Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Artificial Intelligence (ITALIC)
Muhamad Yusup
Satya Wacana Christian University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
The Implementation of Digital Storytelling to Enhance Early Childhood Learning Experiences Marviola Hardini; Muhamad Yusup; Yasir Mustafa Kareem; Eka Dian Astuti
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 2 (2026): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i2.1014

Abstract

This study examines how digital storytelling can enhance early childhood learning experiences in preschool settings, in response to the growing integration of technology in education and the need to ensure its alignment with develop-mentally appropriate practices. The objective of this research is to explore how the implementation of digital storytelling influences children’s cognitive engagement, social interaction, and creative expression. Employing a qualitative de- scriptive approach, the study involves 18 children aged 4-6 years in a preschool setting and utilizes classroom observations, structured interviews with teachers, and documentation of learning activities to analyze the implementation process and its pedagogical impact. The findings indicate a tendency toward improved attention span, vocabulary development, and collaborative behavior, as well as enhanced imagination and emotional expression during digital storytelling ses- sions. Teachers reported that digital tools contributed to more interactive and adaptive learning environments, enabling children to connect personal experi- ences with narrative content in meaningful ways. The study concludes that integrating digital storytelling in early childhood education holds strong poten- tial as a developmentally aligned instructional strategy, supporting both child independence and holistic development. It further highlights the importance of thoughtful technological integration in preschool classrooms as a bridge be- tween modern educational innovation and child-centered learning philosophies.
Hybrid Fuzzy Logic Models for Performance Evaluation in Complex Decision-Making Systems Cicilia Sriliasta Bangun; Padeli Padeli; Muhamad Yusup; Adele Valerry
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1095

Abstract

Complex decision-making systems increasingly face uncertainty, nonlinearity, incomplete information, and dynamic data streams, making conventional rule-based and statistical approaches less reliable for adaptive and consistent decision support. Fuzzy logic offers interpretability for imprecise reasoning, whereas machine learning contributes predictive strength and optimization capability. This study develops and evaluates fuzzy logic-based hybrid models that integrate fuzzy inference systems with neural learning and evolutionary optimization. Benchmark datasets and simulation-based case studies were used to test model performance under uncertain and nonlinear conditions. The models were assessed using prediction accuracy, decision consistency, computational efficiency, error reduction, scalability, and adaptability, followed by comparison with conventional fuzzy, statistical, and standalone machine learning models. The main objective is to evaluate the effectiveness, reliability, scalability, and adaptability of hybrid fuzzy models for complex decision-making systems. The findings show that the proposed hybrid fuzzy models outperform conventional single model approaches across different scenarios. The models improve prediction precision, stabilize decision outputs under uncertainty, reduce error rates, and enhance adaptability to nonlinear data patterns. Neural learning strengthens predictive capability, while evolutionary optimization improves rule refinement, parameter tuning, and adaptive decision processing. This study concludes that fuzzy logic-based hybrid models provide a robust, interpretable, and scalable framework for intelligent decision support in uncertain and dynamic environments. The findings support the development of adaptive hybrid artificial intelligence systems for healthcare, energy management, smart cities, finance, and industrial automation. This structure also promotes transparent reasoning, reproducible evaluation, and practical deployment in high-stakes environments requiring explainability and resilience simultaneously.
Co-Authors Adele Valerry Bangun, Cicilia Sriliasta Eka Dian Astuti Marviola Hardini Padeli Padeli Yasir Mustafa Kareem