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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 
Security Analysis of XYZ Website Using OWASP Zap Tools Muhammad Amirul Mu'min; Yana Safitri; Galih Pramuja Inngam Fanani; Setiawan Ardi Wijaya; Novi Tristanti
Journix: Journal of Informatics and Computing Vol. 1 No. 1 (2025): April
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i1.1

Abstract

In the growing digital era, website security is a critical aspect that must be considered. Vulnerabilities such as Cross-Site Scripting (XSS), Clickjacking, and Man-in-the-Middle can pose serious risks to data integrity and security. Therefore, effective tools are needed to identify and evaluate such vulnerabilities to prevent costly exploitation. This research aims to analyze security vulnerabilities on the website using OWASP ZAP (Zed Attack Proxy) as a penetration testing tool, and provide mitigation recommendations to improve system security. The method used is penetration testing by utilizing OWASP ZAP to identify security vulnerabilities on the website. The research stages include testing, analyzing the results, and preparing mitigation recommendations based on the findings of vulnerabilities such as A01, A03, and A04. The results showed that OWASP ZAP successfully identified various vulnerabilities, including XSS, Clickjacking, and Man-in-the-Middle. Recommended mitigation measures include configuring security headers and protecting sensitive data to prevent exploitation. OWASP ZAP proved to be effective in detecting and evaluating security vulnerabilities on websites. In addition, the tool also raises awareness of the importance of strong security policies. With the implementation of mitigation recommendations, website owners can better protect sensitive data, maintain user trust, and stay safe in an increasingly complex digital environment.
Improving the Accuracy of Social Media Sentiment Classification with the Combination of TF-IDF Method and Random Forest Algorithm Siti Mutmainah; Fathir; Erin Eka Citra
Journix: Journal of Informatics and Computing Vol. 1 No. 1 (2025): April
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i1.2

Abstract

Sentiment classification on social media text data is one of the main challenges in public opinion analysis. The large volume of data and the diversity of informal languages make sentiment analysis a challenge in itself, especially in the context of Indonesian. This research aims to improve the accuracy of social media sentiment classification by combining Term Frequency-Inverse Document Frequency (TF-IDF) method as a text representation technique and Random Forest algorithm as a classification model. The dataset used consists of 20,000 Indonesian opinion data collected from Twitter and Instagram, and has been labeled into three sentiment categories: positive, negative, and neutral. This data went through a preprocessing stage, including text cleaning, tokenization, stopword removal, stemming, and normalization. Experimental results show that the combination of TF-IDF and Random Forest yields an accuracy of 91.2% with average precision, recall, and F1-score values above 0.90. The confusion matrix analysis revealed that the model was highly effective in classifying positive and negative sentiments, although there were challenges in distinguishing neutral sentiments. These findings indicate that the approach used is quite reliable and can be used as a foundation for the development of sentiment analysis systems on an industrial scale as well as further research.
Data-driven MSME Success Prediction Using Decision Tree-Based Machine Learning Techniques Sahrul Ramadhan; Zumhur Alamin; Miftahul Jannah; Muhammad Akbar; Rizki Fikriyansah
Journix: Journal of Informatics and Computing Vol. 1 No. 1 (2025): April
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i1.3

Abstract

MSMEs play an important role in Indonesia's digital economy, but not all businesses are able to survive and thrive sustainably. The low predictive ability of MSME success is a major challenge in formulating effective policies and interventions. This research aims to build a prediction model for the success of MSMEs by utilizing machine learning algorithms as a strategic decision-making tool. The approach used is an experimental method by comparing the performance of three popular algorithms: Decision Tree, Random Forest, and Support Vector Machine (SVM). The dataset used comes from a combination of survey and open source data, which includes variables of MSME characteristics. The data was analyzed through preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest algorithm performed best with an optimal classification balance. This finding indicates that the machine learning approach is effective in identifying MSME success patterns based on historical data. The main contribution of this research is the development of an artificial intelligence-based decision support system that can be adapted for the local context to support the sustainable growth of digital MSMEs.
Evolving DevOps Practices in Modern Software Engineering: Trends, Challenges, and Impacts on Quality and Delivery Performance Zumhur Alamin; Dahlan; Khaeruddin; Sahrul Ramadhan
Journix: Journal of Informatics and Computing Vol. 1 No. 1 (2025): April
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i1.4

Abstract

The adoption of DevOps has significantly reshaped modern software engineering by tightly integrating development and operations activities to achieve faster, more reliable, and scalable software delivery. Despite widespread industry adoption, empirical research on the quantifiable impact of DevOps practices remains fragmented. This study aims to investigate how contemporary DevOps practices influence software quality, delivery speed, and team productivity in real-world environments. A mixed-method approach was employed, combining a multi-case study across four mid-to-large enterprises with a controlled simulation of key DevOps performance metrics. Data were collected from interviews, DevOps telemetry tools (e.g., Jenkins, GitLab CI/CD, Docker, and Kubernetes), and automated system logs. The findings indicate that mature DevOps adoption is associated with a 45% increase in deployment frequency, a 38% reduction in lead time for changes, and a 32% decrease in change failure rates. Nonetheless, organizations face persistent challenges in integrating security (DevSecOps), managing technical debt, and fostering cultural alignment across siloed teams. The results confirm the strategic importance of Infrastructure as Code (IaC), continuous monitoring, and automated testing in optimizing DevOps outcomes. For practitioners, this study offers a practical roadmap for scalable and sustainable DevOps transformation. For researchers, it identifies critical gaps in socio-technical integration and intelligent automation that warrant further investigation. This research contributes to bridging empirical evidence and theoretical frameworks in the evolving field of DevOps in software engineering.
RLBSA-based Academic Information System Optimization for Student Performance Prediction Dahlan; Miftahul Jannah; Dilla Puspita Mentia; Nurul Aulia Safitri
Journix: Journal of Informatics and Computing Vol. 1 No. 1 (2025): April
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i1.5

