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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics G-Tech : Jurnal Teknologi Terapan Aiti: Jurnal Teknologi Informasi Journal of Applied Computer Science and Technology (JACOST) Journal of Community Services: Sustainability and Empowerment
Gilang Raka Rayuda Dewa
Sampoerna University, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Social Sciences Other

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Introducing Artificial Intelligence Utilization in Learning to High School Teachers Djajasoepena, Rafie; Syamsuri, Ady; Nurfais, Ahmad; Bahagia, Katherine Luckman; Kusuma, Felicia; Dewa, Gilang Raka Rayuda; Purnomo, Ariana Tulus; Bhakti, Muhammad Agni Catur; Wandy, Wandy; Triawan, Farid; Githa, Arum; Lestari, Tika Endah; Setiawan, Iwan
Journal of Community Services: Sustainability and Empowerment Vol. 5 No. 01 (2025): March 2025
Publisher : Center for Research and Community Service of Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/jcsse.v5i1.515

Abstract

The development of artificial intelligence (AI) has significantly impacted various sectors, including education. However, based on observation, no AI tool-integrated course has been utilized by teachers at SMA 6 Cirebon. Moreover, based on the pretest assignment, the average understanding of teachers in SMAN 6 Cirebon regarding AI technology was only 55.48%, indicating challenges in implementing AI tools due to a lack of knowledge and practical guidance. To address this issue, a community service activity was held to empower teachers with applicable AI knowledge and skills through a seminar titled "How AI Learns Like a Brain: Implementasi AI dalam Pembelajaran". A qualitative approach was employed, beginning with seminar preparation, AI literature review, and interactive team discussions. Pre- and post-tests showed an increase in understanding of AI technology, with the mean score rising from 55.48% to 67.22% and the median score increasing from 60% to 80%. Finally, this community service recommends ongoing training, the development of AI-integrated lesson plans, hands-on workshops, and collaboration with educational authorities to support the further implementation of AI in teaching.
Performance Analysis of Synchronous Multilink in Wireless-Based Computer Networks Dewa, Gilang Raka Rayuda
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.778

Abstract

The increasing demand for data transmission results in throughput degradation, which lowers the data rate and increases network outages. ITU notes that global mobile broadband surpasses 1ZB and continues to grow in successive years. Numerous techniques have been investigated to maintain the expected throughput with low computational complexity, including the synchronous multilink method. This technique generates multiple data links to enable simultaneous transmission, allowing for the transmission of more data. However, there is no unified analytical model that captures the inherent trade-offs with procedural simulation in multilink operations. Accordingly, this paper provides a comprehensive analysis of synchronous multilink. The analysis includes the work system, constraints, mathematical expression, Markov chain model, and performance result of synchronous multilink. The simulation results indicate that the synchronous multilink offers promising performance, albeit with certain limitations, for wireless-based computer networks.
Advanced Machine Learning Techniques for Assessing Water Quality: A Comparative Study Using Ensemble, Neural Networks, and Instance-Based Models Muhammad Hafiz; Johan Iswara; Bari Fakhrudin; Widitra Nararya Rama; Avellino Vincent Juwono; Gilang Raka Rayuda Dewa
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7162

Abstract

Access to safe water remains a significant issue, with around 5.8 billion people lacking access to potable water globally. Rapid and accurate identification of water safety is thereby essential to reduce public waterborne diseases. However, conventional laboratory-based testing is typically time-consuming and expensive. On the other hand, machine learning provides time- and cost-effective assessments based on physicochemical properties. Unfortunately, most studies only evaluate a single model type in a small dataset, resulting in limited insight that makes it hard to determine the actual effectiveness of these models. To address this limitation, the present study conducts a comparative analysis of three machine learning paradigms: ensemble-based, neural network-based, and instance-based models. Using a publicly available dataset of 7,999 samples, each model is evaluated using key performance metrics, including accuracy, precision, and confusion matrix analysis. The evaluation results show that the ensemble-based model achieves the highest accuracy of 96.62% and precision of 96.53%, outperforming the neural network-based model, which achieves an accuracy of 94.75% and precision of 70.47%. Additionally, the instance-based model achieves an accuracy of 91.12% and a precision of 83.04%. These results indicate the effectiveness of the ensemble-based model for real-time water quality monitoring.
Lung cancer prognosis based on salivary biomarkers using Graph Convolutional Networks Gilang Raka Rayuda Dewa; Raisa Imani Sani; Ady Syamsuri; Charles Agustin; Muhammad Agni Catur Bhakti; Ariana Tulus Purnomo
AITI Vol 23 No 2 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i2.245-260

