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Dress Code Selection Recommender System Based on Smartphone Venus Lidzikri Adhitya; Muhamad Irsan; Muhammad Faris Fathoni; Diky Zakaria
Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 1 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.6.1.82934

Abstract

In the era of rapidly developing information technology, the existence of smartphones has become an integral part of everyday life. Appearance and choice of dress code play a crucial role in a person's self-image. Therefore, this research aims to design a smartphone-based dress code selection recommendation system. This system will use clothing usage data, user preferences, and event context to provide relevant dress code recommendations. With this solution, it is hoped that users can easily and efficiently choose the appropriate dress code, increase self-confidence, and create a pleasant dressing experience. This research contributes to the development of smartphone-based applications to support users' lifestyle and personal appearance. This application not only provides dress code inspiration, but also makes it easier for users to make decisions regarding clothing choices. Model testing using Machine Learning with the K-Nearest Neighbor (KNN) algorithm shows satisfactory accuracy, precision and recall, namely 83.67%, 83.82% and 99.34%. This application has the potential to be a useful tool helping users live an informed fashion lifestyle and according to personal preferences, and also minimize the waste of time that would occur when choosing clothes.
Benchmarking Mobile Apps Security in Universities: An OWASP Mobile Top 10 Framework Perspective Fajar Maulana Kadir; Muhamad Irsan; Aji Gautama Putrada
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 1 (2025): Vol. 11 No. 1 Jun 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i1.9094

Abstract

Leading Indonesian universities such as Telkom University (Tel-U), Institut Teknologi Bandung(ITB), Universitas Indonesia (UI), and Universitas Gadjah Mada (UGM) have developed mobilebasedacademic information systems that improve the accessibility of campus services, wheresensitive information such as personal data, access credentials, and educational information arestored and managed through the mobile application. The current gap is the lack of understanding ofthe specific vulnerability profile of campus mobile applications and how these vulnerabilities canaffect the data security of educational institutions. This study conducts a comparative analysis ofvulnerabilities in campus mobile applications using the OWASP Mobile Top 10 framework as itstesting standard. In its implementation, this study uses three mobile application security testingtools: AndroBugs, Mobile Security Framework (MobSF), and QARK (Quick Android Review Kit).These three tools were chosen because of their ability to detect various types of vulnerabilitiescovered in the OWASP Mobile Top 10. By comparing vulnerability analysis results on differentcampus mobile applications, this study aims to identify common vulnerability patterns and providerecommendations for improvements following the OWASP Mobile Top 10 security standards. Thetest results show that MySIX ITB and WeAreUI have the most vulnerabilities compared to the otherthree campuses, with 24 vulnerabilities from three different tools. However, if we look at theconsensus between the three tools, MySIX ITB is the most vulnerable application, withvulnerabilities in five categories: M3, M5, M6, M8, and M9. In addition to using three differenttools to strengthen the vulnerability detection rate, we also found some new knowledge. The first isthat all three tools have the same agreement for detecting M2, M6, and M8, which shows the highreliability of the three tools for the categories mentioned. The second is the knowledge that QARKmakes the most different decisions from the other two tools. The test results show that QARK makesdifferent decisions eight times. We also learned that for the four campus mobile apps, developersshould pay more attention to two categories detected by each tool, namely M6 and M8, or InadequatePrivacy Controls and Security Misconfiguration, respectively. Finally, there is knowledge that thestrength of the four mobile apps is resistance to M2; in other words, each campus has used thirdpartylibraries well.