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Journix: Journal of Informatics and Computing
Published by Yayasan Ran Edu Center
ISSN : -     EISSN : 30906784     DOI : https://doi.org/10.63866/journix
Core Subject : Science,
Computer Science & IT
Journix: Journal of Informatics and Computing is a peer-reviewed scientific journal published by Ran Edu Center, dedicated to disseminating high-quality research and studies in the fields of informatics and computing. This journal serves as a platform for researchers, practitioners, and academicians to publish innovative and impactful contributions in various domains, including but not limited to: Artificial Intelligence (AI) and its applications, Data processing and analytics for decision-making, Information systems and software engineering, Cybersecurity and risk mitigation strategies, Computer networks and communication technologies, Emerging trends in information technology and computing innovations. JOURNIX aims to advance knowledge and foster discussions on the design, development, and implementation of cutting-edge technologies that drive advancements in computing and informatics. Submissions are expected to offer strong technical contributions while also considering their implications for users, organizations, and industries. Research methodologies may include empirical studies, experimental evaluations, theoretical analyses, and practical implementations.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 3 (2025): December" : 5 Documents clear
FCM-Guided CNN with Fuzzy Membership Maps for Robust Brain MRI Tumor Classification Firnanda Al-Islama Achyunda Putra; Kukuh Yudhistiro; Sutriawan; Zumhur Alamin
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.9

Abstract

Accurate brain MRI classification is critical for early tumor diagnosis and computer-aided clinical decision support. Conventional convolutional neural networks (CNNs) are effective in learning deep hierarchical features but often struggle with intensity heterogeneity and partial volume effects inherent to MRI data. To address these limitations, this study proposes a hybrid Fuzzy C-Means–CNN (FCM–CNN) framework that integrates unsupervised soft clustering with deep feature learning. The fuzzy segmentation stage preserves boundary uncertainty by generating multi-channel membership maps, which are then fed into a CNN for robust classification. Evaluations conducted on the Kaggle brain MRI dataset (3,264 slices across four diagnostic categories) under Stratified 5-Fold Cross-Validation show consistent improvements over baseline models. The proposed FCM–CNN achieves a mean accuracy of 96.26% and Macro-F1 of 0.9622, surpassing both CNN-only and K-Means+CNN by +4.84% and +2.74% respectively. Ablation analysis confirms that soft memberships enhance discrimination between visually similar tumors, while statistical testing verifies that the gains are systematic and reproducible. Furthermore, the fuzzy membership maps provide interpretable visual cues, aligning with recent trends in explainable AI (XAI) for medical imaging. Overall, the FCM–CNN framework demonstrates that combining fuzzy logic with deep learning yields a balanced trade-off between performance, interpretability, and computational efficiency, making it promising for clinical-grade brain MRI analysis.
A Hybrid Fuzzy–Genetic Algorithm–Neural Network Framework for Robust Short-Term Electricity Load Forecasting in Tropical Power Systems Muhammad Khoirul; Ardin Arisandi
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.10

Abstract

Accurate and robust short-term electricity load forecasting is essential for reliable power system operation, particularly in tropical regions where demand is strongly influenced by nonlinear consumption patterns and weather-induced uncertainty. Conventional statistical models often struggle to capture these characteristics, while standalone neural networks may suffer from training instability and sensitivity to initialization. This study proposes a hybrid soft computing framework that integrates fuzzy logic–based weather uncertainty representation, genetic algorithm–driven optimization, and artificial neural networks (Fuzzy–GA–ANN) for short-term load forecasting. The fuzzy component provides an uncertainty-aware abstraction of meteorological effects, while the genetic algorithm enhances training robustness by mitigating local minima and initialization sensitivity. The framework is evaluated using a large-scale hourly load dataset from the Java–Bali interconnected power system, covering multiple operational horizons (1-hour, 6-hour, and day-ahead). Experimental results demonstrate that the proposed model consistently outperforms classical statistical baselines (ETS and SARIMA) and ANN-based variants across all horizons. The most significant improvements are observed for day-ahead forecasting, where the proposed approach achieves substantially lower forecasting errors and improved training stability. These findings indicate that combining uncertainty-aware feature representation with robust optimization yields reliable and operationally viable forecasting performance in climate-sensitive power systems.
Performance Evaluation of Container-Based Microservices Architecture for Enhancing Scalability and Resource Efficiency in Modern Information Systems Anwar Fattah; Johnathan Robert Moore; Chi Neng Cheng
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.15

