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INDONESIA
Journal of Innovation Materials, Energy, and Sustainable Engineering
Published by Institute for Advanced Science, Social, and Sustainable Future
ISSN : -     EISSN : 30250307     DOI : -
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Electrical & Electronics Engineering Engineering
Journal of Innovation Materials, Energy, and Sustainable Engineering (JIMESE) encourages deeper discussion about sustainability, especially on energy engineering. JIMESE publishes research and review papers about energy sustainability. This journal primary aims to develop and implement technologies that harness renewable energy sources to meet our energy needs. This journal also advance the development of sustainable technologies, promote clean energy production, and address environmental challenges. Article focuses to a more sustainable and environmentally friendly future by improving materials, energy sources, and renewable technology solutions. The scope encompasses materials for structural engineering, electronics, aerospace, healthcare, ossil fuels, nuclear energy, and renewable sources such as solar, wind, hydro, geothermal energy, solar panels, wind turbines, hydropower systems, bioenergy technologies, and other renewable energy solutions. It also involves energy storage systems and grid integration.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol. 3 No. 1: (July) 2025" : 6 Documents clear
A systematic review of machine learning and deep learning approaches for load and energy consumption prediction in contemporary power systems Akinrogunde, Oluwadare Olatunde; Adelakun, Adeola; Theophilus, Edwin Ejike; Thomas, Temitope Grace
Journal of Innovation Materials, Energy, and Sustainable Engineering Vol. 3 No. 1: (July) 2025
Publisher : Institute for Advanced Science Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jimese.v3i1.2025.1949

Abstract

Background: Machine learning (ML) methods are prevalent forecasting model construction tools that outperform conventional methods. This study is a systematic review of machine learning method utilization for load and energy consumption forecasting between 2020-2025. The study covers a variety of methods, ranging from simple algorithms such as linear regression and support vector machines to complex deep learning models such as LSTM, Convolutional Neural Networks CNNs, Transformer models, Graph Neural Networks GNNs, and particular ensemble and hybrid methods. Methods: This study systematically reviewed electric load and energy demand forecasting machine learning techniques with strict methods and harvested primary research databases and preprint servers for English-language papers from January 2020 to May 2025. Results: This study revealed that deep learning models, including LSTM and CNN-LSTM, are becoming more widely used, which indicates a shift towards operational maturity. However, their complexity can be difficult for low-resource environments. The performance of Machine learning models is vastly context dependent. It is a function of factors such as the size, resolution, and range of forecasting involved, thus requiring the proper selection of models. Above all, quality data and proper pre-processing always prevail over the effect of selected machine learning techniques. Conclusion: Machine learning has assisted energy forecasting a lot but falls short on usability and reliability. More technology and collaboration are required to succeed with renewable energy systems. Originality/Novelty of this article: This study describes new developments in Machine learning for energy forecasting and mentions trends and issues to be expected. It recommends what is in the pipeline for future research and applications.
PRISMA systematic review: The application of natural language processing (NLP) to identify greenwashing in sustainability reports within the oil and gas industry Amaliyah, Firli; Putri, Athaya Harmana; Andyna, Naajwaa Putri; Amri, Zaky Khalif
Journal of Innovation Materials, Energy, and Sustainable Engineering Vol. 3 No. 1: (July) 2025
Publisher : Institute for Advanced Science Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jimese.v3i1.2025.2004

