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All Journal Journal of Moeslim Research Technik Journal of Selvicoltura Asean Journal of Loomingulisus ja Innovatsioon Techno Agriculturae Studium of Research
Usman Tariq
COMSATS University Islamabad
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Other

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 1 
Application of Model Predictive Control (MPC) in Industrial Automation Robotic Systems Bilal Aslam; Usman Tariq; Arnes Yuli Vandika
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1566

Abstract

The industrial automation sector is rapidly evolving, with a growing need for advanced control strategies to enhance the efficiency and precision of robotic systems. Model Predictive Control (MPC) has emerged as a promising approach due to its ability to handle multivariable control problems and constraints effectively. However, its application in robotic automation remains underexplored. This research aims to implement Model Predictive Control in industrial robotic systems to improve performance, adaptability, and operational efficiency. The study focuses on evaluating the effectiveness of MPC in real-time robotic applications, specifically in tasks requiring high precision and dynamic response. A simulation-based approach was employed, using a robotic arm model as a testbed for implementing MPC. The control algorithm was designed to predict future states of the system based on current measurements and optimize control inputs accordingly. Performance metrics, including tracking error and response time, were evaluated under various operational scenarios. The implementation of MPC resulted in a significant reduction in tracking error and improved response times compared to traditional control methods. The robotic arm demonstrated enhanced adaptability to changes in the environment and task requirements, showcasing the robustness of the MPC approach. The findings indicate that Model Predictive Control is an effective strategy for enhancing the performance of robotic systems in industrial automation. The successful application of MPC not only improves operational efficiency but also provides a framework for future research into more complex robotic applications. This study contributes to the growing body of knowledge on advanced control methods in automation.  
USING ARTIFICIAL INTELLIGENCE AND LIDAR DATA FOR HIGH-RESOLUTION FOREST INVENTORY AND ABOVE-GROUND BIOMASS ESTIMATION IN A SUMATRAN RAINFOREST Nofirman Nofirman; Ahmed Shah; Usman Tariq
Journal of Selvicoltura Asean Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i5.2483

Abstract

Accurate quantification of forest carbon stocks is critical for global climate change mitigation initiatives like REDD+. Traditional forest inventory methods are often labor-intensive, costly, and limited in scale, particularly in complex tropical ecosystems such as the Sumatran rainforest. The integration of advanced remote sensing technologies and artificial intelligence (AI) offers a transformative potential for overcoming these limitations. This study aimed to develop and validate a high-resolution model for individual tree detection and above-ground biomass (AGB) estimation in a Sumatran rainforest by synergizing airborne LiDAR data with machine learning algorithms. High-density LiDAR data was acquired over a 10,000-hectare study area. Concurrently, extensive field inventory data from 150 plots were collected to serve as ground truth. A deep learning model, specifically a Convolutional Neural Network (CNN), was trained to perform individual tree crown delineation (ITCD) from the LiDAR-derived canopy height model. Tree-level metrics were then used as predictors in a Random Forest algorithm to estimate AGB, which was calibrated against field-measured biomass. The CNN model successfully identified individual trees with an accuracy of 92.4%. The subsequent Random Forest model demonstrated high predictive power for AGB estimation, yielding a strong coefficient of determination ( = 0.89) and a low Root Mean Square Error (RMSE) of 25.8 Mg/ha. The approach generated a high-resolution (1-meter) AGB map, revealing detailed spatial variations in carbon stock across the landscape. The fusion of AI and LiDAR data provides a highly efficient methodology for forest inventory and AGB mapping in dense tropical rainforests. This approach significantly enhances our capacity to monitor carbon dynamics, forest conservation and climate policy.
Analysis of Factors that Influence the Implementation of Technological Innovation in the Indonesian Public Sector Bilal Aslam; Usman Tariq; Omar Ahmad
Journal of Loomingulisus ja Innovatsioon Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v1i6.1713

Abstract

The implementation of technological innovation in the public sector is a critical driver of efficiency, transparency, and service delivery in many countries, including Indonesia. Despite the increasing importance of technology in public administration, the adoption and effective implementation of technological innovations in the Indonesian public sector face various challenges, including organizational resistance, inadequate infrastructure, and limited digital skills. This study aims to analyze the factors that influence the successful implementation of technological innovations in Indonesian public institutions. Using a mixed-methods approach, the research integrates qualitative interviews with key public sector managers and quantitative surveys of public sector employees to identify the critical factors that facilitate or hinder the adoption of technology. The findings highlight that leadership commitment, organizational culture, availability of resources, and training programs are significant drivers of successful technological implementation. Conversely, barriers such as budget constraints, political factors, and a lack of technical expertise were found to limit the effectiveness of technological innovation. The study concludes that for successful technological innovation in the Indonesian public sector, it is crucial to focus on strengthening leadership, fostering a supportive organizational culture, and investing in infrastructure and training programs. These findings provide practical recommendations for policymakers and public sector managers in Indonesia.
The Effect of Counselor Training Programs on the Quality of Interventions in Schools Omar Ahmad; Maria Clara Reyes; Usman Tariq
Journal of Loomingulisus ja Innovatsioon Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v3i1.2364

Abstract

Entrepreneurial ecosystems have become critical drivers of the creative economy by fostering collaboration, innovation, and sustainable business models. In the context of the Global South, these ecosystems face structural challenges such as limited access to finance, infrastructure gaps, and unequal opportunities, yet they also present unique opportunities for inclusive growth. This study aims to examine how entrepreneurial ecosystems shape and influence the development of the creative economy in a Global South context, with a focus on the mechanisms that enable creative entrepreneurs to thrive despite these constraints. A qualitative case study design was employed, drawing on data collected from 45 semi-structured interviews with entrepreneurs, policy makers, and cultural actors, as well as document analysis and participant observation in three creative industry clusters. The findings reveal that local networks, informal mentoring, and culturally embedded innovation serve as the backbone of these ecosystems. Results highlight that while systemic limitations persist, adaptive strategies, collaborative practices, and policy interventions can strengthen ecosystem performance and contribute to creative sector resilience. This study concludes that fostering inclusive entrepreneurial ecosystems in the Global South has a transformative potential for advancing the creative economy and addressing socio-economic disparities.
INNOVATIVE WATER-SAVING IRRIGATION TECHNOLOGY FOR AGRICULTURE IN ARID REGIONS OF SOUTH AFRICA Usman Tariq; Rizky Franchitika; Kim Minho
Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i1.1988

Abstract

Agriculture in the arid regions of South Africa faces major challenges related to water scarcity, which worsens the sustainability of the sector. Water-efficient irrigation technology has emerged as a potential solution to reduce water use and increase agricultural productivity. This study aims to evaluate the impact of water-saving irrigation technology on water use efficiency and crop yields in arid regions of South Africa. Quantitative and qualitative approaches were used in this study, involving 150 farmers as a sample, as well as questionnaire data analysis and in-depth interviews. The results of the study show that this technology is able to increase water use efficiency by up to 30%, increase crop yields by 20%, and reduce average operating costs by 15%. The conclusion of the study is that water-efficient irrigation technology plays an important role in improving the sustainability of agriculture in dry regions and can contribute to food security in South Africa. The adoption of this technology needs to be encouraged more widely through government support and training for farmers.
Co-Authors Ahmed Shah Arnes Yuli Vandika Bilal Aslam Kim Minho Maria Clara Reyes Nofirman, Nofirman Omar Ahmad Rizky Franchitika