Abstract

Academic information systems play an important role in student data management and data-driven decision-making. However, traditional analysis methods such as Decision Tree (DT) and Support Vector Machine (SVM) often suffer from limitations in prediction accuracy and processing efficiency. This research develops an Academic Information System based on Random Leapfrog Band Selection Algorithm (RLBSA) to improve student performance prediction accuracy and academic data processing efficiency. The system adopts Google Firestore (NoSQL) architecture based on cloud computing, which enables large-scale data management with low latency and high scalability. Experimental results show that the RLBSA-based model achieves a prediction accuracy of 94.3%, higher than that of SVM (89.7%) and DT (87.4%). In terms of efficiency, the RLBSA-based system reduces data processing time by 40% compared to traditional methods, making it faster in handling large-scale academic datasets. In addition, scalability testing shows that the system is capable of handling up to 1,500 simultaneous users with an average latency below 250 milliseconds, proving its superiority in cloud-based academic environments. This research contributes to the development of data-driven academic evaluation systems, algorithm optimization in student performance analysis, as well as the application of cloud technology in academic information systems. The implications of this research open up opportunities for further integration with deep learning and reinforcement learning to improve accuracy and efficiency in academic decision making.
Real-Time Phishing Detection Using Google Safe Browsing API and Machine Learning Zumhur Alamin; Ritzkal
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.v1i2.8

Abstract

Phishing remains one of the fastest-evolving cybersecurity threats, where attackers mimic legitimate websites to obtain sensitive user information. This study presents a real-time evaluation of a phishing detection system integrating the Google Safe Browsing API with ensemble machine learning models. The research aims to enhance detection accuracy and responsiveness against emerging phishing websites by combining real-time threat intelligence with automated URL analysis. The dataset used comprises over 20,000 URLs collected from Google Safe Browsing, PhishTank, and OpenPhish between June and December 2024. Four approaches were evaluated: (1) machine learning models without API, (2) API-only detection, (3) machine learning with API as an additional feature, and (4) machine learning with API as a validator. The best performance was achieved by the API-as-validator model, reaching 98.2% accuracy, reducing false positives to 2.1%, and lowering false negatives to 3.2%, with an average latency of 108 ms. These findings demonstrate that integrating real-time threat feeds significantly enhances adaptability and reliability in phishing detection. Future research will focus on latency optimization and federated learning to enable large-scale collaborative detection systems.
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.
Development of Management Information System Based on MVC Architecture to Improve Business Process Efficiency Miftahul Jannah; Dahlan; Chairul Akbar
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.v1i2.11

Abstract

With the increasing need for flexible, scalable, and maintainable information systems, the Model-View-Controller (MVC) architecture has become a popular approach in software development. This research aims to design and implement an MVC-based management information system to improve data management and business process efficiency. The study adopts the Agile software development method with the Scrum framework, enabling iterative progress and rapid adaptation to user needs. The implementation employs the Laravel framework to ensure modular separation between business logic, user interface, and control flow. The evaluation is carried out using black-box testing and performance testing with Apache JMeter. The results show that the MVC-based system reduces processing time by 30% compared to a monolithic system and enhances system scalability and maintainability. This study concludes that MVC architecture provides significant improvements in system efficiency, modularity, and sustainability. Future work may focus on integrating microservices and cloud computing to further enhance scalability and performance.
Implementation of Post-Quantum Cryptography Algorithms for Financial Applications in Indonesia Sutriawan; Enggar Novianto
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.v1i2.13

Abstract

The development of quantum computing poses a serious threat to classical cryptographic algorithms that have been used to protect digital data and financial transactions. Algorithms such as RSA and Elliptic Curve Cryptography (ECC) are vulnerable to quantum computer attacks capable of running Shor's algorithm to efficiently solve large number factorization problems. This study aims to explore and analyze the implementation of Post-Quantum Cryptography (PQC) algorithms, specifically Falcon and Dilithium, in the context of digital financial systems in Indonesia. The research approach was conducted through literature studies and case study analysis on the Algorand platform, which has adopted the Falcon algorithm to strengthen digital signature security. The results of the study show that the integration of PQC algorithms can be done without sacrificing system efficiency, while providing a significant increase in security resilience against quantum threats. This research is expected to serve as a reference for financial institutions and national regulators in formulating transition strategies towards a secure digital security infrastructure in the quantum era.

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