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A Holistic Approach to Carbon Trading Based on AI, Blockchain, and Satellite Data: A Study on East Java Protected Forest Haryono, Haryono; Rahman, Arief; Zainal, Rifki Fahrial; Santoso, Bagus Teguh; Endarto, Budi
West Science Interdisciplinary Studies Vol. 3 No. 03 (2025): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v3i03.1803

This study explores a holistic approach to carbon trading by integrating Artificial Intelligence (AI), blockchain technology, and Geographic Information Systems (GIS) to address challenges in managing East Java’s protected forests. AI models were utilized to estimate carbon stocks with high accuracy, while blockchain ensured transparent and secure carbon credit transactions. GIS analysis provided real-time monitoring of forest dynamics and identified high-priority zones for carbon trading. The integrated framework demonstrated significant improvements in efficiency, accuracy, and stakeholder trust compared to traditional methods. The study concludes that this approach is a scalable and effective solution for enhancing carbon trading systems and contributing to sustainable forest management in Indonesia.

Modeling Carbon Trade with Satellite Approach and AI Technology: A Sustainable Solution for REDD+ Scheme in Indonesia Haryono, Haryono; Rahman, Arief; Zainal, Rifki Fahrial; Santoso, Bagus Teguh; Endarto, Budi
West Science Nature and Technology Vol. 3 No. 01 (2025): West Science Nature and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsnt.v3i01.1804

The increasing urgency to mitigate climate change has intensified the need for effective carbon trading mechanisms, particularly under the REDD+ scheme. This study explores the potential of integrating satellite technology, Geographic Information Systems (GIS), and Artificial Intelligence (AI) to develop a sustainable carbon trade model tailored to Indonesia’s unique environmental and policy landscape. The research focuses on deforestation hotspots in Kalimantan, Sumatra, and Papua, leveraging high-resolution satellite imagery and machine learning algorithms for precise carbon stock estimation. Results indicate significant deforestation trends, with an average annual loss of 1.2% of forest cover and 320 million metric tons of carbon over the past decade. AI-powered predictive models achieved 92% accuracy in identifying deforestation hotspots and estimating carbon stocks, underscoring their utility in enhancing Monitoring, Reporting, and Verification (MRV) systems. Policy analysis highlights critical gaps in enforcement and community participation. This study proposes a scalable and transparent carbon trade model that aligns with REDD+ objectives, fostering equitable and sustainable climate solutions for Indonesia.

Utilizing AI and Satellite Technology to Measure the Effectiveness of Carbon Trading in East Java Protected Forests Haryono, Haryono; Rahman, Arief; Zainal, Rifki Fahrial; Santoso, Bagus Teguh; Endarto, Budi
West Science Nature and Technology Vol. 3 No. 01 (2025): West Science Nature and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsnt.v3i01.1805

This study investigates the effectiveness of utilizing Artificial Intelligence (AI) and Geographic Information Systems (GIS) for measuring the impact of carbon trading initiatives on East Java's protected forests. The research integrates satellite imagery, AI-driven land-use classification, and carbon stock analysis to evaluate the environmental, economic, and social outcomes of these programs. Key findings indicate a significant reduction in deforestation and an increase in carbon sequestration, driven by targeted reforestation efforts and financial incentives from carbon trading. Socioeconomic benefits, including enhanced community livelihoods and reduced reliance on unsustainable practices, further underscore the program's success. However, challenges such as leakage effects and data inconsistencies highlight areas requiring improvement. The study concludes that advanced technologies, when effectively integrated, offer transformative potential for sustainable forest management and carbon trading efficacy in Indonesia.

A Holistic Approach to Carbon Trading Based on AI, Blockchain, and Satellite Data: A Study on East Java Protected Forest Haryono, Haryono; Rahman, Arief; Zainal, Rifki Fahrial; Santoso, Bagus Teguh; Endarto, Budi
West Science Interdisciplinary Studies Vol. 3 No. 03 (2025): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v3i03.1803

This study explores a holistic approach to carbon trading by integrating Artificial Intelligence (AI), blockchain technology, and Geographic Information Systems (GIS) to address challenges in managing East Java’s protected forests. AI models were utilized to estimate carbon stocks with high accuracy, while blockchain ensured transparent and secure carbon credit transactions. GIS analysis provided real-time monitoring of forest dynamics and identified high-priority zones for carbon trading. The integrated framework demonstrated significant improvements in efficiency, accuracy, and stakeholder trust compared to traditional methods. The study concludes that this approach is a scalable and effective solution for enhancing carbon trading systems and contributing to sustainable forest management in Indonesia.

