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Donna Setiawati
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JITU : Journal Informatic Technology And Communication
Published by Universitas Boyolali
ISSN : -     EISSN : 26205157     DOI : 10.36596
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science
JITU : Journal Informatic Technology And Communication adalah terbitan berkala ilmiah yang fokus pada teknologi informasi dan komunikasi yang berbentuk kumpulan/akumulasi pengetahuan baru, pengamatan empirik atau hasil penelitian, dan pengembangan gagasan atau usulan baru. Beberapa sub bidang ilmu yang menjadi fokus ilmu komputer antara lain: Internet of Things (IoT), electronics engineering, software engineering, mobile technology and applications, robotics, database system, information engineering, artificial intelligence, interactive multimedia, computer networking, information system audit, accounting information system, information technology investment, information system development methodology, strategic information system (business intelligence, decision support system, executive information system, enterprise system, knowledge management), e-learning, and e-business (e-health, e-commerce, e-supply chain management, e-customer relationship management, e-marketing, and e-government).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 126 Documents
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Implementasi Hierarchical Clustering untuk Analisis FDMC Narrative Crypto Berbasis Web M. Fathir Adha; Hendrik Fery Herdiatmoko
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2255

Abstract

This study implements the Hierarchical Clustering algorithm with Ward linkage and Euclidean distance methods to analyze 26 crypto narratives based on the Fully Diluted Market Cap (FDMC) metric. Using a hybrid method that integrates Waterfall, Cross-Industry Standard Process for Data Mining (CRISP-DM), and Knowledge Discovery in Databases (KDD), data was obtained from the CoinGecko API, manually clustered, and aggregated per narrative. Pre-processing involved logarithmic transformation (log-10) and Z-Score normalization to address power-law distributions and outliers, resulting in a more stable cluster structure. The clustering results mapped the market into five clusters: Bluechip (L1 with FDMC $2.76T), Growth (PAY, MEME, CEX, DEX, DeFi totaling $468.22B), Growth (AI, DePIN, DAO, L2, RWA, ORC, GameFi, XCH, DID, PRC, LST with $192.91B), Speculative (NFT, MET, SocialFi, BTC Eco, W3I with $17.55B), and Speculative (LPD, GambleFi, FTO, SEC with $2.34B). The model was validated with a Silhouette Score of 0.650 and a Cophenetic Correlation Coefficient of 0.647, indicating cohesive and representative clusters. A web-based implementation using Django, D3.js, and Chart.js provides interactive visualizations and portfolio recommendations. Contributions include a novel fundamental valuation approach, an adaptive clustering model, and practical analytical tools for investors, with potential expansion to multidimensional metrics in the future.
Optimasi Deteksi Hama Tanaman Melon Berbasis YOLOv9 Aulia, Muhammad Immawan; Yudhana, Anton; Sunardi
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2265

Abstract

Pest attacks are one of the main problems in melon cultivation, significantly impacting productivity and crop quality. Manual pest identification has limitations in terms of objectivity, consistency, and efficiency, especially in medium to large-scale agricultural fields. This study developed a computer vision-based visual detection system for melon pests by utilizing the YOLOv9 architecture and public datasets obtained from the Roboflow platform. The dataset used consisted of 1,198 images, divided into 879 training images, 131 validation images, and 188 test images. The model training process employed data augmentation techniques, generating three outputs per training example and adding noise up to 2.52% of pixels to enhance the model's resilience to visual variations. The research methodology included system architecture design, data preprocessing, model training, and performance evaluation using precision, recall, and mean Average Precision (mAP) metrics. The test results showed that the system achieved mAP@50 of 61.6%, with 56.9% precision and 58.8% recall, indicating adequate detection capability with good inference efficiency. Thus, the developed system has the potential to be used as an early detection mechanism for melon plant pests to support decision-making in precision agriculture.
Implementasi Secure Tunnel pada Peering BGP untuk Mitigasi Serangan Man-in-the-Middle di Jaringan TCP/IP Surono; Agus Hartanto; Setiarso, Galih; Pandu G, Krida
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2284

