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All Journal Jurnal Pengembangan Sains dan Teknologi Scientific: Journal of Computer Science and Informatics
Yana Safitri
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Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT

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Security Analysis of XYZ Academic Information System Using Information System Security Assessment Framework (ISSAF) Muhammad Amirul Mu'min; Yana Safitri; Sabarudin Saputra
Jurnal Pengembangan Sains dan Teknologi Vol. 1 No. 2 (2025): Juli 2025
Publisher : Yayasan Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/jpst.v1i2.76

Abstract

Academic information system security is a crucial aspect in the development of technology and information today, especially in maintaining structured and comprehensive data from various threats. Academic Information System (AIS) XYZ which provides services based on HTTP or HTTPS protocols is vulnerable to hacker attacks through security holes that may not be realized by the website owner. This study aims to identify and analyze security vulnerabilities in the AIS and provide recommendations for improvements to improve the level of system security. Using the ISSAF method to evaluate system security. The tools used in the analysis include Whois, SSL Scan, Nmap, OWASP Zap, and LOIC to detect and test vulnerabilities on the website. From this study, 12 vulnerabilities were found, consisting of four medium level vulnerabilities, six in moderate vulnerabilities, and two information level vulnerabilities. In improving system security, it is recommended to make improvements to the vulnerabilities found, especially at high and medium levels, and to implement regular security monitoring to prevent future attacks.
Pengenalan Citra Batik Tradisional Menggunakan Deep Learning untuk Klasifikasi Motif Daerah Fanani, Galih Pramuja Inngam; Muhammad Amirul Mu'min; Yana Safitri; Setiawan Ardi Wijaya; Novi Tristanti; Tri Stiyo Famuji
Scientific: Journal of Computer Science and Informatics Vol. 2 No. 1 (2025): Januari 2025
Publisher : Universitas Muhammadiyah Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34304/scientific.v2i1.336

Abstract

Batik merupakan warisan budaya Indonesia yang kaya akan nilai estetika dan keragaman motif berdasarkan asal daerahnya. Namun, upaya digitalisasi dan klasifikasi motif batik secara otomatis masih menghadapi tantangan, terutama dalam hal ketersediaan dataset representatif dan pendekatan pemodelan yang optimal. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi motif batik berdasarkan daerah asal menggunakan metode deep learning berbasis Convolutional Neural Network (CNN). Dataset citra batik yang digunakan terdiri dari 1.200 gambar, mewakili empat daerah utama yaitu Solo, Pekalongan, Cirebon, dan Madura. Model CNN dirancang dengan empat blok konvolusi dan dua fully connected layer, serta dilatih menggunakan optimizer Adam dan teknik early stopping. Hasil eksperimen menunjukkan bahwa model mencapai akurasi klasifikasi yang tinggi dan mampu membedakan motif berdasarkan karakteristik visual khas masing-masing daerah. Meskipun terdapat sedikit kesalahan klasifikasi antara motif yang memiliki kemiripan visual, secara keseluruhan model menunjukkan kinerja yang baik dan stabil. Penelitian ini menyimpulkan bahwa pendekatan deep learning efektif dalam mengenali motif batik secara otomatis dan berpotensi diimplementasikan dalam aplikasi edukasi budaya maupun promosi digital batik berbasis kecerdasan buatan.
Co-Authors Fanani, Galih Pramuja Inngam Muhammad Amirul Mu'min Muhammad Amirul Mu'min Novi Tristanti Sabarudin Saputra Tri Stiyo Famuji Wijaya, Setiawan Ardi