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All Journal Jurnal Ilmiah FIFO Green Engineering: Journal of Engineering and Applied Science Global Science: Journal of Information Technology and Computer Science
Krisna Widi Nugraha
Universitas Harapan Bangsa
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Adaptive Graph Based Intelligence Models for Cross Domain Knowledge Discovery in Large Scale Heterogeneous Information Systems Winny Purbaratri; Krisna Widi Nugraha; Rian Ardianto; Rosyid Ridlo Al-Hakim; Yogiek Indra Kurniawan; Ribut Julianto
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.193

Abstract

The rapid growth of heterogeneous information systems across multiple domains has introduced complex challenges in data analysis, particularly when dealing with diverse data types such as text, images, and sensor data. Traditional machine learning (ML) methods often struggle to capture the intricate relationships inherent in these large scale datasets, as they typically rely on linear models and feature vectors that fail to represent the full complexity of the data. This study aims to develop an adaptive graph based intelligence model that addresses these challenges by leveraging the power of graph structures to represent heterogeneous data and capture both structural dependencies and semantic connections. The proposed model integrates Graph Neural Networks (GNNs) with adaptive learning mechanisms, allowing for continuous knowledge extraction, pattern discovery, and cross domain inference. By representing diverse data sources as interconnected graphs, the model enables the transfer of knowledge across different domains, improving its ability to make accurate predictions and generate insights in dynamic environments. The results demonstrate that the graph based model outperforms traditional machine learning techniques in terms of accuracy, efficiency, and scalability, especially when applied to real world applications involving large and complex datasets. This paper also discusses the advantages of the adaptive learning mechanisms, which personalize the model’s training process and improve its robustness over time. Furthermore, the findings highlight the model’s potential for cross domain knowledge discovery, with applications in fields such as healthcare, marketing, and industrial automation. Finally, the paper offers recommendations for future research, including refining adaptive learning mechanisms and exploring new graph based techniques to enhance the representational power of the model. The study contributes to the ongoing development of intelligent systems capable of handling heterogeneous data across multiple domains and offers a foundation for future advancements in cross domain knowledge discovery.
Interoperabilitas Sistem Presensi Berbasis Web dengan Push Notification untuk Distribusi Informasi Real-Time Krisna Widi Nugraha; Arief Luqman Hadiyani
Jurnal Ilmiah FIFO Vol. 18 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2026.v18i1.009

Abstract

Efisiensi pengelolaan data kesiswaan sangat dipengaruhi oleh kecepatan dan ketepatan distribusi informasi, sementara sistem presensi berbasis web konvensional umumnya masih bersifat pasif dan bergantung pada akses manual pengguna. Kondisi ini berpotensi menimbulkan keterlambatan dalam memperoleh informasi kehadiran yang dibutuhkan untuk pemantauan operasional dan pengambilan keputusan manajerial di lingkungan sekolah. Penelitian ini bertujuan mengimplementasikan dan menguji interoperabilitas sistem presensi berbasis web dengan layanan Telegram API melalui mekanisme push notification untuk mendukung distribusi laporan presensi secara lebih proaktif dan real-time. Sistem dikembangkan menggunakan arsitektur client-server dengan integrasi antarplatform melalui RESTful API. Setiap transaksi presensi yang tervalidasi melalui pemindaian QR Code secara otomatis memicu pengiriman notifikasi kehadiran ke platform Telegram. Evaluasi sistem dilakukan melalui pengukuran latensi pengiriman notifikasi, yaitu selang waktu antara proses pemindaian QR Code hingga notifikasi diterima pada platform Telegram, pada beberapa skenario operasional dengan total 105 transaksi presensi. Hasil pengujian menunjukkan bahwa sistem mampu mendistribusikan informasi presensi dengan latensi rata-rata kurang dari dua detik. Temuan ini menunjukkan bahwa mekanisme push-based notification efektif dalam mengurangi jeda distribusi informasi dibandingkan pendekatan pull-based konvensional. Kontribusi penelitian ini terletak pada implementasi model interoperabilitas berbasis event-driven yang mengintegrasikan sistem presensi dengan layanan pesan instan sebagai media distribusi informasi real-time. Dengan demikian, sistem yang dikembangkan dapat berfungsi sebagai komponen awal Decision Support System melalui penyediaan informasi presensi yang relevan, tepat waktu, dan terstruktur, khususnya pada lingkungan SMK Muhammadiyah Bobotsari.
AI driven Circular Waste to Energy Conversion System Using Smart Thermal Monitoring and Emission Optimization for Sustainable Urban Infrastructure Kiki Ahmad Baihaqi; Krisna Widi Nugraha; Rian Ardianto; Rosyid Ridlo Al-Hakim; Riza Phahlevi Marwanto; Erick Fernando
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.289

Abstract

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.
Integrating Semantic Computing and Predictive Analytics to Enhance Reliability and Scalability of Global Information Systems Agus Wantoro; Adhie Thyo Priandika; Tiwuk Widiastuti; Yulaikha Mar’atullatifah; Krisna Widi Nugraha; Dwi Utari Iswavigra
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.196

Abstract

Global information systems (GIS) are essential for managing large scale data across industries such as healthcare, finance, and urban planning. As the volume and complexity of data continue to grow, there is an increasing need for systems that can handle these demands while maintaining reliability and scalability. This research explores the integration of semantic computing and predictive analytics as a solution to improve the performance of GIS. Semantic computing, through the use of ontologies and standardized data models, enhances data interoperability, allowing systems to interpret and exchange data meaningfully across diverse platforms. On the other hand, predictive analytics uses statistical methods and machine learning models to forecast system behavior and optimize resource allocation, ensuring systems remain adaptive under varying loads. By integrating these two methodologies, this study demonstrates how they can address key challenges in global information systems, such as fault tolerance, system adaptability, and real time decision making. The results show significant improvements in system reliability and scalability, as well as better performance under high data volumes and diverse user interactions. The integrated approach was tested in several use cases, including urban planning, healthcare, and supply chain management, with results indicating that systems utilizing both semantic computing and predictive analytics are more resilient, accurate, and efficient. This paper discusses the practical implications of this integration for global scale applications and suggests future research directions, including the incorporation of emerging technologies like blockchain and artificial intelligence to further enhance the capabilities of GIS.
Co-Authors Agus Wantoro Arief Luqman Hadiyani Baihaqi, Kiki Ahmad Dwi Utari Iswavigra Erick Fernando Julianto, Ribut Kurniawan, Yogiek Indra Priandika, Adhie Thyo Rian Ardianto Riza Phahlevi Marwanto Rosyid Ridlo Al-Hakim Tiwuk Widiastuti Winny Purbaratri Yulaikha Mar’atullatifah