Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Media Computer Science
Hanif Nurkhalis
Universitas Negeri Yogyakarta
Computer Science & IT

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

Found 2 Documents
Search

 1 
A Systematic Review of Educational Facial Emotion Recognition: Datasets, Methods, Modality, and Potential Transfer to Vocational Teaching Contexts Pipit Utami; Masduki Zakarijah; Satrio Wiroyudho Pratomo; Mozan Osman Gebalr; Dwi Osman Indriyani; Bismaka Sahasika; Hanif Nurkhalis; Tomy Herlambang
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.10588

Abstract

Facial emotion recognition (FER) has emerged as a promising component of educational technology, yet its integration into pedagogical practice remains uneven, particularly in vocational learning contexts. This systematic review examines 38 empirical studies published between 2015 and 2025, identified through a PRISMA-guided search of major academic databases, including Scopus. The synthesis explores how datasets, model architectures, multimodal learning signals, and system design shape the applicability of affect analytics in authentic instructional settings. The findings indicate that progress in educational FER is largely driven by benchmark datasets and incremental architectural refinement evaluated under controlled conditions, which limits transferability to hands-on learning environments. While lightweight, attention-enhanced models and multimodal approaches improve deployment feasibility and affective interpretation, most systems remain open-loop and rarely support sustained pedagogical adaptation. Overall, the review highlights that advancing educational FER requires closer alignment between data practices, model and modality design, and the pedagogical realities of vocational education.
An Integrated IoT–AI Architecture for Precision Beekeeping: Sensing, Data Communication, Colony-State Intelligence, and Decision-Oriented Actions Pipit Utami; Mashoedah Mashoedah; Hanif Nurkhalis; Muhammad Akhdan Nafi'; Wulan Savitri; Widya Prastowo; Diah Wulan Safitri; Fajar Dwi Saputra
Jurnal Media Computer Science Vol 3 No 2 (2024): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v3i2.10589

Abstract

Precision beekeeping increasingly adopts Internet of Things (IoT) and artificial intelligence (AI) technologies, yet most existing systems remain monitoring-centric. This study synthesizes the architectural characteristics of IoT–AI precision beekeeping systems and identifies integration gaps that constrain decision-oriented operation. A systematic literature review of 50 Scopus-indexed studies published between 2015 and 2024 was conducted using a PRISMA-based selection process and an architecture-oriented synthesis across sensing, communication, intelligence, and decision layers. The results reveal a strong emphasis on sensing and data acquisition, while analytical outputs are weakly linked to operational decision-making, preventing most systems from closing the loop from inference to action. These findings suggest that the main limitation is architectural rather than technological. Accordingly, this study positions a reference architecture as an analytical framework for end-to-end smart beehive systems, with implications for more integrated and practical applications in small- and medium-scale beekeeping operations.
Co-Authors Bismaka Sahasika Diah Wulan Safitri Dwi Osman Indriyani Fajar Dwi Saputra Masduki Zakarijah Mashoedah Mashoedah Mozan Osman Gebalr Muhammad Akhdan Nafi' Pipit Utami Satrio Wiroyudho Pratomo Tomy Herlambang Widya Prastowo Wulan Savitri