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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Physics and Its Applications Advance Sustainable Science, Engineering and Technology (ASSET)
Karnadi, Indra
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Astronomy Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics

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Enhancement of Laser-Induced Breakdown Spectroscopy (LIBS) Sensitivity Using Electric Fields: A Study on Non-Metal Samples Tanra, Ivan; Limanto, Agus; Hadiyanto, Marvin Yonathan; Karnadi, Indra
Journal of Physics and Its Applications Vol 7, No 2 (2025): May 2025
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v7i2.25558

Abstract

Laser-induced breakdown spectroscopy (LIBS) has proven to be a versatile and effective technique for elemental analysis across a variety of fields, including geology, archaeology, materials science, nuclear power, and medicine. This study focuses on the application of an external electric field to enhance the performance of LIBS, specifically for non-metal samples such as black coral. By introducing an electric field and varying laser energy levels, the effects on plasma generation and emission intensities were investigated. The experimental results demonstrate that applying an electric field significantly enhances spectral intensity, with notable improvements in the Carbon (C I) emission line at 247.8 nm. The enhancement was observed to be nonlinear, with significant increases only when the electric field strength exceeded 200 V/cm. Laser energy was also found to play a critical role, with carbon signals only detectable at energies above 20 mJ, and optimal results achieved at 50 mJ. These findings highlight the combined effect of laser energy and electric field strength in enhancing LIBS sensitivity, particularly for detecting trace elements in organic samples. This approach offers a simple, efficient, and effective method to improve LIBS performance, with potential applications in fields such as fossil age determination and other analytical studies requiring high sensitivity.
Zonation Method for Efficient Training of Collaborative Multi-Agent Reinforcement Learning in Double Snake Game Hadiyanto, Marvin Yonathan; Harsono, Budi; Karnadi, Indra
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17562

Abstract

This paper proposes a zonation method for training the two reinforcement learning agents. We demonstrate the method's effectiveness in the double snake game. The game consists of two snakes operating in a fully cooperative setting to maximize the score. The problem in this game can be related to real-world problems, namely, coordination in autonomous driving cars and the operation of collaborative mobile robots in warehouse applications. Here, we use a deep Q-network algorithm to train the two agents to play the double snake game collaboratively through a decentralized approach, where distinct state and reward functions are assigned to each agent. To improve training efficiency, we utilize the snake sensory data of the surrounding objects as the input state to reduce the neural network complexity. The obtained result show that the proposed approaches can be used to train collaborative multi-agent efficiently, especially in the limited computing resources and training time environment
Imitation Learning to Accelerate Training Process of Multi-Agent Reinforcement Learning in 2v2 Pong Game Hadiyanto, Marvin Yonathan; Harsono, Budi; Karnadi, Indra; Tanra, Ivan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2564

Abstract

Training multi-agent reinforcement learning (MARL) systems often requires a significant amount of time due to sample inefficiency, particularly where agents need to do a considerable amount of exploration in a complex environment and coordination among multiple entities. This study proposes the use of imitation learning to accelerate the MARL training process in a 2v2 pong game by learning from demonstrations in 1v1 pong game to shape the initial policy without undergoing inefficient exploration procedure. We use deep Q-network (DQN) with centralized training with decentralized execution (CTDE) to observe the difference of performance between pretrained and untrained agents in 2v2 pong game. Experimental results show that learning from demonstration in 1v1 setting significantly improved reward accumulation and game scores of pretrained agent in 2v2 pong game. The improvement peaks at 700 learning steps of demonstration and diminishes at the larger learning steps due to excessive memorization of the demonstration gameplay. This work demonstrates that imitation learning from demonstrations can be used to reduce a prolonged training process in MARL, offering a viable solution especially when data collecting, computational resources, and training are the severely constrained.
Co-Authors Agus Limanto Budi Harsono Hadiyanto, Marvin Yonathan Tanra, Ivan