Analysis of Blockchain Integration in Carbon Trade Management: A Perspective on Forest Cover and REDD+ Schemes in Indonesia Haryono, Haryono; Rahman, Arief; Zainal, Rifki Fahrial; Santoso, Bagus Teguh; Endarto, Budi
West Science Social and Humanities Studies Vol. 3 No. 03 (2025): West Science Social and Humanities Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsshs.v3i03.1806

The integration of blockchain technology in carbon trade management offers transformative potential for improving transparency, monitoring, and efficiency in Indonesia's REDD+ schemes. This study employs a qualitative approach, analyzing data from three informants—a blockchain expert, an environmental policymaker, and a conservationist—using NVivo software for thematic analysis. Findings reveal that blockchain enhances transparency and trust, streamlines Monitoring, Reporting, and Verification (MRV) processes, and addresses challenges in carbon trade systems. However, barriers such as infrastructure gaps, high implementation costs, and regulatory ambiguities remain. The study underscores the need for targeted capacity building, pilot programs, and robust policy frameworks to enable effective blockchain adoption in Indonesia's carbon markets.

Integrative Blockchain for Transparency and Efficiency of Carbon Trade Marketing Model in Bromo-Tengger-Semeru Region Haryono, Haryono; Rahman, Arief; Zainal, Rifki Fahrial; Santoso, Bagus Teguh; Endarto, Budi
West Science Social and Humanities Studies Vol. 3 No. 03 (2025): West Science Social and Humanities Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsshs.v3i03.1807

The integration of blockchain technology with Geographic Information Systems (GIS) offers an innovative solution for enhancing transparency and efficiency in carbon trade marketing. This study explores the development of an integrative blockchain-based carbon trade marketing model for the Bromo-Tengger-Semeru region, utilizing GIS analysis for spatial verification and dynamic monitoring of carbon stock. Results indicate significant carbon sequestration potential in the region, with high carbon stock zones and reforestation opportunities identified through GIS. The blockchain system improves transaction transparency, traceability, and efficiency through the use of smart contracts and decentralized ledgers. Stakeholder workshops demonstrated strong support for the proposed model, emphasizing its potential to overcome existing challenges in carbon trading. The integration of GIS and blockchain ensures spatial accuracy and enhances stakeholder confidence, making it a viable approach for scaling carbon trade initiatives. This study contributes to the advancement of sustainable carbon trade practices, offering a replicable model for other regions.

Tinjauan Integrasi Teknologi Deep Learning Untuk Revolusi Industri Dalam Sistem Siber-Fisik Zainal, Rifki Fahrial; Alim, Syariful; Arizal, Arif; Purnama, Rangsang
INTER TECH Vol 3 No 1 (2025): INTER TECH
Publisher : Fakultas Teknik Universitas Bhayangkara Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/i.v3i1.1266

An important development in industrial automation is the combination of deep learning with cyber-physical systems (CPS), which allows systems to make data-driven, intelligent decisions with little assistance from humans. With an emphasis on its capacity to handle massive amounts of data for tasks including object detection, semantic segmentation, predictive maintenance, and autonomous control, this research investigates the revolutionary effects of deep learning on CPS. It looks at how technology has developed from early frameworks that relied on visual cues to complex systems that use cutting-edge neural networks that can function in dynamic, unstructured contexts. The study also emphasizes how important it is to integrate ethical frameworks, organizational preparedness, and human-centered design in order to successfully implement CPS. This study analyzes important trends, obstacles, and best practices that influence the application of deep learning in CPS through an extensive examination of recent literature. The significance of CPS in facilitating the Industry 4.0 and Industry 5.0 paradigms—which prioritize sustainability, human-machine collaboration, and real-time adaptation in industrial processes—is given particular attention.

