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Wilayah perbatasan Ambalat merupakan kawasan strategis NKRI yang rawan terhadap pelanggaran kedaulatan dan ancaman keamanan. Satuan Tugas (Satgas) Ambalat Korps Marinir menghadapi tantangan geografis dan keterbatasan teknologi pengawasan konvensional. Penelitian ini bertujuan menganalisis potensi pemanfaatan teknologi Unmanned Aerial Vehicle (UAV) untuk meningkatkan efektivitas pengamanan perbatasan oleh Satgas Ambalat. Penelitian menggunakan pendekatan deskriptif kualitatif. Teknik pengumpulan data meliputi observasi, wawancara mendalam dengan 4 narasumber (Komandan Skuadron Udara 700, pilot UAV, Dansatgas Ambalat, dan personel komunikasi Satgas Ambalat), serta dokumentasi. Analisis data menggunakan model Miles & Huberman. Penelitian menunjukkan bahwa (1) UAV belum dimanfaatkan secara langsung oleh Satgas Ambalat, pengamanan masih mengandalkan patroli konvensional dan drone non-militer; (2) tantangan utama meliputi keterbatasan personel terlatih, belum adanya program pelatihan formal, keterbatasan sarana prasarana (Ground Control Station, sistem komunikasi), serta kekosongan regulasi operasional; (3) UAV tipe ScanEagle dan Camcopter S-100 dinilai paling sesuai untuk kebutuhan Satgas Ambalat karena daya tahan terbang lama dan sensor canggih. Pemanfaatan UAV dapat memperluas jangkauan pengawasan, menyediakan data real-time, mengurangi risiko personel, serta meningkatkan efisiensi operasional. Diperlukan pendekatan terintegrasi berupa penyusunan regulasi, pengadaan sarana dan pelatihan personel bertingkat dan berlanjut. Teknologi UAV memiliki potensi strategis untuk memperkuat sistem pengamanan perbatasan Ambalat. Rekomendasi meliputi dukungan kebijakan pemerintah, pengembangan doktrin TNI AL, pembentukan unit UAV Korps Marinir, serta pelatihan personel secara berkelanjutan.

Modern food supply chains, particularly those involving essential commodities like rice, often suffer from major challenges such as product fraud, inefficient record-keeping, and a lack of consumer trust. Traditional centralized systems are prone to data tampering, limited transparency, and poor traceability, making it difficult to verify the authenticity and origin of goods. To address these issues, our research introduces TraceRoot, a blockchain-based traceability framework designed to enhance transparency, accountability, and trust in agricultural supply chains.TraceRoot leverages the immutability and decentralization of blockchain technology to maintain a secure, distributed ledger that records every transaction and movement of goods across the supply chain. Each stakeholder including farmers, distributors, retailers, and consumers has role-based access to authenticated data through a user-friendly interface. The framework integrates smart contracts to automate transactions and digital signatures to verify the integrity of the data being uploaded, minimizing the risk of human error or manipulation

This study aims to analyze a comparison of coal stockpile volumes using the DJI Mavic 3 Pro Unmanned Aerial Vehicle (UAV) method versus the truck count method at PT. Mitra Barito. Data collection was conducted through aerial photography using a UAV at altitudes of 60 meters and 70 meters, as well as Ground Control Point (GCP) measurements using GPS. The aerial imagery data was processed using photogrammetry software to generate orthophotos and a Digital Elevation Model (DEM), followed by a geometric accuracy test based on the Geospatial Information Agency Regulation No. 6 of 2018, using the Circular Error 90% (CE90) and Linear Error 90% (LE90) parameters. The research results show that high-quality processing at an altitude of 60 meters yields a CE90 value of 2.1619 meters and an LE90 value of 4.3656 meters, thereby meeting the accuracy standards for RBI maps at a scale of 1:5,000, Class 3 for horizontal accuracy, and a scale of 1:10,000, Class 3 for vertical accuracy. Volume calculations of the stockpile using UAVs yielded a result of 22,750.900 m³, while the truck count method produced a volume of 23,503.300 m³. The volume difference between the two methods was 753.400 m³, with a deviation percentage of 3.2%. Based on the research results, the UAV method is considered capable of providing relatively accurate calculations of coal stockpile volume.

The coal mining industry requires accurate stockpile volume measurements for inventory and production reporting. Conventional methods have limitations in accuracy, efficiency, and safety. This study compares the accuracy and efficiency of coal stockpile volume measurements using a Terrestrial Laser Scanner (TLS) Leica MS60 and an Unmanned Aerial Vehicle (UAV) DJI Matrice 4E, validated by the ASTM D6172-98 standard. Conducted on five Run of Mine (ROM) coal stockpiles covering 13,500 m² at PT XYZ, Lahat, South Sumatra, the TLS method used 43 scan positions, while the UAV employed 430 aerial images with specific flight parameters. Data were processed using Leica Infinity, Surpac, and Agisoft Metashape. The results showed volumes of 94,076 m³ (TLS) and 94,965 m³ (UAV), with a difference of 889 m³ (0.95%). Volume deviations ranged from 0.48% to 1.89%, with an average of 1.42%, all within the ASTM tolerance (<2%). Time efficiency analysis revealed that the UAV method required 200 minutes (3.33 hours), saving 63.3% (approximately 6.17 hours) compared to the TLS method (570 minutes). The largest efficiency gain occurred during field data acquisition, with an 85% reduction in time. This study confirms UAV photogrammetry as a valid, accurate, and efficient alternative for coal stockpile volume measurement in mining.

Measuring coal volume in the Stock ROM area played an important role in production control and mining evaluation. This research was conducted in the Stock ROM area at PT. Victor Dua Tiga Mega, where volume measurements were generally carried out using Total Station (TS) as the main method, howefer the use of Unmanned Aerial Vehicles (UAV) has begun to be implemented as a more efficient alternative. This study aims to compare the results of Stock ROM coal volume calculations using the Total Station and UAV methods. The research method was carried out by collecting data in the field, processing the digital Elevation Model (DEM), and calculating the volume using the Cut and Fill method. The results of the study the difference in volume between the two methods, where the UAV measurement results tend to be greater than those of the Total Station. The difference in Fine Coal volume was 724,15 m3 or 16,74% and Raw Coal volume of 9.335,98 m3 or 8,03%. Based on a comparison with weighing data, measurements using the Total Station provided results that were closer to the actual conditions in the field.