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All Journal SMARTICS Journal Julia Jurnal
Kartika Imam Santoso
Universitas An Nuur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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PENGAMANAN DATABASE MENGGUNAKAN MODEL HIBRID BLOCKCHAIN DAN HOMOMORPIC ENCRYPTION DENGAN OPTIMASI ZERO-KNOWLEDGE PROOF Muhammad Muzammil; Deden Pratyaten; Nadia Cahaya Islami; Kartika Imam Santoso; Eko Supriyadi
Julia: Jurnal Ilmu Komputer An Nuur Vol 6 No 1 (2026): juliajournal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v6i1.33

Abstract

Database security has become a crucial issue with the increasing cyber threats and data breaches that significantly impact organizations and individuals. This research proposes an innovative hybrid model that integrates blockchain technology, homomorphic encryption, and zero-knowledge proof to secure databases. This approach offers a multi-layer security mechanism that enables user identity verification without exposing credentials, encrypted data processing, and immutable access tracking. The research method uses an experimental approach with attack simulations on databases implemented with and without the proposed hybrid model. Results show significant improvements in resistance against SQL injection attacks (99.7%), man-in-the-middle attacks (98.2%), and unauthorized access (100%) compared to conventional methods. Although there is a trade-off in the form of a 12% latency increase, this hybrid model offers an optimal balance between security and performance. The main contribution of this research is the development of zero-knowledge proof integration algorithm that optimizes the verification process by reducing computational overhead by 35% compared to conventional implementations.
Performance Evaluation of Agentic Workflow-Driven Trend-Aware Rule Mining for Dynamic Menu Bundling Andri Triyono; Kartika Imam Santoso; Rohman Hadi Al Haq
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.14079

Abstract

Digital transformation in the culinary industry currently demands moving beyond writing static lines of code, instead acting as an AI orchestrator adaptive to real-world conditions. This research focuses on addressing significant challenges in traditional data mining methods, such as the Apriori and FP-Growth algorithms, which often lack the flexibility to handle dynamic variables like ambient temperature fluctuations.Through the innovative orchestration of the Trend-Aware Rule Mining (TARM) algorithm and a LangGraphbased Agentic Workflow, this study transforms raw association rules into strategic business decisions via an iterative reasoning process and self-correction mechanism. Experimental results on a dataset of 52,494 rows demonstrate TARM's computational superiority, with memory usage of only 8.04 MB , significantly more efficient than Apriori's 127.44 MB. Furthermore, the synergy between the Strategy Agent and Evaluator Agent achieved a logic consistency score of 100% , validated by an independent audit with an average score of 96.25%.These findings confirm that the developed system is in a ready-to-use state to support precise and adaptive decision-making automation in production environments.
Sistem Penjualan Pakaian Online "tukuCALAMBY" Anjar Septinegara; Freshma Neda Cisya Tama; Isyatin Rodliyati Karima; Kartika Imam Santoso; Dhika Malita Puspita Arum
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.26

Abstract

The development of information and communication technology has changed the way consumers shop, especially in the fashion and clothing industry. This study aims to develop an online clothing ordering system called “tukuCALAMBY.” This system is useful for improving the efficiency of the sales process and providing convenience for customers when shopping. The system is designed using a web-based approach with two main actors, namely the admin and the customer. The system development method employs the Software Development Life Cycle (SDLC) approach using the Waterfall model by Sommerville. The design utilizes system modeling with the Unified Modeling Language (UML). The development results demonstrate that the “tukuCALAMBY” system successfully integrates features for managing product data, ordering, payment, and reporting into a single user-friendly platform. This system provides an effective solution to expand market reach and improve operational efficiency for online clothing stores. User Acceptance Testing (UAT) involving 20 users yielded a testing result of 92%.
TRANSFORMASI DIGITAL UMKM PERCETAKAN: OPTIMALISASI PLATFORM ECOMMERCE TERINTEGRASI PADA ESPRINT.STORE Muhammad Nabil Musyarof; Afif Kisnandhya Putra; Nibroos Naufal Islam; Rizky Dwi Astuti; Kartika Imam Santoso; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.27

Abstract

Digital transformation has become a strategic necessity for Micro, Small, and Medium Enterprises (MSMEs), particularly in the printing sector which demands speed, flexibility, and personalized services. This study aims to examine the effectiveness of the esprint.store platform as a web-based eCommerce solution integrated with WhatsApp API and a dynamic pricing system. A mixed-method approach was employed, combining Google Analytics data, a System Usability Scale (SUS) questionnaire from 120 respondents, and system architecture observation. The results indicate a 35% increase in sales conversion and a reduction in customer response time from 24 hours to 15 minutes. These findings suggest that digitalization through a simple yet functional system can enhance service efficiency and customer satisfaction within the MSME.
AI-BAHSI: Metode Hibrid Artificial Intelligence-Behavioral Analysis dan Hybrid Security Intelligence untuk Deteksi dan Mitigasi Ancaman Real-time pada Wireless Access Point Rheimanda Devin Emmanuel; Ani Anggraini; Agus Condro Wibowo; Kartika Imam Santoso; Eko Supriyadi
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.30

