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All Journal INTECOMS: Journal of Information Technology and Computer Science Jurnal Minfo Polgan (JMP) Al Tahdzib: Jurnal Pendidikan Islam Anak Usia Dini Integrated System and Management Technology Software Engineering in Computing Systems Big Data Analytics and Data Science Cyber Security and Network Management
Irlon Irlon
Institut Teknologi Budi Utomo
Computer Science & IT Education Library & Information Science Mathematics Social Sciences

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Strategic Approaches to Managing Tantrum Behavior in Early Childhood Education Institutions Nor Aishah Binti Adnan; Mangihut Siregar; Irlon Irlon; Muhammad Arib Sulaiman; Rizky Hasibuan; Riyadlotus Sholichah Riyadlotus Sholichah
Al Tahdzib: Jurnal Pendidikan Islam Anak Usia Dini Vol. 5 No. 1 (2026): Al Tahdzib: Jurnal Pendidikan Islam Anak Usia Dini
Publisher : STAI Publisistik Thawalib Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54150/altahdzib.v5i1.807

Abstract

There has been no comprehensive synthesis of strategies specifically designed for managing tantrum behaviour in early childhood within Early Childhood Education (ECE) settings. This study aims to analyse and explain various approaches used to handle tantrum behaviour in early childhood as implemented in ECE institutions. The research employs a Systematic Literature Review based on the Scopus database covering the period from 2016 to 2026, involving stages such as searching, selection, quality assessment, thematic analysis, synthesis of findings, and data interpretation related to tantrum management strategies in ECE. Results indicate that different tantrum management techniques in early childhood education settings are effective in reducing tantrum occurrences and enhancing children’s emotional regulation. Collaborative play activities contribute to improving social skills and cooperation among children. Mental health screening programmes support early detection of emotional and behavioural disorders. Social and Emotional Learning (SEL) programmes have been demonstrated to improve self-control, emotional management skills, and classroom engagement. Additionally, parent education, audio-visual affirmation therapy, and the use of wearable technology can significantly decrease the frequency and duration of tantrums in early childhood. Conclusion: Preventive, educational, collaborative, and technology-driven strategies are effective for reducing tantrum behaviour and fostering children’s socio-emotional development. Research significance: This study provides scientific references for teachers and researchers to design effective tantrum management strategies in early childhood education.
EVALUASI STRATEGI MOVING AVERAGE, RELATIVE STRENGTH INDEX, DAN PARABOLIC SAR TERHADAP PERGERAKAN HARGA EUR/USD PADA PT ROYAL TRUST FUTURES Abdurrahman Abdurrahman; Sigit Wibisono; Bagus Prabowo; Aji Nurrohman; Irlon Irlon
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/nmen2h79

Abstract

Perdagangan valuta asing (forex) merupakan salah satu instrumen investasi yang memiliki risiko tinggi dan memerlukan analisis yang tepat dalam pengambilan keputusan. Salah satu pendekatan yang banyak digunakan adalah analisis teknikal dengan bantuan indikator teknikal. Penelitian ini bertujuan untuk mengevaluasi kinerja tiga indikator teknikal, yaitu Moving Average periode 5 (MA5), Relative Strength Index (RSI), dan Parabolic SAR dalam memberikan sinyal beli dan jual terhadap pasangan mata uang EUR/USD. Permasalahan dalam penelitian ini  adalah untuk mengetahui sejauh mana efektivitas masing-masing indikator dalam membaca pergerakan harga dan menghasilkan profit yang optimal. Data yang digunakan adalah data historis EUR/USD periode 2018–2025 yang diperoleh dari platform MetaTrader 4, dengan pendekatan metode CRISP-DM dan pengolahan data menggunakan bahasa pemrograman Python. Hasil evaluasi menunjukkan bahwa Parabolic SAR merupakan indikator paling unggul dengan win rate 76.84%, net return sebesar 65.43%, dan CAGR sebesar 7.46%. MA5 menunjukkan hasil moderat dengan win rate 36.55% dan net return 6.15%, sedangkan RSI menunjukkan performa terendah dengan hasil negatif. Penelitian ini memberikan gambaran mengenai efektivitas masing-masing indikator teknikal dan dapat menjadi referensi untuk pengambilan keputusan trading yang lebih tepat. Kata Kunci: Evaluasi indikator teknikal, MA5, RSI, Parabolic SAR, pergerakan harga EUR/USD
Executive Decision Support System Implementation Strategies Based on Big Data Analytics to Improve Operational Efficiency and Corporate Governance in Global Digital Enterprises Asro Asro; Solihin Solihin; Irlon Irlon
Integrated System and Management Technology Vol. 1 No. 1 (2026): January: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i1.13

