method. That The system that will be created uses the criteria that will be applied by the user based on the SAW method. The SAW design method uses a weighted summation method with the accumulation of various data, each value from the weight results obtained becomes the final decision. The Simple Additive Weighting (SAW) method can be applied properly and correctly to the decision support system at the Diva Store. The steps and results of calculations manually are the same as the steps and results of calculations performed by the system. For testing applications based on the Confusion Matrix method, the accuracy is 73.77% with an error percentage of 26.23%.
Information Security Readiness Assessment at XYZ Agency Using KAMI Index 4.2 Rahayu, Risgi Sri; Afriyani, Febrilia Dinda
Mobile and Forensics Vol. 6 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v6i1.8649

Abstract

Dinas Xyz is an agency owned by the government of the Special Region of Yogyakarta (DIY) Province. This agency is responsible for managing activities in a region. This research was conducted to evaluate the level of information security readiness at Dinas Xyz using the KAMI Index 4.2 based on ISO/IEC 27001:2013 criteria. This research involves data collection through interviews and observations, followed by data analysis using the KAMI Index categories. The results showed that Dinas Xyz has a sufficient level of information security readiness, with a score of 285 out of a total of 645. This article provides details of the evaluation results for each category. This research suggests improvements to information security management, especially on cloud storage, to meet the minimum requirements of ISO.
EEG Classification for Brain Response Analysis through University Website Interface in Yogyakarta Using Naive Bayes and KNN Suhail, Faiq; Azhari, Ahmad
Mobile and Forensics Vol. 6 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v6i1.9056

Abstract

One of the challenges high school students face is the abundant availability of information about various campuses through different media, making it difficult to accurately predict their interest in a particular campus. Electroencephalogram (EEG) technology can read human brain activity, such as when students access information on a campus website. The Naive Bayes and K-Nearest Neighbor (KNN) methods can be employed to predict student interest in a campus based on EEG signals recorded while they browse the official campus website. Naive Bayes is known for achieving high accuracy with small datasets, whereas KNN excels at classifying noisy data. These two methods offer variables that can be directly compared. Classification using Naive Bayes and KNN achieved the highest accuracy score of 92%. The most appropriate algorithm is determined by evaluating performance using a confusion matrix. In this case study, Naive Bayes slightly outperformed KNN, as evidenced by precision, recall, and f1-score matrices. The Naive Bayes method resulted in an F1-score of 94%, compared to KNN’s 92%.
Network Security Monitoring System via Android Mobile App With IDS Prasetyo, Hamas; Anwar, Nuril
Mobile and Forensics Vol. 6 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v6i1.10317

Abstract

Network security is an important factor in securing data on a server, so a server needs to be kept safe from things that could threaten the validity and integrity of stored data. One way that can be used to detect threats on a server is implementing an Intrusion detection system on the server. A literature study conducted on research that implemented intrusion detection systems, found that there was a lack of intrusion detection system research that could detect one type of network security attack with a variety of attack variables and it was also found in research that had successfully implemented an intrusion detection system to detect network security attacks but still incorrectly identifying the type of attack. This research uses the Snort intrusion detection system method with an experimental model of an attack detection system and an Android application which is applied to monitor the statistics of attacks detected on the Xyz University network. The research results showed that the rules created on the IDS can detect network security attacks, especially DoS/DDoS and PortScan attacks. Then an IDS was created that can send application alert notifications and SMS with a response time that is quite responsive based on the NIST Cybersecurity reference with an average of 22 seconds for DoS/DDoS attacks and 21 seconds for Port Scanning attacks. For the percentage results from 3 times testing the rule by sending DoS/DDoS attack packets of 309,462 to 1,459,548, getting a high level of accuracy with an average of 92.1% on first test, 91.7% on the second test and 91.5% on the third test. In the results of testing the PortScan rule by sending 1,001 to 10,564 attack packets, a high level of accuracy was obtained with an average result of 92.2% in the first test, 94.2% in the second test and 93.4% in the third test.

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