Abstract

Wireless access point (AP) security faces significant challenges with the emergence of sophisticated attacks such as SSID Confusion (CVE-2023-52424), KRACK attacks, and advanced persistent threats. This research develops a hybrid AI-BAHSI (Artificial Intelligence-Behavioral Analysis and Hybrid Security Intelligence) method that integrates deep learning, ensemble machine learning, and federated learning for real-time threat detection and mitigation on wireless access points. The proposed method combines Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for pattern recognition, Random Forest-Support Vector Machine ensemble for threat classification, and federated learning for privacy-preserving security intelligence. Evaluation was conducted on a synthetic dataset that includes 15,000 normal traffic samples and 8,500 attack samples of various types. The results show that AI-BAHSI achieves a detection accuracy of 98.7%, a precision of 97.3%, a recall of 98.1%, and an F1-score of 97.7% with a false positive rate of only 1.2%. This method successfully detected zero-day attacks with a 94.6% confidence level and was able to automatically mitigate them in an average of 0.8 seconds. The main contribution of this research is the development of an adaptive security framework that can learn from new attack patterns in real time while preserving privacy through a federated learning architecture.
SMART-GUARD: Self-adaptive Multi-Agent Reinforcement learning Threat Guard dengan Game Theory dan Consensus Mechanisms untuk Enhanced Wireless Access Point Security  Dwi Kurniawan Aprilianto; Ahmad Yusuf Mufarihin; Akhie Najhan Atifa; Eko Supriyadi; Kartika Imam Santoso
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.32

Abstract

Kompleksitas serangan cyber terhadap wireless access point semakin meningkat dengan munculnya adversarial AI dan coordinated attack scenarios. Penelitian ini mengembangkan framework SMART-GUARD (Self-adaptive Multi-Agent Reinforcement learning Threat Guard) yang mengintegrasikan multi-agent reinforcement learning (MARL), game theory, dan consensus mechanisms untuk membangun sistem pertahanan adaptif dan kolaboratif. Framework yang diusulkan menggabungkan Deep Q-Networks (DQN) dengan hierarchical multi-agent architecture, Stackelberg game untuk strategic defense planning, Self-Organizing Maps (SOM) untuk threat clustering, dan Byzantine-fault tolerant consensus untuk koordinasi terdistribusi. Evaluasi dilakukan pada testbed yang mensimulasikan 20 access points dengan 500 client devices dan 15 jenis serangan berbeda. Hasil eksperimen menunjukkan SMART-GUARD mencapai defense success rate 97.4%, mean response time 1.2 detik, dan resource utilization efficiency 89.3%. Framework ini mampu beradaptasi dengan 12 jenis zero-day attacks dengan confidence level 92.8% dan menunjukkan scalability yang superior hingga 1000+ access points. Kontribusi utama penelitian ini adalah pengembangan self-adaptive defense ecosystem yang dapat melakukan strategic decision making secara autonomous melalui game-theoretic analysis dan koordinasi multi-agent yang fault-tolerant.
Model Hibrid Keamanan Komunikasi Data Menggunakan Kriptografi Berbasis Federated Lattice Dan Stegnografi Homomorfik Dengan Optimasi Quantum-Resistant Protocol Yekti Kuncorojati; Metha Mudrifah Zain; Sindy Hertika Putri Sindy; Kartika Imam Santoso; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 6 No 2 (2026): juliajurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v6i2.53

Abstract

Data communication security faces significant challenges with the development of quantum computing and the increasing complexity of cyberattacks. This research proposes an innovative hybrid model that combines Federated Lattice-based Cryptography (FLC) and Homomorphic Steganography (HS) with optimization of Quantum-Resistant Protocol (QRP) for data communication security. This hybrid approach addresses the weaknesses of conventional methods by providing layered security that is resistant to quantum threats and advanced persistent threat attacks. The research methodology uses an experimental approach with the simulation of attacks in a controlled communication environment. Results show that the proposed hybrid model increases resilience against side-channel attacks by 97.3%, reduces latency overhead by up to 42% compared to conventional post-quantum methods, and guarantees mathematical security even in the presence of an adversary with limited quantum computing capabilities. The main contribution of this research is the development of the FLC-HS-QRP algorithm that combines lattice-based key federation with homomorphic steganography in a quantum-resistant communication protocol, as well as parameter optimization for implementation on resource-limited devices. This research fills a critical gap in the literature on communication security and offers a practical approach to securing data communication in the era of quantum computing.
Co-Authors Afif Kisnandhya Putra Agus Condro Wibowo Ahmad Yusuf Mufarihin Akhie Najhan Atifa Andri Triyono Ani Anggraini Anjar Septinegara Arum, Dhika Malita Puspita Deden Pratyaten Dwi Kurniawan Aprilianto Eko Supriyadi Eko Supriyadi Freshma Neda Cisya Tama Isyatin Rodliyati Karima Metha Mudrifah Zain Muhammad Muzammil Muhammad Nabil Musyarof Nadia Cahaya Islami Nibroos Naufal Islam Rheimanda Devin Emmanuel Rizky Dwi Astuti Rohman Hadi Al Haq Sindy Hertika Putri Sindy Yekti Kuncorojati