Abstract

This study explores the transformative role of big data-driven Decision Support Systems (DSS) in global digital enterprises, particularly focusing on their impact on operational efficiency and corporate governance. By leveraging big data analytics, DSS offer organizations the tools to process vast amounts of real-time data, enabling executives to make more informed decisions that optimize resources, improve productivity, and reduce operational costs. The research highlights the integration of predictive analytics, machine learning, and real-time data processing within DSS, which allows businesses to gain strategic insights and anticipate market trends. Furthermore, the study emphasizes the significant role of DSS in enhancing corporate governance, improving transparency, accountability, and compliance with regulations. These systems foster better decision-making processes, which contribute to building trust among stakeholders and ensuring long-term organizational success. However, the study also identifies several challenges in implementing big data-driven DSS, including data management complexities, technological integration difficulties, and the need for skilled personnel. Despite these challenges, the findings demonstrate that big data-driven DSS are pivotal in driving competitive advantage, operational optimization, and governance improvements. The research concludes with actionable recommendations for executives to adopt and implement big data-driven DSS, emphasizing the importance of continuous support, training, and system integration. The study also suggests future research directions, including exploring the integration of emerging technologies like AI and IoT into DSS and assessing their long-term impact on sustainability and corporate governance.
Adaptive DevOps Practices for Enhancing Reliability and Performance of Embedded Computing Platforms in Safety Critical Industrial Applications Deasy Widyastomo; Yosef Lefaan; Irlon Irlon
Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i1.49

Abstract

This study investigates the adoption of adaptive DevOps practices in embedded systems used in safety-critical industrial applications. Traditional DevOps models, which are primarily designed for cloud-based systems, face significant challenges when applied to embedded platforms due to hardware constraints, real-time performance requirements, and stringent safety standards. The research focuses on developing a tailored DevOps framework that integrates continuous integration/continuous delivery (CI or CD) pipelines, automation, real-time monitoring, and safety assurance processes to enhance system reliability, performance, and compliance with regulatory standards. The study uses a case study methodology, involving embedded system teams across multiple industrial sectors, to assess the impact of these adapted DevOps practices on system stability and operational efficiency. Key findings show that the adoption of adaptive DevOps practices led to significant improvements in system reliability, performance, and deployment stability. Continuous feedback mechanisms allowed for early issue detection and faster resolution, leading to enhanced system uptime and responsiveness. Additionally, the integration of safety assurance into the DevOps pipeline ensured that safety-critical systems complied with required safety integrity levels and certification standards. The study further explores the integration of DevOps with embedded safety-critical systems, highlighting the benefits of cross-domain collaboration, enhanced communication, and the ability to address the unique challenges of these platforms. The research also underscores the limitations of conventional DevOps models in embedded systems and presents practical implications for the wider adoption of DevOps in safety-critical industrial applications. Future research is recommended to refine DevOps frameworks for embedded systems, integrating emerging technologies like the Industrial Internet of Things (IIoT) and Digital Twins to further optimize performance, security, and predictive maintenance.
Optimizing End to end Machine Learning Pipelines Using Hybrid Edge Cloud Architectures for Real Time Decision making Applications Asro Asro; Solihin Solihin; Irlon Irlon
Big Data Analytics and Data Science Vol. 1 No. 1 (2026): March: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i1.22

Abstract

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.
Digital Forensics and Automated Incident Response Framework Leveraging Big Data Analytics and Real Time Network Traffic Profiling in Heterogeneous Cyber Environments Danang Danang; Zaenal Mustofa; Irlon Irlon
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.15

Abstract

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.
Implementasi Machine Learning untuk Deteksi Intrusi pada Jaringan Komputer Dyan Prawita Sari; Zuhri Halim; Irlon Irlon; Bayu Waseso; Saromah Saromah
Jurnal Minfo Polgan Vol. 13 No. 2 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i2.14074

Abstract

Dalam era digital yang semakin berkembang, keamanan jaringan komputer menjadi isu yang sangat penting, terutama dengan meningkatnya ancaman dari serangan siber. Salah satu metode yang efektif dalam mendeteksi ancaman tersebut adalah melalui implementasi machine learning. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi model machine learning yang mampu mendeteksi intrusi pada jaringan komputer secara real-time. Model yang diusulkan menggunakan teknik supervised learning, di mana dataset yang berisi lalu lintas jaringan normal dan lalu lintas yang mengandung serangan digunakan untuk melatih algoritma. Algoritma yang dipertimbangkan meliputi Decision Tree, Random Forest, dan Support Vector Machine (SVM). Penelitian ini juga melakukan analisis komparatif untuk menilai kinerja masing-masing algoritma dalam hal akurasi, presisi, recall, dan waktu pemrosesan. Hasil eksperimen menunjukkan bahwa model machine learning yang diterapkan mampu mendeteksi berbagai jenis serangan dengan tingkat akurasi yang tinggi, mencapai lebih dari 95% pada dataset uji. Selain itu, Random Forest terbukti menjadi algoritma yang paling efektif dalam mendeteksi intrusi dengan keseimbangan terbaik antara akurasi dan waktu pemrosesan. Implementasi sistem ini diharapkan dapat meningkatkan kemampuan deteksi intrusi pada jaringan komputer, sehingga membantu dalam menjaga keamanan data dan mengurangi potensi kerugian akibat serangan siber.
Co-Authors Abdurrahman Abdurrahman Aji Nurrohman Asro Asro Bagus Prabowo Bayu Waseso Danang, Danang Deasy Widyastomo Dyan Prawita Sari Mangihut Siregar Muhammad Arib Sulaiman Nor Aishah Binti Adnan Riyadlotus Sholichah Riyadlotus Sholichah Rizky Hasibuan Saromah Saromah Sigit Wibisono Solihin Solihin Yosef Lefaan Zaenal Mustofa Zuhri Halim