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All Journal Jurnal Pendidikan Vokasi Jurnal Edukasi Elektro JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Pendidikan Teknologi dan Kejuruan ELINVO (Electronics, Informatics, and Vocational Education) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Journal of Research in Social Science and Humanities Jurnal Media Computer Science
Pipit Utami
Universitas Negeri Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Social Sciences Other

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Journal : jurnal media computer science

 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.
IoT-Enabled Dairy Systems: From Sensing and Data Integration to Operational Evaluation Pipit Utami; Masduki Zakarijah; Mashoedah Mashoedah; Zidni Fikriawan; Nabila Yanti; Debora Aritonang; Gregoria Gendhis Pertiwi; Anafrio Rizqy Arba Pratama; Damarjati Azra
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.v3i2.10590

Abstract

The adoption of Internet of Things (IoT) technologies in dairy farming has expanded rapidly, yet the literature remains dominated by isolated technological components rather than operationally integrated systems. This study synthesizes research on IoT-based dairy farming systems through an end-to-end system perspective linking system purposes, sensing, data integration, intelligence, operational outputs, and evaluation. A systematic literature review was conducted on 38 Scopus-indexed articles published between 2020 and 2024 following the PRISMA protocol. The synthesis indicates that IoT applications are primarily oriented toward operational performance and initial quality indicators, barn environmental monitoring, animal health and welfare management, and operational efficiency and resource management. Although sensing technologies are relatively mature at the component level, most systems remain monitoring-oriented and support decision-making mainly through notifications and early warnings, with limited automation and operational evaluation. This review contributes a system-level conceptual framework that highlights the gap between technological capability and operational readiness, guiding the development of more coherent and operationally meaningful IoT applications in dairy farming.
Multimodal Learning in AIoT Systems: Sensor Fusion and Vision-Based Intelligence Agnes Prima Wulanjari; Ria Dymyati; Indar Bismoko Indar Bismoko; Nuryake Fajaryati; Pipit Utami
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.v4i2.11040

Abstract

This study evaluates the effectiveness of multimodal learning in Artificial Intelligence of Things (AIoT) systems, focusing on the integration of sensor fusion and computer vision for classification tasks. A systematic review and meta-analysis were conducted on studies published between 2020 and 2025. Thirteen studies met the inclusion criteria; however, only six provided comparable quantitative data due to inconsistent baseline reporting and evaluation practices. The results indicate that multimodal approaches generally improve accuracy compared to unimodal baselines when comparable evaluations are available, with an average increase of 8.88% (95% CI: 5.33%–12.44%, p < 0.001). High heterogeneity was observed, influenced by domain, sensor configuration, and model architecture. These findings suggest that multimodal effectiveness is conditional and depends on modality complementarity, fusion strategy, and system-level constraints
Co-Authors Aanggraini, Yussi Agnes Prima Wulanjari Anafrio Rizqy Arba Pratama Anggraini, Yussi Ariffin, Arihasnida Arihasnida Ariffin Badraningsih Lastariwati Bekti Wulandari Bismaka Sahasika Bonita Destiana Cikarge, Ghia Pisti Damarjati Azra Debora Aritonang Diah Wulan Safitri Djoko Santoso Djoko Santoso Dwi Osman Indriyani Erik Yumita Sudharta Fajar Dwi Saputra Fajar, Muklas Felestin Felestin, Felestin Gregoria Gendhis Pertiwi Hairuddin Harun Hamzah, Norhasyimah Hanif Nurkhalis I Ketut Suada Indah Soesanti Indar Bismoko Indar Bismoko Irwan Mahazir Ismail Irwan Mahazir Ismail Ismail, Irwan Mahazir Ismail, Mohd Erfy Khairul Azhar Mat Daud Maneetien, Nopadon Masduki Zakarijah Mashoedah Mashoedah Mat Daud, Khairul Azhar Mofu, Alfred Michel Moh. Khairudin Mohd Erfy Ismail Mohd Erfy Ismail Mohd Hasril Amiruddin Mozan Osman Gebalr Muhammad Akhdan Nafi' Muhammad Irfan Luthfi Mustofah, Mustofah Nabila Yanti Nopadon Maneetien Norhasyimah Hamzah Nunung Setiawati Nuryake Fajaryati Pardjono, P. Putra, Abdul Aziz Sidiq Tri Putu Sudira Putu Sudira Putu Sudira Fajaryati Ria Dymyati Rinawan, Rangga Bayu Rudy Hartanto Santi Utami, Santi Sari, Praramadini Saroh, Vetin Yumita Satrio Wiroyudho Pratomo Slamet Slamet Tomy Herlambang Widya Prastowo Wulan Savitri Yoga Sahria Yoga Sahria Yunda Michel Rismawati Zidni Fikriawan