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All Journal Media Ekonomi dan Manajemen ASEAN Journal for Science and Engineering in Materials ASEAN Journal of Educational Research and Technology
Chochole, Tomas
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Analysis of Aggregate Herding Behavior in the Capital Market: Evidence from Indonesia and Singapore Gusni, Gusni; Nugraha, Nugraha; Disman, Disman; Chochole, Tomas
Media Ekonomi dan Manajemen Vol 38, No 2 (2023): July 2023
Publisher : Fakultas Ekonomika dan Bisnis UNTAG Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56444/mem.v38i2.3934

Abstract

The high uncertainty in the capital market due to some crises that hit the world over the last few decades has the potential to cause herding behavior in the aggregate capital market, both in developed and emerging capital markets. The main objective of this study is to detect the existence of herding behavior, including asymmetric herding and global factor drives (oil prices and fed fund rates) on aggregate herding behavior in the Indonesian and Singapore capital markets during the period Jan 2015 to December 2020. This study employs a cross-sectional dispersion approach to achieve study goals. Research findings denote aggregate herding behavior occurs only in the Singapore capital market, while in Indonesia no herding behavior is detected. Asymmetric herding testing for both capital markets revealed no herding tendency in up and down market conditions. This condition implies that low volatility cannot ensure the absence of aggregate herding behavior. Global factors have proven to significantly drive herding behavior in the Singapore capital market, while in Indonesia it is only the oil price. The findings of this study will provide information that policymakers can use to maintain capital market stability in both countries.
Evolution of Artificial Intelligence (AI)-driven Information Systems in Higher Education: A Review Karin, Juliana; Dharmayanti, Dian; Luckyardi, Senny; Soegoto, Eddy Soeryanto; Ximmataliyev, Dostnazar; Yusof, Mohd. Kamir; Chochole, Tomáš; Zangana, Hewa Majeed
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.
Energy-Harvesting Materials for Autonomous Smart Farming Sensors: A Literature Review Septiani, Riska Endah; Kurniawan, Bobi; Luckyardi, Senny; Soegoto, Eddy Soeryanto; Ximmataliyev, Dostnazar; Yusof, Mohd. Kamir; Chochole, Tomas; Zangana, Hewa Majeed
ASEAN Journal for Science and Engineering in Materials Vol 6, No 1 (2027): (ONLINE FIRST) AJSEM: Volume 6, Issue 1, March 2027
Publisher : Bumi Publikasi Nusantara

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Abstract

The integration of the Internet of Things (IoT) in smart farming is hindered by limited battery life and the environmental impact of electronic waste. This review evaluates the development of energy-harvesting materials as a solution to power autonomous agricultural sensors. Through a systematic review, this paper analyzes three main mechanisms: Organic Photovoltaic (OPV), triboelectric nanogenerator/piezoelectric nanogenerator (TENG/PENG), and thermoelectric generator (TEG). Flexible polymers for TENGs and perovskite-based solar cells have the highest potential in addressing canopy shading and outdoor weather challenges. However, material toxicity and degradation due to UV and humidity remain major obstacles. Future research must prioritize biocompatible materials and hybrid systems to ensure the sustainability of precision agriculture.
Predictive Modelling of Electronic Materials: A Review of Deep Learning Techniques in Computer Engineering Rafdhi, Agis Abhi; Maulana, Hanhan; Luckyardi, Senny; Soegoto, Eddy Soeryanto; Ximmataliyev, Dostnazar; Wen, Goh Kang; Chochole, Tomáš; Zangana, Hewa Majeed
ASEAN Journal for Science and Engineering in Materials Vol 5, No 3 (2026): (ONLINE FIRST) 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.
Co-Authors Bobi Kurniawan, Bobi Dharmayanti, Dian Disman, Disman Eddy Soeryanto Soegoto Gusni, Gusni Karin, Juliana Luckyardi, Senny Maulana, Hanhan Nugraha Nugraha Rafdhi, Agis Abhi Septiani, Riska Endah Wen, Goh Kang Ximmataliyev, Dostnazar Yusof, Mohd. Kamir Zangana, Hewa Majeed