Abstract

Background: Greenwashing refers to misleading sustainability claims not backed by real actions, commonly seen in the oil and gas industry due to its dependence on fossil fuels. While companies may publicly commit to sustainability, their investments often contradict these claims, obstructing global renewable energy efforts. This mismatch between statements and actions misleads stakeholders and complicates audit processes. As demands for transparency grow, there is a pressing need for systematic tools to detect greenwashing. Prior research highlights that the narrative format of sustainability reports makes manual detection difficult, underscoring the need for technology-based solutions. Methods: This study aims to examine the application of Natural Language Processing (NLP), particularly the N-Gram model, in identifying indications of greenwashing in the oil and gas industry. The research uses a qualitative approach with a Systematic Literature Review (SLR) method and applies the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Findings: The N-Gram model aids in feature extraction by converting raw text from sustainability reports into structured representations and detecting linguistic patterns commonly found in overstated sustainability claims. When combined with classification methods like Support Vector Machine (SVM), it improves the accuracy of greenwashing detection. Key findings show that NLP can support auditors in assessing greenwashing risks and improving the efficiency of sustainability audits. Moreover, the integration of this technology promotes greater transparency in corporate disclosures. Conclusion: The application of the N-Gram model in the NLP context is effective in detecting greenwashing practices that were previously difficult to identify manually. Novelty/Originality of this article: This study offers novelty through the application of the N-Gram NLP model within the oil and gas industry context, which has been rarely explored in previous research. The practical implications of this study open opportunities for cross-sectoral implementation and the development of data-driven greenwashing identification standards in the future.
RETRACTED: Optimizing geothermal brine for balneological use: An integrated study of health, engineering, social, and economic dimesion Assariy, Naufal Fabianito; Pratama, Arief Putra
Journal of Innovation Materials, Energy, and Sustainable Engineering Vol. 3 No. 1: (July) 2025
Publisher : Institute for Advanced Science Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jimese.v3i1.2025.2108

Abstract

The Editorial Board of Journal of Innovation Materials, Energy, and Sustainable Engineering hereby retracts the article entitled “Optimizing geothermal brine for balneological use: An integrated study of health, engineering, social, and economic dimesion”, published in Vol. 3, No. 1, 2025 and accessible at https://journal-iasssf.com/index.php/JIMESE/article/view/2108. This decision follows confirmation of significant similarity with a previously prepared work by another research team, which had been presented at the IIGCE 2024 Conference. Although the earlier work was not formally published at the time of submission, data from that study were reused without proper authorization. The matter was reported to the Editorial Board in August 2025 and verified through internal review. At the request of the authors and in accordance with publication ethics, the Editorial Board has agreed to retract the article to maintain academic integrity and prevent redundant publication. We sincerely apologize to our readers, reviewers, and the wider academic community for any inconvenience caused. 
Innovation in regeneration of graphene and nmc electrodes from lithium-ion battery waste through an environmentally friendly method Hansel, Jesaya
Journal of Innovation Materials, Energy, and Sustainable Engineering Vol. 3 No. 1: (July) 2025
Publisher : Institute for Advanced Science Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jimese.v3i1.2025.2157

Abstract

Background: The problem of lithium-ion battery (LIB) waste that has not been optimally addressed poses serious risks to the environment and strengthens dependence on primary metal mining. The limited availability of efficient and environmentally friendly recycling methods encourages the need for innovative approaches in the recovery of active materials from used electrodes. This study aims to evaluate the potential of a combination of two alkali-acid regeneration methods for graphene- based anodes and low-temperature molten salt for Nickel Manganese Cobalt (NMC) cathodes as a sustainable strategy in LIB waste treatment. Methods: The study was conducted through a critical literature review of various national and international scientific publications, focusing on the purification effectiveness, morphological characteristics, crystal structure, and electrochemical performance of the regenerated materials. Findings: The analysis results show that the alkali-acid method is effective in selectively removing impurities and is able to increase the specific capacity of the anode to 359 mAh/g, approaching the theoretical capacity of commercial graphene . Meanwhile, the NMC cathode regenerated through the molten salt method and combined with graphene through a simple solid-state mixing showed a capacity of 158.1 mAh/g at a current of 0.5C with good cycle stability. The integration of these two methods synergistically improves electron conductivity, cycle efficiency, and electrode structural stability. In addition to its technical advantages, this approach also utilizes relatively safe and readily available chemicals, making it relevant for both laboratory and industrial applications. The proposed process is competitive with commercial materials and has the potential to be implemented in the economical and industrial-scale remanufacturing of 18650 batteries. Conclusion: These findings significantly contribute to strengthening the battery recycling ecosystem in Indonesia and support the achievement of sustainable energy targets. Furthermore, reducing the volume of hazardous and toxic waste (B3) and optimizing the reuse of high-value materials support the implementation of circular economy principles that align with national policies in the energy and environmental sectors. Novely/Originality of this article: The novelty of this research lies in the integration of two selective and environmentally friendly regeneration methods in one processing system, which has not been widely developed in previous literature, thus offering a new applicable framework for LIB waste processing towards sustainable industrialization.
Beyond carbon mechanisms: The role of energy service companies in strengthening energy transition policies and mitigating emissions Sari, Vivi Arumita; Lioe, Jenniffer Jerica; Sarbana, Virginahlya Hilmarani Ratnaning
Journal of Innovation Materials, Energy, and Sustainable Engineering Vol. 3 No. 1: (July) 2025
Publisher : Institute for Advanced Science Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jimese.v3i1.2025.2171

