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Analisis Keamanan Sistem Informasi Menggunakan Sudomy dan OWASP ZAP di Universitas Duta Bangsa Surakarta Hariyadi, Dedy; Nastiti, Faulinda Ely
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5134

Abstract

Peretas saat ini tidak hanya menyerang instansi pemerintah seperti pada tahun 2019 melainkan sudah melakukan serangan ke instansi pendidikan. Hal ini sesuai dengan pantauan dan identifikasi Badan Siber dan Sandi Negara bahwa instansi pendidikan telah diserang sebanyak 38% pada tahun 2020. Sebagai wujud tindakan preventif terkait dengan serangan siber pada instansi pendidikan perlu dilakukan sebuah tindakan analisis keamanan informasi terhadap sistem-sistem yang terpasang. Pada artikel ini diusulkan tahapan teknis melakukan analisis keamanan informasi menggunakan perangkat lunak dengan lisensi Free Open Source Software, yaitu Sudomy dan OWASP ZAP. Menggunakan kedua perangkat lunak tersebut didapatkan hasil analisis potensi-potensi celah keamanan pada sistem informasi yang terpasang pada Universitas Duta Bangsa.
Model Inspeksi Keamanan Jaringan Nirkabel Dengan Teknik Wardrving Berbasis ChatBot Purweni, Mei; Hariyadi, Dedy; Nastiti, Faulinda Ely; Fazlurrahman, Fazlurrahman
Jurnal Komtika (Komputasi dan Informatika) Vol 6 No 2 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v6i2.7943

Abstract

Penggunaan perangkat jaringan nirkabel seperti Access Point perlu dianalisis untuk menghindari serangan intersep ataupun bypass oleh pelaku kejahatan. Kegiatan inspeksi keamanan jaringan nirkabel dalam inspeksi/operasi intelligen harus dilakukan secara rahasia, akurat dan cepat. Penelitian ini mengusulkan pengembangan aplikasi pengumpulan informasi dan pemetaan perangkat jaringan access point dalam operasi intelijen oleh petugas lapangan sebagai bahan rujukan dalam penyajian laporan saat proses penyidikan. Pengumpulan informasi pada penelitian ini dilakukan dengan pendekatan Signal Intelligence yang telah diselaraskan dengan model Signal Intelligence dan Intelligence Collection System. Penelitian ini telah menggunakan kedua cabang tersebut dikolaborasikan dengan komunikasi chatbot untuk mempermudah proses analisis petugas lapangan yang disertai dengan aktivitas menyaru data jaringan di tengah-tengah masyarakat.
Pengembangan Prototipe Token Transaksi Cryptocurrency SDSPay Berbasis Blockchain Ethereum Virgian Galang Sasongko; Faulinda Ely Nastiti; Sopingi Sopingi
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5665

Abstract

The transformation of digital payment systems through blockchain technology has brought new challenges and opportunities in the development of secure, efficient, and decentralized crypto tokens. One emerging approach is the use of smart contract-based tokens, such as ERC-20 tokens running on the Ethereum network. This research proposes the design and implementation of the SDSPay (SDS) token as a prototype Ethereum-based ERC-20 token, with a modern smart contract approach to meet the needs of a more efficient and secure digital payment system. The SDS token adopts the basic ERC-20 standard but is equipped with advanced features that enhance functionality and security, such as role-based access control (RBAC) to regulate access and control over transactions, pauseable transactions to pause transactions if necessary, and compatibility with EIP-2612 permits that enable more efficient transaction authorization in terms of gas. These features are designed to improve the efficiency and security of transactions on blockchain networks, thus enabling the use of tokens in a more reliable digital payment system. The SDS token prototype was tested on the Sepolia Testnet using Remix IDE and MetaMask to develop and manage smart contracts. Additionally, a static security audit was conducted using Slither Analyzer to detect potential vulnerabilities. The test results showed that the SDS token was successfully deployed and performed well, with an average transaction time of 10–12 seconds and stable gas fees. The Slither audit also found no significant vulnerabilities, indicating that the smart contract structure adheres to security best practices. This study confirms that the development of standardized smart contract-based tokens can be carried out using an efficient, reliable, and replicable methodology for other applications in future blockchain-based payment systems. This implementation of the SDSPay (SDS) token can serve as a foundation for designing secure and efficient digital payment systems, paving the way for the broader development of blockchain technology.
Bridging hybrid deep learning detection and lightweight handcrafted features for robust single sample face recognition Nastiti, Faulinda Ely; Sopingi, Sopingi; Hariyadi, Dedy; Sumarlinda, Sri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp888-900

Abstract

Single sample face recognition (SSFR) remains a challenging task due to the limitation of having only one reference image per identity, which reduces embedding diversity and decreases robustness under variations of pose, expression, and illumination. This study proposed a hybrid framework that integrates deep learning-based detection through anchor box optimization and non-maximum suppression (NMS) with lightweight handcrafted feature extraction using local binary pattern (LBP). The detection stage leverages deep learning to ensure robust face localisation, while LBP maintains computational efficiency under limited-sample conditions. The training process showed accuracy improvement from 47.5% at the initial epoch to 98.0% at epoch 72, while testing accuracy stabilized at 85-88% with the best value of 87.9%. Evaluation on 48 new facial images achieved 89.6% accuracy, 95.3% precision, 91.1% recall, 93.1% F1-score, and 0.94 area under the receiver operating characteristic curve (AUC ROC). Real-world implementation on Android and iOS-based attendance applications further validated the model, reaching 88.46% accuracy across 52 tests under 50-400 lux illumination. The findings proved that the proposed hybrid design provides improved accuracy and stability compared with previous approaches.