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All Journal Jurnal Teknik Industri Jurnal Keuangan dan Perbankan Trikonomika: Jurnal Ekonomi Indonesian Journal of Science and Technology Jurnal Ilmiah Econosains Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Bisnis dan Manajemen YUME : Journal of Management JURISMA: Jurnal Riset Bisnis & Manajemen Majalah Ilmiah UNIKOM FIRM: Journal of Management Studies Indonesian Community Service and Empowerment Journal (IComSE) Jurnal Ekonomi Bisnis Dan Industri (EBI) International Journal of Research and Applied Technology (INJURATECH) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Journal of Artificial Intelligence and Digital Business Journal Research of Social Science, Economics, and Management Eduvest - Journal of Universal Studies Jurnal Ilmiah Bisnis dan Ekonomi Asia (Jibeka) Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Jurnal Salingka Nagari ASEAN Journal for Science and Engineering in Materials ASEAN Journal of Educational Research and Technology Jurnal Manajemen, Bisnis dan Kewirausahaan Software Engineering in Computing Systems
Eddy Soeryanto Soegoto
Program Studi Manajemen, Fakultas Ekonomi Dan Bisnis, Universitas Komputer Indonesia
Aerospace Engineering Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Neuroscience Social Sciences Other

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Predictive Modelling of Electronic Materials: A Review of Deep Learning Techniques in Computer Engineering Agis Abhi Rafdhi; Hanhan Maulana; Senny Luckyardi; Eddy Soeryanto Soegoto; Dostnazar Ximmataliyev; Goh Kang Wen; Tomáš Chochole; Hewa Majeed Zangana
ASEAN Journal for Science and Engineering in Materials Vol 5, No 3 (2026): AJSEM: Volume 5, Issue 3, December 2026
Publisher : Bumi Publikasi Nusantara

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Abstract

This review evaluates the application of deep learning (DL) for the predictive modeling of electronic materials in computer engineering. We analyzed peer-reviewed literature across four major databases, focusing exclusively on advanced architectures like Graph Neural Networks (GNNs) and Generative models. Results indicate these models accurately predict critical properties, such as band gaps and thermal conductivity, for next-generation semiconductors, 2D materials, and memristors. These high accuracies are achieved because architectures like GNNs effectively capture complex 3D spatial interactions without requiring manual feature engineering. However, practical fabrication remains hindered by data scarcity, algorithmic opacity, and a profound "Sim-to-Real Gap". While DL accelerates predictive design, sustaining Moore's Law ultimately requires developing autonomous "Self-Driving Labs" and Large Material Models to bridge digital predictions with physical synthesis.
Evolution of Artificial Intelligence (AI)-driven Information Systems in Higher Education: A Review Juliana Karin; Dian Dharmayanti; Senny Luckyardi; Eddy Soeryanto Soegoto; Dostnazar Ximmataliyev; Mohd. Kamir Yusof; Tomáš Chochole; Hewa Majeed Zangana
ASEAN Journal of Educational Research and Technology Vol 5, No 3 (2026): AJERT: VOLUME 5, ISSUE 3, December 2026
Publisher : Bumi Publikasi Nusantara

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Abstract

Artificial Intelligence (AI) has fundamentally reshaped the architecture of Information Systems (IS) within higher education institutions. This systematic literature review examines the technological transition from traditional management databases to intelligent, autonomous frameworks. By analyzing peer-reviewed studies published over the last decade, this paper identifies three major evolutionary phases: the automation of administrative tasks, the rise of adaptive learning platforms, and the integration of predictive analytics for student success. The findings highlight how AI-driven systems enhance operational efficiency and personalize student experiences while simultaneously introducing complex challenges regarding data ethics and algorithmic bias. This review provides a comprehensive synthesis of current trends, offering a strategic roadmap for educators and technologists to navigate the future of intelligent academic ecosystems.
From Students To Talent: Orchestrating Human Capital For The Technopreneurial University Transformation Imanuel Eko Anggun Sugiyono; Eddy Soeryanto Soegoto; Rahma Wahdiniwaty; Irfan Dwiguna Sumitra; Adam Mukharil Bachtiar; Puri Swastika Gusti Krisna Dewi
YUME : Journal of Management Vol 9, No 2
Publisher : Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/yume.v9i2.11446

Abstract

This research focuses on testing the effectiveness of the Student-HRM framework—Competency Development, Incentive Support, and Innovation-based Assessment—in influencing multidimensional transformation of student technopreneurship (Culture, Digital Readiness, and Innovation Ecosystem) in higher education institutions. Based on a quantitative explanatory design, data were obtained from 100 undergraduate students who are actively involved with digital entrepreneurship programs through a purposive sampling technique. The conceptual model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4 to assess the measurement model and evaluate the structural path coefficients by a bootstrapping procedure using 5,000 subsamples. The structural model shows significant predictive power (R2 > 0.70). The results highlighted a surprising paradox: Competency Development Support did not contribute significantly to transformation in any dimension, therefore questioning the ingrained belief that training alone leads to readiness. On the other hand, Incentive and Appreciation Support emerged as the stronger predictor and significantly affected all three dimensions. Innovation-based Assessment, however, was effective only in shaping the Technopreneurship Culture but proved ineffective in improving Digital Readiness or Ecosystem engagement.
HelloUMKM : Platform Teknopreneur berbasis Cloud dan Line Bot untuk UMKM Sufa Atin; Eddy Soeryanto Soegoto; Tri Utomo Wiganarto
Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Vol 5 No 1 (2026): Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Publisher : STMIK Amika Soppeng