Abstract

The adoption of container-based microservices architecture has transformed the way modern information systems are developed and scaled. This study evaluates the performance and scalability improvements gained by implementing Docker and Kubernetes for microservices deployment compared to a traditional monolithic architecture. An experimental approach was conducted using identical system modules tested under workloads ranging from 1,000 to 10,000 concurrent requests. Performance metrics such as throughput, response time, and resource utilization were collected and analyzed. The results show that containerized microservices achieve a 45% increase in throughput and a 28% reduction in average response time, with 18% higher resource efficiency compared to the monolithic system. These findings indicate that container-based microservices significantly enhance scalability, maintainability, and deployment agility in modern information systems. The research provides quantitative evidence supporting the transition from monolithic to microservices architecture and highlights the critical role of container orchestration in enabling dynamic resource management.
Distribution-Aware Evaluation of LAN, WAN-IPsec, and SD-WAN Architectures for Real-Time Enterprise Applications Muhammad Haris Jamaluddin; Dwi Setiawan; Novita Ayuningtyas; Ilham Muamarsyah
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.20

Abstract

Real-time enterprise applications increasingly depend on network architectures that can sustain predictable performance under dynamic traffic conditions. While Software-Defined Wide Area Networks (SD-WAN) are widely adopted to improve flexibility and resilience, their benefits relative to traditional WAN-IPsec overlays and local-area networks (LANs) remain insufficiently characterized from a distributional and experience-oriented perspective. This paper presents a controlled, comparative evaluation of three enterprise network architectures—LAN, WAN with IPsec overlay, and Hybrid SD-WAN—under varying traffic loads. The analysis combines Quality of Service (QoS) measurements, formal statistical validation, Quality of Experience (QoE) modeling, and machine learning–based prediction. Rather than focusing on average performance, the study emphasizes variability and tail behavior of delay and jitter, which are critical for real-time services. Experimental results show that LAN environments provide consistently stable performance across load conditions, whereas WAN-IPsec overlays exhibit pronounced delay and jitter tail expansion under congestion. Hybrid SD-WAN significantly mitigates this variability by reducing dispersion and high-percentile delay, even when average throughput gains are modest. Statistical analysis confirms significant architecture–load interaction effects for delay and jitter, while QoE evaluation demonstrates that stability, rather than throughput, dominates perceived service quality. Furthermore, non-linear machine learning models accurately predict QoE from observable network features, with jitter and packet loss emerging as the most influential predictors. These findings highlight the necessity of distribution-aware evaluation and experience-driven control for designing and operating real-time enterprise networks.
Integration of Fuzzy Logic and Neural Networks for Explainable Early Diagnosis of Rice Plant Diseases Teguh Ansyor Lorosae; Miftahul Jannah; Siti Mutmainah; Fathir; Hilyatul Mustafidah
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.21

Abstract

Early diagnosis of rice leaf diseases remains challenging due to subtle symptom manifestation, uncontrolled illumination, heterogeneous backgrounds, and the limited interpretability of purely data-driven models. This study proposes an explainable hybrid framework integrating a Mamdani Fuzzy Inference System (FIS) with an Artificial Neural Network (ANN) for early rice leaf disease diagnosis under real-field conditions. The framework combines engineered symptom descriptors extracted from segmented leaf regions (GLCM texture and HSV color features), acquisition-time environmental measurements, and a fuzzy-derived disease severity cue to mitigate symptom ambiguity while preserving rule-based interpretability. Experiments were conducted on 8,000 field-acquired rice leaf images collected from multiple locations, covering Healthy, bacterial leaf blight, brown spot, and leaf smut classes. Evaluation followed a leakage-controlled, location-disjoint protocol. Across five independent runs, the proposed FIS–ANN achieved an average accuracy of 91.3 ± 0.6% and a macro-F1 score of 90.8 ± 0.7%, significantly outperforming a feature-based ANN and a fine-tuned ResNet-18 baseline (paired McNemar test, p < 0.05). Per-class analysis shows consistent recall improvements for visually overlapping diseases, and additional evaluation on mild-severity samples confirms maintained sensitivity at early disease stages. Field deployment experiments using smartphone-acquired images from unseen locations further demonstrate robust generalization with low on-device inference latency. These results indicate that integrating fuzzy severity reasoning into a lightweight neural classifier provides a practical balance between performance, interpretability, and computational efficiency, supporting early disease screening and mobile decision-support applications in precision agriculture.

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