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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Nasional Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Alih Teknologi Komputer (ALTEK) Abdi Akommedia : Jurnal Pengabdian Masyarakat West Science Nature and Technology Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Journal of Computer Science Advancements Journal of Loomingulisus ja Innovatsioon Journal Emerging Technologies in Education Journal of Social Science Utilizing Technology
Triyugo Winarko
Universitas Mitra Indonesia
Religion Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Physics Transportation Other

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The Role of Information Systems in Facilitating Collaborative Learning in Higher Education Amelia Hayati; Mahon Nitin; Hilda Dwi Yunita; Fatimah Fahurian; Triyugo Winarko
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1614

Abstract

Background. The rapid evolution of digital technologies has transformed educational practices, with information systems playing a critical role in enhancing collaborative learning in higher education. Collaborative learning, which emphasizes peer-to-peer interaction and shared knowledge construction, fosters critical thinking, problem-solving, and teamwork skills essential for the 21st-century workforce. Despite its potential, the implementation of effective collaborative learning strategies often faces challenges such as insufficient technological support and lack of engagement. Purpose. This study investigates the role of information systems in facilitating collaborative learning, focusing on their impact on student engagement, knowledge sharing, and learning outcomes. Method. A mixed-method approach was employed, combining quantitative surveys and qualitative interviews with 200 undergraduate students and 20 faculty members across three higher education institutions. Data were collected on the use of information systems such as learning management platforms, virtual collaboration tools, and online forums. Key performance indicators included student engagement, participation rates, and academic performance. Results. The findings revealed that information systems significantly enhance collaborative learning by providing a centralized platform for communication, resource sharing, and real-time interaction. Students reported a 30% increase in engagement and a 25% improvement in teamwork efficiency when using digital collaboration tools. Faculty members highlighted the ease of monitoring and supporting group activities through analytics and reporting features. Challenges such as digital literacy gaps and technical issues were also identified. Conclusion. The study concludes that information systems are pivotal in enabling effective collaborative learning by fostering connectivity, engagement, and accountability among students. Addressing challenges related to accessibility and technical support will maximize their potential in higher education.
Geospatial data processing and random forest-based intelligent system for regional investment readiness prediction Yudhinanto Cahyo Nugroho; Desmon Desmon; Hasbullah Hasbullah; Triyugo Winarko
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp651-661

Abstract

This paper presents an intelligent system that integrates geospatial data processing and a random forest (RF) classification model to categorize regional investment readiness (IR). Regional investment planning is often constrained by fragmented socio-economic data, unequal infrastructure distribution, and unquantified disaster risk, which reduce the accuracy of decision making. To address this problem, multidimensional data consisting of socio-economic indicators, infrastructure accessibility, and disaster risk factors were collected from a sample of 15 administrative regions in Lampung Province, Indonesia, and processed through data cleaning, normalization, and feature selection. An IR score was first computed for each region using a weighted composite formula, then discretized into readiness classes and used as the target label to train a RF classifier capable of modeling complex nonlinear relationships among the input features. Given the limited sample size, model performance was evaluated using leave-one-out cross-validation, and classification metrics—accuracy, precision, recall, and F1-score—were reported to assess predictive reliability. The results reveal spatial disparities in IR, where regions with higher human development and better infrastructure tend to exhibit greater investment potential, while areas exposed to higher disaster risk tend to show lower readiness levels. The prediction outputs are integrated into a web-based interactive dashboard that enables spatial visualization and exploration of IR patterns. Given the small and single province sample, the proposed system should be regarded as a preliminary decision-support tool for policymakers and investors to help identify priority regions, and further validation on larger, more geographically diverse datasets is recommended to strengthen generalizability.
Co-Authors Aldesion Denagi Zenda Amelia Hayati Amelia S. Sarungallo Arief Hidayat Arnes Yuli Vandika budi hartanto Desmon Desmon Fatimah Fahurian Hamdan Sukri Hasbullah Hasbullah Hilda Dwi Yunita iin marliana Loso Judijanto Mahon Nitin Maryana Maryana, Maryana Ming Pong Rustiyana, Rustiyana Ryan Teo Teddy Oswari Usman Tahir Yodhi Yuniarthe Yudhinanto Cahyo Nugroho