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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 16 Documents
 1   2 
Genetic Algorithm Optimization for Solving the Traveling Salesman Problem in the Indonesian Business Environment Siti Mutmainah; Teguh Ansyor Lorosae; Erin Eka Citra
Journix: Journal of Informatics and Computing Vol. 1 No. 2 (2025): August
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i2.14

Abstract

The Traveling Salesman Problem (TSP) is one of the combinatorial optimization problems that is highly relevant in distribution and logistics route planning. This study aims to optimize the Genetic Algorithm (GA) for solving TSP in the Indonesian business environment, which has complex geographical characteristics and diverse logistics infrastructure. The proposed approach combines dynamic parameter adaptation and regional clustering to improve convergence efficiency and solution quality. Experiments were conducted on the distribution route data of an Indonesian logistics company with three scenarios: conventional GA, adaptive GA, and clustering-based GA. Performance evaluation was based on total travel distance, computation time, solution stability, and convergence rate. The results show that adaptive AG produces the best performance, with a reduction in total travel distance of up to 20% more efficient, faster convergence time (95 iterations compared to 120 iterations in conventional AG), and solution stability reaching 90.6%. These findings indicate that parameter adaptation in AG can significantly improve the effectiveness of TSP optimization in the Indonesian business context. The contribution of this research not only strengthens the development of adaptive metaheuristic algorithms but also provides practical benefits for the logistics industry in designing more efficient, cost-effective, and sustainable distribution routes.
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.
Implementation of Cloud-Based Information Systems for the Optimization of Higher Education Administration Services Khaeruddin; Mohammad Nur Cholis; Arisandi
Journix: Journal of Informatics and Computing Vol. 1 No. 2 (2025): August
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i2.16

Abstract

Digital transformation in the higher education sector requires increased efficiency, flexibility, and quality in the delivery of administrative services. Conventional on-premise information systems often face obstacles in terms of scalability, accessibility, and infrastructure maintenance, thereby hampering the effectiveness of academic and financial services. This study aims to design and implement a cloud computing-based information system to optimize higher education administrative services. The research method uses a software engineering-based case study approach with an iterative System Development Life Cycle (SDLC) development model, covering the stages of requirements analysis, system design, implementation, and evaluation. The system was built with a Software as a Service (SaaS) architecture using the Google Cloud platform and tested on two main service units, namely academic and financial. The test results showed an increase in administrative process time efficiency of up to 61.75%, a system uptime rate of 99.8%, and an average user satisfaction rate of 4.51 on a scale of 5. These findings indicate that the application of cloud computing can significantly improve operational efficiency, data security, and user satisfaction, while supporting the digital transformation of higher education institutions towards adaptive, integrated, and sustainable administrative governance.
An IoT-Based Soil Moisture Monitoring Prototype with Automated Notifications for Drought Risk Indication Muhammad Amirul Mu'min; Dahlan
Journix: Journal of Informatics and Computing Vol. 1 No. 2 (2025): August
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i2.17

Abstract

Soil moisture monitoring is a critical aspect of sustaining crop cultivation, particularly in environments that are vulnerable to climate variability and fluctuations in soil water content. The limitations of manual monitoring methods, which are often inefficient and time-consuming, highlight the need for automated systems capable of delivering timely and accurate information. This study aims to develop and evaluate an IoT based soil moisture monitoring system equipped with automatic notification capabilities for users. The proposed system employs an ESP32 microcontroller as the main processing unit, soil moisture sensors for data acquisition, MQTT as the data communication protocol, and a Telegram Bot as the notification service. The research methodology includes system architecture design, sensor data processing, internet network integration, and the implementation of automated notification services. Experimental testing was conducted to assess the consistency of sensor readings, the reliability of data transmission, and the system’s responsiveness to changes in soil moisture levels. The results demonstrate that the system is able to deliver real-time soil condition information with good stability and to respond to critical decreases in soil moisture through automatic notifications within a relatively short response time. These findings indicate that an IoT-based approach can enhance the efficiency of environmental monitoring and support informed decision-making in crop management. The developed prototype shows potential for application in precision agriculture as well as small- to medium-scale environmental monitoring scenarios.
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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