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All Journal Jurnal Informatika Dan Tekonologi Komputer Proceeding of The International Conference on Electrical Engineering and Informatics Integrated System and Management Technology Software Engineering in Computing Systems
Safrizal Safrizal
Universitas Pembangunan Jaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

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Sistem Rekomendasi Personalisasi Pembelajaran Mahasiswa untuk Prediksi Karir dan Sertifikasi Kompetensi yang Tepat Safrizal Safrizal; Chaerul Anwar; Augury El Rayeb; Yohana Citra Simamora; Acce Venio Hasugian; Javier Alvino Alfian
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 2 (2025): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i2.5514

Abstract

In the era of digital and globalization, the need for graduates who have competencies in accordance with industry demands is becoming increasingly important. Students often face difficulties in determining the right direction of learning, both for career development and achieving competency certification. This study aims to develop a personalized recommendation system for student learning that is able to predict appropriate career paths and recommend relevant certifications. This system utilizes a data-driven approach using data mining and machine learning techniques, by processing academic data, interests, expertise, and current industry trends. The recommendation system algorithm used includes a content-based and collaborative approach, which are combined to produce more accurate and adaptive results. This system is designed to provide learning suggestions in the form of courses, additional training, and external certifications that support students' career goals. Initial test results show that the system is able to improve students' understanding of their potential and career prospects. Thus, this system is expected to be an innovative solution in supporting the personalization of future-oriented higher education.
An Artificial Intelligence Based Recommendation Model for Personalizing Students' Learning Interest Paths at Universities Safrizal Safrizal; Chaerul Anwar; Augury El Rayeb
Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/iceei.v1i2.28

Abstract

This study explores the integration of artificial intelligence (AI) in education, particularly in supporting personalized learning. AI presents new opportunities through adaptive learning platforms, virtual tutors, and intelligent assessment systems that have the potential to revolutionize teaching and learning methods. By conducting in-depth data analysis, AI can identify student performance patterns and provide tailored recommendations, enabling educators to deliver more targeted interventions. Furthermore, personalized learning plays a crucial role in enhancing student motivation and engagement by customizing learning experiences to meet individual needs and learning styles. This study aims to implement personalized learning strategies in educational settings and offers insights into best practices for their integration. It also examines their impact on student engagement and academic achievement. The findings highlight the importance of personalized learning in fostering an inclusive and effective educational environment. By leveraging AI, educators can optimize learning, empower students, and address achievement gaps. This study provides practical recommendations for educators and policymakers to implement AI-based learning strategies effectively.
An Integrated Framework of Enterprise Architecture and Artificial Intelligence for Optimizing Strategic Decision Making in Digital Service Oriented Organizations Imeldawaty Gultom; Dedi Candro Parulian Sinaga; Safrizal Safrizal
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.v1i1.5

Abstract

This research explores the integration of Enterprise Architecture (EA) and Artificial Intelligence (AI) to optimize strategic decision-making in digital service-oriented organizations. These organizations often face challenges such as fragmented decision-making due to disconnected IT systems and limited data-driven insights. The objective of the study is to develop an integrated framework that combines EA and AI to enhance decision-making accuracy, operational efficiency, and strategic alignment. The study employs design science research methodology, involving the development of the framework, expert validation, and testing in simulated organizational scenarios. The findings reveal that the integrated framework improves decision-making by providing real-time, data-driven insights, predictive analytics, and better alignment with organizational goals. AI's role in analyzing large datasets and generating actionable insights allows decision-makers to anticipate future trends and make more informed decisions. The framework significantly outperforms traditional EA approaches, particularly in terms of predictive decision support and adaptive intelligence. The study concludes that the integration of EA and AI provides a robust solution for organizations looking to improve strategic decision-making, enhance operational efficiency, and stay competitive in dynamic business environments.
Framework for Integrating Continuous Integration and Continuous Deployment (CI or CD) with Automated Security Testing to Improve Software Dependability Syaiful Anwar; Irwanto Irwanto; Safrizal Safrizal
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.v1i1.47

Abstract

The increasing demand for rapid software delivery has led to the widespread adoption of Continuous Integration (CI) and Continuous Deployment (CD) pipelines. These pipelines automate the processes of code integration, testing, and deployment, significantly improving the speed and reliability of software development. However, traditional CI or CD pipelines often overlook security testing, leading to vulnerabilities in the deployed software. To address this gap, this study proposes an integrated framework that embeds automated security testing within the CI or CD process. The framework incorporates security testing tools such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Vulnerability Assessment and Penetration Testing (VAPT) to ensure continuous security checks throughout the development lifecycle. The experimental results show that the proposed framework enhances early vulnerability detection, with detection rates increasing from 30% to 70%. Additionally, the framework reduces deployment failures from 50% to 20%, demonstrating its effectiveness in improving software dependability. While the integration of automated security testing adds a slight 5% increase in pipeline execution time, this minimal impact does not significantly affect the overall speed of the pipeline. The proposed approach successfully balances security and efficiency, ensuring that software is both secure and delivered at high speed. This research highlights the importance of integrating security into CI or CD pipelines and demonstrates that it is possible to achieve high security without sacrificing the speed of software development. The study also discusses the practical implications for software development teams and suggests areas for future research, including the integration of advanced AI-driven security testing tools and the expansion of the framework's applicability across different software projects.
Co-Authors Acce Venio Hasugian Augury El Rayeb Chaerul Anwar Dedi Candro Parulian Sinaga Imeldawaty Gultom Irwanto Irwanto Javier Alvino Alfian Syaiful Anwar Yohana Citra Simamora