Abstract

Background: The presence of carbon market mechanisms in Indonesia as a government solution to achieve emission reductions has proven to be ineffective. This ineffectiveness confirms that more effective and comprehensive alternatives are needed. In this case, ESCo can be one of the mechanisms that can encourage low-carbon development in Indonesia. Methods: This research framework uses a multi-method qualitative approach by combining a narrative literature review and document analysis related to the ESCo model and its effectiveness in reducing emissions. Findings: To address the low price of carbon in carbon trading and the prevalence of alleged phantom credits in REDD+ projects, ESCo emerges as a more measurable and stable mechanism. In fact, Indonesia has also launched regulations governing the implementation of ESCo through Ministerial Regulation No. 14 of 2016. However, the lack of social readiness, policy coherence, and suboptimal funding schemes have hampered the implementation of ESCo in Indonesia. Therefore, this paper provides several solutions by examining benchmarks from other relevant countries that can be adopted by the Indonesian government. Conclusions: The success of the ESCo scheme is determined not only by its business model but also by the synergy between public communication, policy reform, financing schemes, and public-private collaboration. Thus, ESCo in Indonesia can be a strategic step to ensure more tangible emission reductions in line with the Nationally Determined Contribution targets. By adopting best practices from other countries and mapping domestic implementation barriers, this study offers a comprehensive framework of solutions to optimize ESCo implementation for promoting low-carbon development in Indonesia. Novelty/Originality of this article: This article offers originality by presenting a comprehensive framework that integrates international best practices and domestic barrier analysis to optimize the implementation of the ESCo model as a more effective alternative to carbon markets for promoting low-carbon development in Indonesia.
BLUEGENIC: Transforming marine plastic waste through AI drone surveillance as a solution for sustainable energy and maritime security Putra, Sendi Kurnia; Hafifa, Nur
Journal of Innovation Materials, Energy, and Sustainable Engineering Vol. 3 No. 1: (July) 2025
Publisher : Institute for Advanced Science Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jimese.v3i1.2025.2286

Abstract

Background: The issue of marine plastic waste has become a tangible threat to the sustainability of marine ecosystems and national maritime security. This study aims to develop BLUEGENIC, a technology-based innovation that integrates marine surveillance using artificial intelligence (AI)-powered drones with the conversion of plastic waste into alternative fuel. Methods: The research employed a Research and Development (R&D) approach through several stages, including needs analysis, system design, concept testing, and validation of implementation potential. Findings: The results indicate that the routine deployment of AI drones in priority maritime areas can map between 50–200 tons of marine plastic waste annually. The collected waste is then processed using the pyrolysis method, capable of producing 13,000–14,000 liters of alternative fuel per month from approximately 16.7 tons of plastic. In addition to contributing to waste reduction and clean energy transition, BLUEGENIC engages the younger maritime generation in research, education, and technological operations. Conclusion: The program also demonstrates economic potential through a blue economy approach and offers opportunities for cross-sectoral collaboration. This study emphasizes the importance of regulatory support and stakeholder synergy in the implementation of BLUEGENIC. Novelty/Originality of this article: The novelty lies in the synergy between AI-drone technology and plastic waste–based alternative energy within the framework of sustainable ocean management and the empowerment of young human resources in the maritime sector.

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