Abstract

Lung cancer remains the leading factors that incur cancer-related deaths worldwide, mainly due to late-stage detection. In 2022, lung cancer affected almost 2.5 million people, with mortality of more than 1.8 million. However, existing prognostic methods are typically invasive, costly, and time-consuming, hindering effective early detection. Therefore, this research proposes a non-invasive prognostic approach using salivary biomarkers to detect lung cancer via Graph Convolutional Networks (GCNs). By transforming features into graph node representations, the proposed algorithm can model feature dependencies and topological relationships, enabling more effective pattern recognition than conventional classifiers. The proposed algorithm also applies feature selection to reduce computational complexity. The evaluation results show that the proposed algorithm achieves 95.65% accuracy, a macro F1-score of 95.62%, and a Matthews Correlation Coefficient of 0.9434. A comparative analysis shows that the proposed algorithm outperforms other graph-based architectures in terms of classification performance and computational complexity.
A Comparative Study of Random Forest, K-Nearest Neighbors, and XGBoost Models for Weather-Aware Smart Office Building Automation Erwin Yonata; Maya Anggun Beer; Ni Nyoman Putri Shopia; Emilia Loho; Gilang Raka Rayuda Dewa
Journal of Applied Computer Science and Technology Vol. 7 No. 1 (2026): Juni 2026 (In progress)
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/7925qh24

Abstract

The intelligent control of lighting and HVAC systems plays a critical role in reducing energy consumption in smart buildings. However, many existing automation systems rely on static scheduling strategies that fail to adapt to dynamic environmental conditions. Although machine learning has been widely applied to weather-based building automation, inconsistent feature selection, model configuration, and evaluation procedures limit the validity of comparative performance claims. This study aims to develop and evaluate a machine-learning-based weather classification framework for smart building automation. The proposed methodology follows a structured pipeline comprising data acquisition and preprocessing, model training and testing, parameter tuning, and performance evaluation. A publicly available Weather Type Classification dataset is used, consisting of numerical weather parameters, which are encoded prior to training. Feature selection is applied to identify the most influential predictors. Three machine learning models, Random Forest, K Nearest Neighbors, and XGBoost, are trained using an 80:20 stratified split, with hyperparameters optimized through grid search to ensure an optimized model. Model performance is evaluated using accuracy, precision, recall, F1 score, and a confusion matrix. Experimental results demonstrate that Random Forest achieves the highest accuracy of 97.50 percent, followed by XGBoost at 96.90 percent and K Nearest Neighbors at 95.73 percent, with balanced performance across all weather categories. The findings indicate that ensemble-based classifiers are well-suited for robust weather recognition. The classified weather outputs can be directly mapped to real-time control strategies for lighting and HVAC systems, enabling adaptive automation and improved energy efficiency in smart buildings.
Co-Authors Ady Syamsuri Ariana Tulus Purnomo Avellino Vincent Juwono Bahagia, Katherine Luckman Bari Fakhrudin Charles Agustin Emilia Loho Erwin Yonata Githa, Arum Iwan Setiawan Johan Iswara Kusuma, Felicia Lestari, Tika Endah Maya Anggun Beer Muhammad Agni Catur Bhakti Muhammad Hafiz Ni Nyoman Putri Shopia Nurfais, Ahmad Rafie Djajasoepena Raisa Imani Sani Syamsuri, Ady Triawan, Farid Wandy Wandy Widitra Nararya Rama