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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Moeslim Research Technik
Iwan Ady Prabowo
Universitas Tiga Serangkai
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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DEVELOPMENT OF ARTIFICIAL INTELLIGENCE-BASED ROBOTS FOR RESCUE TASKS AT DISASTER LOCATIONS Achmad Nashrul Waahib; Iwan Ady Prabowo; Kusnadi Kusnadi; Antoni Pribadi; Syafiq Amir
Journal of Moeslim Research Technik Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i1.1929

Abstract

The increasing frequency of natural disasters highlights the urgent need for efficient rescue operations. Traditional methods often face limitations in accessing hazardous areas, making the development of intelligent robotic systems essential for enhancing rescue efforts. This research focuses on creating an AI-based robot specifically designed for search and rescue tasks in disaster-stricken locations. The primary aim of this study is to develop a robotic system that utilizes artificial intelligence to navigate complex environments, identify survivors, and deliver essential supplies. The research seeks to evaluate the robot's effectiveness in real-world scenarios and its potential to improve response times during emergencies. A systematic approach was employed, combining hardware design and software development. The robot was equipped with advanced sensors, machine learning algorithms, and autonomous navigation capabilities. Field tests were conducted in simulated disaster environments to assess the robot's performance in detecting obstacles, locating victims, and executing rescue tasks. The AI-based robot demonstrated a 90% success rate in locating simulated survivors and effectively navigating through obstacles. Response times were significantly reduced compared to traditional methods, showcasing the robot's potential to enhance rescue operations in real emergencies. This research successfully developed an AI-driven robotic system for search and rescue tasks, demonstrating its effectiveness in improving operational efficiency.
Evaluating Steganography Detection in JPEG Images Using Gaussian Mixture Model and Cryptographic Keys Indrawan Ady Saputro; Febrianta Surya Nugraha; Lilik Sugiarto; Iwan Ady Prabowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6084

Abstract

This study introduces a novel approach that integrates Gaussian Mixture Models (GMM) with MD5 hash-based verification to detect hidden messages embedded via Least Significant Bit (LSB) steganography in JPEG images. Unlike previous methods, the proposed dual-layer technique combines probabilistic modeling with data integrity verification. The model was trained and evaluated using a dataset comprising both original and stego-JPEG images. The experimental results achieved an accuracy of 78.67% and a precision of 89.15%, indicating good class separation between stego and non-stego images (AUC-ROC = 0.8659). However, the recall rate of 69.70% suggests that there is room for improvement in detecting all stego instances. Although MD5 is a hash function rather than an encryption algorithm, it effectively aids in identifying data anomalies resulting from message embedding. Overall, this lightweight approach offers a practical solution for steganalysis and can be further enhanced through the integration of hybrid deep learning techniques in future research.
Computational framework for smart tourism management: hybrid time series decomposition and predictive modeling Iwan Ady Prabowo; Hendro Wijayanto; Teguh Susyanto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3421-3430

Abstract

Smart tourism management in rural multi-destination settings requires forecasting methods that are accurate enough to support visitor allocation, infrastructure readiness, and ecological protection. This study presents a decomposition-based forecasting framework for Sidowayah Village, Central Java, Indonesia, which integrates three attractions with different demand profiles: Umbul Manten, Siblarak, and Kampung Dolanan. Using monthly visitation data from May 2023 to April 2024, the study compares additive and multiplicative decomposition models within a common workflow of data collection, preprocessing, trend-seasonal decomposition, model evaluation, and sustainability-oriented interpretation. The contribution of the study lies in clarifying destination-specific criteria for selecting additive versus multiplicative models, improving methodological transparency in preprocessing and temporal validation, and translating forecast outputs into practical smart tourism actions aligned with sustainable development goals (SDGs) 11 and 12. The results show that the multiplicative-average all model yields the lowest mean absolute percentage error (MAPE) for Umbul Manten (14.1%) and Siblarak (56.8%), while the additive-centered moving average model is more suitable for Kampung Dolanan based on mean absolute deviation (MAD) (162.6). Although the 12-month dataset limits long-term generalization, the framework provides a reproducible basis for data-informed tourism management in rural destinations.
Co-Authors Achmad Nashrul Waahib Antoni Pribadi Febrianta Surya Nugraha Hendro Wijayanto Indrawan Ady Saputro Kusnadi Kusnadi Lilik Sugiarto Syafiq Amir Teguh Susyanto