Quality of Service (QoS) Analysis using Wireshark on the LAN Network at An Najiyah High School Surabaya Hamidah, Mas Nurul; Tias, Rahmawati Febrifyaning; Zainal, Rifki Fahrial
Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i4.273

In the realm of information technology, Indonesia has entered the fourth generation, or 4G, which is a fast, widely available internet network that can be used to advance a variety of industries, including the agricultural, social, cultural, economic, and even educational ones. Additionally, from 2020 to the beginning of 2022, you will need to be connected to the internet in order to stay productive during the Covid-19 virus outbreak. This is especially true for the education sector, since online teaching and learning activities are essential for maintaining productivity. An Najiyah Surabaya High School needs reliable internet access in order to provide better support for its online learning students. An Najiyah High School Surabaya employs QOS (Quality of Service) to monitor network quality and data traffic transferred over the network. Three QoS parameters—packet loss, throughput, and delay—will be used in this research's analysis. concentrate on keeping an eye on the local area network (LAN); the value is then retrieved following the network's monitoring. When text data transmission on a LAN network was tested, the results indicated that the network quality at SMA Na Najiyah Surabaya was very good, with values of 2.6 Mbps for throughput, o% packet loss, and 0% and 0.12 ms delay.

A Comparative Analysis of K-Nearest Neighbors and Random Forest Methods for Recommendations on Selecting Islamic Boarding Schools Based on Student Interest Profiles (primary and middle school students at xxx) Hamidah, Mas Nurul; Tias, Rahmawati Febrifyaning; Zainal, Rifki Fahrial
NERO (Networking Engineering Research Operation) Vol 10, No 2 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i2.30548

KNN and Random Forest are one of the classification methods, in this study will compare 2 methods in machine learning namely KNN and Random forest to recommend the type of Islamic boarding school based on student interests, the application of a comparison of 2 classification methods in the recommendation system for selecting the type of Islamic boarding school based on student interests at the Elementary and Middle School levels of Xxx, The types of Islamic boarding schools are salafi, khalafi and mixed, with attributes such as academic tendencies, religious interests, extracurricular involvement, and family background. application of machine learning methods to support decision making in selecting Islamic boarding schools that are in accordance with student character, which is still rarely found in Islamic educational institutions. Performance evaluation is carried out using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The test results show that the Random Forest algorithm gives better results with an MAE of 0.23 and an RMSE of 0.57, compared to KNN which has an MAE of 0.6 and an RMSE of 0.96. Thus, Random Forest shown to be more effective in providing recommendations for selecting appropriate Islamic boarding schools, and can be used as a basis for developing a decision support system for Islamic boarding school-based schools.Keywords: KNN, Machine Learning, Random Forest, Islamic boarding schools

Menghitung Sisa Bagi Dari Bilangan Biner Dengan Banyak Digit Tak Terbatas Menggunakan Bagan Finite State Automata (FSA) Purnama, Rangsang; Zainal, Rifki Fahrial
INTER TECH Vol 3 No 2 (2025): INTER TECH
Publisher : Fakultas Teknik Universitas Bhayangkara Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/i.v3i2.1659

Penelitian tentang penggunaan bagan Finite State Automata (FSA) untuk mencari sisa bagi dari dua bilangan dengan digit tak terbatas telah dilakukan di tahun 2024 [1]. Pada penelitian itu bilangan yang akan dicari sisa baginya (pembilang), dan bilangan pembaginya (penyebut), keduanya adalah bilangan desimal. Penelitian ini mencoba menyederhanakan penggambaran bagan FSA untuk proses yang sama. Dalam penelitian ini bilangan yang akan dicari sisa baginya, yang disebut sebagai pembilang, adalah bilangan biner yang hanya memiliki 2 (dua) simbol yaitu 0 dan 1. Adapun untuk bilangan pembagi atau penyebut tetap bilangan desimal. Bilangan desimal penyebut ini akan muncul sebagai nama state pada bagan FSA. Berdasarkan hasil ujicoba yang telah dilakukan, selain terbukti bahwa hasil perhitungan mendapatkan hasil yang benar yang didukung dengan penggunaan aplikasi MS Excel sebagai pembanding hasil perhitungan, penelitian ini juga memperlihatkan bahwa bagan FSA untuk hasil perhitungan sisa bagi yang dihasilkan dari penelitian ini lebih sederhana dibandingkan dengan penelitian terdahulu.

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