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70247/jumistik.v5i1.285

Abstract

Perkembangan kewirausahaan digital menuntut UMKM untuk beradaptasi dengan teknologi informasi guna meningkatkan daya saing. Namun, sebagian besar UMKM di Kota Bandung masih menghadapi kendala dalam pengelolaan website promosi dan responsivitas layanan pelanggan. Penelitian ini bertujuan mengembangkan HelloUMKM sebagai platform teknopreneur yang mengintegrasikan teknologi Cloud Computing model SaaS dan LINE Bot untuk memfasilitasi promosi serta transaksi produk. Metode penelitian menggunakan pendekatan deskriptif kualitatif dengan model pengembangan prototipe. Pengujian dilakukan melalui uji alpha (Blackbox dan akurasi algoritma Jaro-Winkler Distance serta Forward Chaining pada LINE Bot) dan uji beta kepada 26 pelaku UMKM. Hasil pengujian menunjukkan fungsionalitas sistem mencapai 100% dengan akurasi LINE Bot sebesar 86%. Secara responsif, 85,38% responden sangat setuju aplikasi mendukung promosi online, 82,31% setuju LINE Bot efektif dalam komunikasi pelanggan, dan 74,62% menilai antarmuka mudah digunakan. Integrasi SaaS multi-tenant dan chatbot cerdas ini terbukti memberikan solusi praktis bagi UMKM dalam membangun ekosistem teknopreneur yang mandiri, responsif, dan berorientasi pada digitalisasi usaha.
The Integration Of Non-Academic Variables In Student Risk Assessment: A Conceptual Framework Hani Irmayanti; Eddy Soeryanto Soegoto; Hidayat Hidayat; Rio Yunanto; Zainal Arifin Hasibuan; Sri Supatmi
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: 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.v1i2.435

Abstract

Students’ success in completing their studies on time is a vital indicator of the quality of higher education management in Indonesia. However, high dropout rates pose a major challenge, often caused by institutions’ failure to detect warning signs of academic failure in a timely manner. The main issue lies in the current evaluation approach, which is reactive and limited to conventional academic indicators such as the Grade Point Average (GPA), thereby neglecting the psychosocial factors that influence performance. This study aims to develop a more comprehensive conceptual framework for the early detection of academic failure risk by integrating academic and non-academic dimensions. The methodology used is adapted from the Design Science Research Methodology (DSRM), focusing on the stages from problem identification to the design of the model artifact. The proposed approach is a hybrid model that combines traditional academic variables with non-academic variables, including psychological stress levels, self-efficacy, and social support. The design results indicate that this framework is capable of identifying “latent pressure” as a leading indicator of failure before a decline in academic performance occurs. The synthesis of this study confirms that the integration of non-academic variables enhances the model’s transparency and provides a more meaningful and targeted interpretation of risk factors. In conclusion, this framework provides a theoretical foundation for educational institutions to transition from reactive evaluation to a system of personalized, proactive interventions. The implementation of this model is expected to improve student retention through earlier and more targeted risk mitigation.
Co-Authors Adam Mukharil Bachtiar Adani Ghina Puspita Sari Adhea Vinora Putri Adilah, N Agis Abdi Rafdhi Agis Abhi Rafdhi Agung Nugraha Agus Mulyana Agustin, Nada Delia Akbar, Aldi Mohamad Albar, Chepi Nur Asep Bayu Dani Nandiyanto Asep Koswara Bachtiar, Adam Mukharil Bobi Kurniawan, Bobi Burhanuddin Burhanuddin Chepi Nur Albar Cindy Nuke Mardika Daniel Maruli Dedi Sulistiyo Soegoto Dian Dharmayanti Dicky Kurniawan Dina Oktafiani Dostnazar Ximmataliyev Elfiyah, E Fadillah, Ismail Hudan Firmansyah Firmansyah Fitri Febriyanti Fristaloka, Gina Dwi Goh Kang Wen Hani Irmayanti Hayati, Euis Neni Hayin Ananta Herman Soegoto Herry Saputra Hewa Majeed Zangana Hidayat Hidayat Ilham Zaki Iluh Sri Purwani Imanuel Eko Anggun Sugiyono Irfan Dwiguna Sumitra Irine Sofianty Juliana Karin Jumansyah, Rizky Kamil, N H Lia Warlina Linda Norhan Luckyardi, Senny Lukito Angga Prasakti M Mulyanto Makalalag, Sukiman Maryati, Mari Maulana, Hanhan Mohamad Akbar, Aldi Mohd. Kamir Yusof Muhammad Ananta Hafidz Muhammad Habibi Putera Muhammad Ikhlas Naufalsyah Ranau Muhammad Irfan Nada Archy Dhafina Nadia Tahiyyah Alifia Natalia, Tri Widiati Novia Aenu Rizqi Nurintang Nurintang Nurul Amelia Puri Swastika Gusti Krisna Dewi Putera, Muhammad Habibi Rafdhi, Agis Abhi Ragadhita, Risti Rahma Wahdiniwaty Rahma Wahdiniwaty Rahmadani, Indri Kristanty Raka Pradana Kostarian Rania Febiananda Rina Maryanti Rio Yunanto Riska Endah Septiani Rizky Jumansyah Rofi Fadilah Madani Saputra, Herry Senny Luckyardi Shinta Wanda Kusuma Modjo Soegoto, Dedi Sulistiyo Sri Supatmi Sri Supatmi Sri Yuliawati Sufa Atin Sugilar, Audya Pridita Sumitra, Irfan Dwiguna Suryatno Wiganepdo Soegoto Sutisnawati, Yayah Tarso, Tarso Theresia Valentina Lumban Gaol Tiara Salsabila Tomas Chochole Tri Utomo Wiganarto Tri Widianti Natalia Wellga Berlianti Zainal Arifin Hasibuan Zanjabil Zulkarnain