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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.

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.

ALGORITMA SHARED NEAREST NEIGHBOR BERBASIS DATA SHRINKING Rifki Fahrial Zainal; Arif Djunaidy
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 1, Januari 2008
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Shared Nearest Neighbor (SNN) algorithm constructs a neighbor graph that uses similarity between data points based on amount of nearest neighbor which shared together. Cluster obtained from representative points that are selected from the neighbor graph. The representative point is used to reduce number of clusterization errors, but also reduces accuracy. Data based shrinking SNN algorithm (SSNN) uses the concept of data movement from data shrinking algorithm to increase accuracy of obtained data shrinking. The concept of data movement will strengthen the density of neighbor graph so that the cluster formation process could be done from neighbor graph components which still has a neighbor relationship. Test result shows SSNN algorithm accuracy is 2% until 8% higher than SNN algorithm, because of the termination of relationship between weak data points in the neighbor graph is done slowly in several iteration. However, the computation time required by SSNN algorithm is three times longer than SNN algoritm computational time, because SSNN algorithm constructs neighbor graph in several iteration.

Analysis of the Indonesian Tourist Destination Recommendation System Using User Profile-Based Collaborative Filtering Hamidah, Mas Nurul; Zainal, Rifki Fahrial; Tias, Rahmawati Febrifyaning; Ardiansyah, Tio Kukuh
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.6

Tourism recommendation systems in Indonesia are challenged by highly heterogeneous user preferences and severe rating sparsity, which undermine the effectiveness of conventional collaborative filtering methods. However, prior studies predominantly rely on rating-based interactions and often utilize generic datasets, limiting their ability to capture the contextual and behavioural diversity of Indonesian tourism. Although user profile information is known to influence preferences, its integration with latent factor models is still fragmented and rarely evaluated in a unified, context-aware framework. Consequently, existing approaches often produce suboptimal accuracy and lack robustness in sparse and imbalanced data environments. This study proposes a unified user profile-enriched collaborative filtering framework that integrates Singular Value Decomposition (SVD), Jaccard similarity, and K-Nearest Neighbor (KNN) to jointly model latent preferences and contextual user characteristics. This integration constitutes the main novelty of this work, enabling simultaneous mitigation of sparsity and enhancement of personalization in a single pipeline. Experiments are conducted on an Indonesian tourism dataset, with performance evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and execution time. The results show that the proposed method consistently outperforms the rating-based baseline, achieving lower MAE (1.6994 vs. 1.7355) and RMSE (2.0653 vs. 2.1148), while maintaining comparable computational efficiency. Furthermore, the model demonstrates greater stability across varying neighbor sizes, indicating improved scalability and robustness. Practically, this approach provides a scalable and context-aware recommendation framework that can support more adaptive and personalized tourism services in Indonesia, particularly in real-world scenarios characterized by sparse and heterogeneous data.

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) Mas Nurul Hamidah; Rahmawati Febrifyaning Tias; Rifki Fahrial Zainal
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

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