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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sistem Komputer Jurnal Teknologi Informasi dan Ilmu Komputer Infotech Journal CESS (Journal of Computer Engineering, System and Science) Edcomtech Jurnal Kajian Teknologi Pendidikan Jurnal Komtika (Komputasi dan Informatika) Share : Journal of Service Learning JURTEKSI Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Ilmiah Edunomika (JIE) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotech: Journal of Technology Information Jurnal Teknik Informatika (JUTIF) Journal Computer Science and Informatic Systems : J-Cosys Jurnal Dinamika Informatika (JDI) Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Biner : Jurnal Ilmiah Informatika dan Komputer Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Journal Of Artificial Intelligence And Software Engineering SmartComp CSRID Jurnal Komtika (Komputasi dan Informatika) Jurnal Teknik Informatika dan Teknologi Informasi
Faulinda Ely Nastiti
Universitas Duta Bangsa Surakarta
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics Social Sciences

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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.
Hybrid LSTM Forecasting Framework with Mutual Information and PSO–GWO Optimization for Short-Term SARS-CoV-2 Prediction in Indonesia Nastiti, Faulinda Ely; Musa, Shahrulniza; Riadi, Imam
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5485

Abstract

SARS-CoV-2 remains an endemic challenge in Indonesia, requiring reliable short-term forecasting tools that support informatics, digital epidemiology, and data-driven public health systems. Standard LSTM models, while widely used for epidemic forecasting, face notable limitations such as sensitivity to poor weight initialization, and reduced ability to capture interactions within heterogeneous high-dimensional data—resulting in inconsistent performance. This research introduces ADELMI (Adaptive Deep Learning Metaheuristic Intelligence), a unified hybrid forecasting framework specifically designed not only to enhance forecasting accuracy but also to overcome core weaknesses of traditional LSTM architectures when applied to complex epidemic datasets. ADELMI integrates Mutual Information and Pearson Correlation for dual feature selection with a hybrid Particle Swarm–Grey Wolf Optimization (PSO–GWO) approach for optimizing LSTM parameters. The dataset includes 657 daily observations and 82 epidemiological, vaccination, and meteorological variables sourced from the Ministry of Health and BMKG (2020–2021). Feature selection reduced the dataset to 20 relevant predictors for recovery and death and one dominant predictor for positive cases. The optimized 50-unit LSTM with early stopping achieved highly accurate 7-day forecasts, producing MAPE scores of 0.01% (positive cases), 1.44% (recoveries), and 3.00% (deaths) across 5-fold cross-validation. These results significantly outperform ARIMA, SIR, and baseline LSTM models. By unifying dual feature selection with hybrid PSO–GWO optimization, ADELMI improves LSTM stability, weight initialization, and multivariate interaction modeling, delivering more reliable forecasts across heterogeneous datasets. This advancement strengthens informatics through DL-metaheuristic multivariate epidemic modeling and enables proactive, adaptive surveillance against evolving threats such as influenza hybrids.
Co-Authors Aghni Rizqi Nimal Abdu Agustina Srirahayu Anisatul Farida Aprilisa Arum Sari Ardian Pamungkas Arif Wicaksono Septyanto Atina, Vihi Aziiz, Dear Whizkid Dedy Hariyadi Fazlurrahman Bima Fazlurrahman Fazlurrahman Fazlurrahman, Fazlurrahman Hartanti, Dwi Heri Gunawan Imam Riadi Indraswari, Elsa Fachrisa Jayawarsa, A.A. Ketut Khotimah, Agil Husnul Kusumawati, Novi Maulindar, Joni Mei Purweni Mira Erlinawati Musa, Shahrulniza Nindya Dwi Anggana Nur Muftiyanto, R. Taufiq Nurdin, Muhammad Alwan Nurohman, Nurohman Oktaviani, Intan Pamekas, Bondan Wahyu Permatasari, Hanifah Pipin Widyaningsih Pradana, Rico Yoga Prasetyo, Yoga Andrian Purweni, Mei Rifal Bayu Ardi Rina Arum Prastyanti Setyawan, Yanuar Anggit Singgih Purnomo Sopingi Sopingi Sopingi, Sopingi Sri Sumarlinda Sumarlinda, Sri Suryani, Fajar Toni Iksanudin Triana Triana Uning Kristiana Utomo, Diva Reihan Ferdian Virgian Galang Sasongko Vita Aryadi Wijaya, Muhammad Krisna Wijiyanto Wijiyanto, Wijiyanto Yafi, Eiad