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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) AR-RUMMAN: Journal of Education and Learning Evaluation
M Fajar Sahendra Chan
Universitas Negeri Medan
Automotive Engineering Computer Science & IT Control & Systems Engineering Education

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Pemahaman dan Penerapan Nilai-Nilai Pancasila Sebagai Sistem Filsafat Ika Purnamasari; Erika Togito Siahaan; M Fajar Sahendra Chan; Nurhaliza Nurhaliza; Joko Hendratmo; Alvin Efraim Situmorang
AR-RUMMAN: Journal of Education and Learning Evaluation Vol 1, No 2 (2024): Desember 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/arrumman.v1i2.4088

Abstract

Pendidikan Pancasila sebagai sistem filsafat memainkan peran kunci dalam membentuk karakter bangsa Indonesia. Sebagai dasar negara, Pancasila tidak hanya menjadi panduan dalam kehidupan bernegara, tetapi juga menjadi landasan etis dan moral dalam kehidupan bermasyarakat. Pendidikan Pancasila bertujuan untuk menanamkan nilai-nilai Pancasila secara mendalam, sehingga mampu membentuk individu yang memiliki integritas, tanggung jawab, dan semangat kebangsaan yang tinggi. Artikel ini membahas konsep, implementasi, dan tantangan pendidikan Pancasila dalam konteks filsafat, serta dampaknya terhadap pembentukan karakter dan pembangunan bangsa. Melalui pendekatan filosofis, pendidikan Pancasila diharapkan dapat memberikan kontribusi nyata dalam menciptakan masyarakat yang adil, makmur, dan beradab sesuai dengan cita-cita luhur bangsa Indonesia
Literature Review: Transitioning usage from BFS and DFS to Heuristic Search in the Modern AI Era Fahmy Syahputra; Elsa Sabrina; M Fajar Sahendra Chan; Rifki Fali; Muhammad Fattah; Joko Hendratmo; Fadhil Ardiansyah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1856

Abstract

Uninformed search algorithms, specifically Breadth-First Search (BFS) and Depth-First Search (DFS), encounter significant scalability limitations when addressing complex problem spaces in modern Artificial Intelligence (AI) ecosystems. This study investigates the paradigm shift toward intelligent heuristic algorithms through a systematic literature review and comparative analysis of 24 recent academic sources. The evaluation focuses on three primary domains: logical problem solving, robotic navigation, and data infrastructure management. Results demonstrate that heuristic methods, such as A-Star and hybrid variants like PrunedBFS, offer superior time efficiency and memory optimization for autonomous navigation and massive computing tasks. Nevertheless, classic algorithms retain functional relevance for specific scenarios requiring exhaustive exploration. Furthermore, this study reveals that algorithmic evolution has fundamentally transformed digital infrastructure, driving a shift from Search Engine Optimization (SEO) to Answer Engine Optimization (AEO) and necessitating adaptive cybersecurity architectures. The research concludes that the future of AI development relies not on substitution, but on a collaborative synthesis integrating the robustness of classic methods with the adaptability of modern heuristics.
Co-Authors Alvin Efraim Situmorang Elsa Sabrina Erika Togito Siahaan Fadhil Ardiansyah Ika Purnamasari Joko Hendratmo Muhammad Fattah Nurhaliza Nurhaliza Rifki Fali Syahputra, Fahmy