Abstract

The Border Gateway Protocol (BGP), as the core internet routing protocol, lacks built-in security mechanisms, making it vulnerable to Man-in-the-Middle (MITM) attacks and sniffing. This research aims to test the effectiveness of an OpenVPN-based secure tunnel in enhancing the security of BGP peering sessions while analyzing its impact on network performance. The method used is an experiment with a pre-test and post-test design, comparing conditions before and after OpenVPN implementation between two routers on different platforms (Linux/FRRouting and MikroTik RouterOS). Test results show that OpenVPN successfully secures BGP communication by encrypting all traffic, thereby eliminating the risk of plaintext reading and passive MITM attacks. However, this implementation introduces a performance trade-off: latency increases by 2.6 ms (50%), throughput decreases by 289 Mbps (30.6%), and CPU utilization surges up to 60% due to encryption overhead. Nonetheless, BGP session stability is maintained with 99.95% uptime. The research concludes that OpenVPN is an effective solution for securing BGP in high-risk environments, with the caveat that hardware capacity and bandwidth requirements must be evaluated to minimize performance overhead impact.
Teori Graf Diskrit untuk Deteksi Intrusi dan Optimasi Firewall: Systematic Literature Review Adnan, Andhika; Tjiptabudi, Fransiskus Mario Hartono; Ndaumanu, Ricky Imanuel; Malelak, Yohanis
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2306

Abstract

The escalating complexity of computer networks and cybersecurity threats demand analytical approaches capable of systematically and measurably representing network structure. Discrete mathematical graph theory offers a formal framework for modeling network topology as nodes and edges, thus potentially supporting more effective intrusion detection and firewall placement optimization. This research aims to conduct a Systematic Literature Review of publications from 2022–2026 to identify graph theory applications in intrusion detection, evaluate the most effective graph-based firewall optimization methods, and map research gaps and future development trends. The methodology employed follows the SLR protocol with stages of systematic search across reputable databases, selection based on inclusion-exclusion criteria, and analysis through descriptive-comparative meta-analysis, thematic meta-synthesis, and content analysis. Results show the dominance of weighted graphs and structure-based learning approaches for network anomaly detection, as well as firewall optimization modeling through integer linear programming and graph heuristics. This research contributes to presenting an integrated synthesis between intrusion detection and firewall optimization within discrete graph framework, and provides conceptual foundation for developing adaptive network security models based on mathematical structure.
Integrasi Moodle API dan LLM dalam Otomasi Monitoring Capaian Pembelajaran Lulusan Berbasis OBE Wicaksono Yuli Sulistyo; Joko Supriyanto
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2307

Abstract

The implementation of Outcome-Based Education (OBE) necessitates transparency and accountability in mapping Program Learning Outcomes (PLO). Nevertheless, the manual mapping process from an extensive array of courses (60 Course Learning Outcomes/CLO) to 10 PLOs frequently encounters administrative constraints and a high risk of human error. This study aims to design and develop an integrated OBE monitoring system utilizing the Moodle API for grade synchronization and the OpenAI API for academic documentation assistance. Adopting the Design Science Research (DSR) methodology, the system was implemented using native PHP. The findings demonstrate that Moodle API integration facilitates the real-time automation of grade retrieval per sub-CLO. Simultaneously, the application of Mermaid.js effectively transforms these data into dynamic traceability visualizations. Moreover, the implementation of the OpenAI API (GPT) provides significant cognitive assistance for faculty members in drafting Semester Learning Plans (RPS) aligned with competency standards. This system establishes a robust data infrastructure for institutions to perform accurate and transparent curriculum evaluations.
Meningkatkan Efisiensi Energi Perangkat Edge melalui Optimasi Pruning dan Kuantisasi Model Sembilu, Nambi; Mukhlis, Iqbal Ramadhani; Satibi, Iswanda Fauzan
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2324

Abstract

Edge computing devices are increasingly tasked with performing artificial intelligence inference under strict constraints on processing capacity and power consumption. This study evaluates magnitude-based weight pruning and dynamic quantization as practical model compression techniques for energy-efficient edge AI deployment. MobileNetV2, pretrained on ImageNet, was adapted to the CIFAR-10 classification task and compressed under three configurations: 40% L1 unstructured pruning followed by recovery fine-tuning (Prune40), dynamic INT8 post-training quantization (QuantINT8), and a sequential combination of both (Prune+Quant). All experiments were executed on a physical Intel N150 mini PC with a thermal design power of 6 watts, using PyTorch 2.1 in CPU-only inference mode. Results show that Prune40 reduced inference latency by 17.9% while simultaneously improving classification accuracy by 1.04 percentage points, attributed to the implicit regularisation effect of sparse weight removal and recovery fine-tuning. QuantINT8 yielded moderate latency savings (6.6%) with negligible accuracy loss. The combined pipeline achieved the lowest absolute latency at a marginal energy overhead. These findings establish magnitude pruning with recovery training as the most effective single-step compression strategy for low-power x86 edge platforms.

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