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All Journal AGRITEPA: Jurnal Ilmu dan Teknologi Pertanian Global Science: Journal of Information Technology and Computer Science
Anjun Dermawan
Universitas Dharmas Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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An IoT-Based Monitoring System For Assessing Biodigester Fertilizer Quality For Vegetable Farmers Yusran Yusran; Alex Ferdinal; Anjun Dermawan
AGRITEPA: Jurnal Ilmu dan Teknologi Pertanian Vol 13 No 1 (2026)
Publisher : UNIVED Press, Dehasen University Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/agritepa.v13i1.9885

Abstract

Purpose: The increasing demand for organic vegetables is due to their superior quality, freshness, longer shelf life, and healthier flavors compared to non-organic vegetables. Organic vegetables are generally considered safer for consumption. Therefore, vegetables with higher nutritional content, such as vitamin C, antioxidants, and rich minerals, as well as fresher colors and appearance, are sought after. Methodology: This Internet of Things Monitoring System research was implemented and developed using the waterfall method. . Results The results of the reading from the Node MCU control device will then be sent via a WiFi device so that the results can later be accessed online. Findings: Conditions in the field for vegetable farmers make it difficult to use appropriate biodegradable fertilizers, therefore farmers tend to use more instant fertilizers because they are easier and more practical, where fertilizers and pesticides can be purchased at shops.Novelty: The system equipment used and developed is sensor equipment designed to obtain information related to the quality of Biodigester fertilizer that is suitable for use as vegetable fertilizer, namely a methane sensor (MQ-4 Sensor) and a control device used by the MCU Node. Originality: This research uses the Waterfall method, the stages are Engineering, design and implementation, testing, application and maintenance. Conclusion: The test results of the prototype of the cow manure pH meter as a biodigester fertilizer above show that the NodeMCU-based cow manure pH meter system was successfully designed and functioned well. The pH value can be displayed on the LCD and sent to a smartphone in real time via a WiFi network. The measurement accuracy is quite good with an error below 3%. Type of Paper: Empirical Research Articles
Augmented Reality-Assisted Explainable AI Platform for Collaborative Design of Cyber-Physical Systems in Industrial Automation Anjun Dermawan; Efan Efan; Elay Yusifli Elshad
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 3 (2025): September: Global Science: Journal of Information Technology and Computer Scien
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i3.177

Abstract

The integration of Augmented Reality (AR) and Explainable AI (XAI) within Cyber-Physical Systems (CPS) design is transforming the industrial automation landscape. This study explores how combining AR’s immersive visualization with XAI’s decision transparency enhances collaborative design processes in CPS. The AR-XAI platform developed in this research improves team collaboration by offering real-time visual feedback and enabling interactive decision-making. The platform provides interpretable insights into AI-driven decisions, fostering trust among engineers and decision-makers. Key features of the platform include the ability to visualize complex CPS models, facilitating faster iterations, reducing design errors, and improving design accuracy. The integration of XAI ensures transparency in decision-making by offering clear explanations of AI predictions, which is essential for ensuring accountability and building trust in automated systems. Testing with industrial engineers confirmed that the AR-XAI platform significantly improved design outcomes, with a reduction in errors and enhanced team performance compared to traditional design methods. Moreover, the platform enabled faster decision-making and improved collaboration across diverse teams, demonstrating its potential to optimize CPS design workflows. This research provides valuable insights into the role of AR and XAI in advancing Industry 4.0 and paves the way for more advanced integrations of these technologies in industrial settings. Future research should focus on developing scalable solutions for various industrial applications and exploring more sophisticated AR-XAI integrations for emerging fields like smart cities and autonomous manufacturing.
Co-Authors Alex Ferdinal Efan Efan Elay Yusifli Elshad Yusran Yusran