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All Journal Journal of Information Systems Engineering and Business Intelligence Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Edsence: Jurnal Pendidikan Multimedia Building of Informatics, Technology and Science Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Innovation Information Technology and Application (JINITA) Jurnal Pengabdian UNDIKMA Jurnal Difusi Journal of Software Engineering, Information and Communication Technology Jurnal Pengabdian Masyarakat IPTEK Green Intelligent Systems and Applications SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal Informatika Polinema (JIP) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Gelar, Trisna
Politeknik Negeri Bandung (Polban)
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Improving SAM 2 for Agricultural Land Segmentation through Fine-Tuning, Point Prompt Augmentation, and Negative Prompt Calibration Yayang Setia Budi; Fardan Al Jihad; Nurjannah Syakrani; Trisna Gelar; Muhammad Rizqi Sholahuddin; Djoko Cahyo Utomo Lieharyani
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.210-222

Abstract

Background: The Segment Anything Model 2 (SAM 2) represents a state-of-the-art foundation model for object segmentation; however, its application to satellite-based agricultural mapping faces significant challenges. Standard SAM 2 architectures often struggle with the spectral ambiguity of fragmented tropical landscapes and the domain gap inherent in remote sensing imagery. Furthermore, the model’s interactive nature requires precise spatial guidance, making it sensitive to both the location and density of input prompts, which limits its scalability for automated large-scale monitoring. Objective: This study aims to (1) analyze the impact of domain-specific fine-tuning combined with automated Point Prompt Augmentation (PPA) and Negative Prompt Calibration (NPC) on segmentation accuracy; (2) evaluate the performance of four SAM 2 variants (Tiny, Small, Base+, and Large) to identify the optimal backbone for agricultural tasks; and (3) determine the optimal prompt density for both positive and negative points. Methods: The SAM 2 variants were fine-tuned using the LoveDA satellite dataset. Evaluation was conducted through an automated pipeline comparing two initialization strategies: Largest Agricultural Area (LAA) Centroid and random placement. The study implemented PPA to strategically increase positive prompt density and NPC to suppress "mask leakage" into irrigation infrastructure. Performance was quantified using mean Intersection over Union (mIoU) and Jaccard & F-measure (J&F) metrics. Results: The Small variant emerged as the superior backbone, achieving a peak mIoU of 0.7255 and J&F of 0.7734, representing a significant improvement over the pretrained baseline. The results indicate that the LAA Centroid strategy provides a more stable spatial anchor, while the integration of three positive and three negative points optimized the boundary alignment. The Small variant maintained a high computational efficiency with an average inference time of 2.62 minutes. Conclusion: Domain-specific fine-tuning, coupled with the proposed PPA and NPC frameworks, successfully mitigates the limitations of SAM 2 in agricultural remote sensing. This research provides a robust methodology for automated, high-precision land segmentation, bridging the gap between foundation models and specialized geographic information systems.   Keywords: Agriculture Segmentation, Satellite Imagery, Segment Anything Model 2, Fine-tuning, Point Prompt Augmentation, Negative Prompt Calibration
YOLOv8 to YOLO11 Performance Benchmark and Comprehensive Architectural Comparative Review Priyanto Hidayatullah; Nurjannah Syakrani; Muhammad Rizqi Sholahuddin; Trisna Gelar; Refdinal Tubagus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6598

Abstract

In the domain of deep learning-driven computer vision, YOLO is revolutionary. However, not all YOLO models are accompanied by academic articles and architectural diagrams. It complicates the comprehension of the model's operation. Moreover, the existing review papers fail to examine each model comprehensively. This work aims to provide a thorough comparative analysis of the architectures from YOLOv8 to YOLO11, allowing readers to swiftly understand the operational mechanisms and differences among the models. We analyzed the architecture of each YOLO version by reviewing relevant scholarly articles, official documentation, and examining the source code. In particular, we discovered that YOLOv8 through YOLO11 differ in novelty while sharing similarities in the anchor-free and Non-Maximum Suppression (NMS) aspects, except YOLOv10 (NMS-free). Each also has drawbacks, such as differing levels of complexity in the way features are connected (v8), architectural structure and training (v9), training methods or dual assignments (v10), inference, and code implementation (v11). While each version improves architecture, some blocks remain unchanged. This study helps readers understand different YOLO version architectures and inspires how to improve their performance. It also provides readers with a comprehensive architecture diagram and detailed descriptions of each block, serving as a reference for both academic and practical applications. In terms of performance, a benchmark using the Roboflow 100 dataset reveals that YOLOv9 achieves superior accuracy; however, it is eight times slower owing to its NMS mechanism. YOLOv10 is the fastest but least accurate, whereas YOLOv8 and YOLO11 provide a balanced compromise between speed and accuracy.
Peningkatan Kompetensi Abstraksi dan Dekomposisi Computational Thinking Melalui LKPD Digital dan Card Mat Modular bagi Guru SMPN 1 Baleendah Trisna Gelar; Lukmannul Hakim Firdaus; Gema Rullyana
Jurnal Pengabdian UNDIKMA Vol. 7 No. 2 (2026): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i2.18540

Abstract

This community service program aims to enhance the pedagogical competence of teachers at State Junior High School 1 Baleendah in applying the concepts of Abstraction and Decomposition in Computational Thinking (CT) within the learning process, while improving operational efficiency in managing teaching materials through the utilization of structured digital systems and media. The program was implemented using a participatory approach with an integrated hybrid intervention. The effectiveness of the intervention was evaluated using a one-group pretest-posttest design with cognitive understanding tests and rubric-based performance observation sheets, which were subsequently analyzed using descriptive quantitative methods. The results showed that the hybrid model was effective in overcoming pedagogical barriers, as evidenced by a significant improvement in conceptual understanding and the quality of CT implementation in learning activities, with an average post-test score increase of 15%. In addition, operational inefficiency was addressed through improved time efficiency in preparing teaching materials, reaching an increase of at least 60%. This model also generated findings regarding the effectiveness of Hexagon Chips as cognitive artifacts for mediating the visualization of the concepts of Abstraction and Decomposition in Computational Thinking.
Co-Authors Abdurrahman, Muhammad Syahid Ade Hodijah Ais Laksana Aprianti Nanda Sari Athiyya Rahmawati Auliarahmi, Rizka Bakhrun, Akhmad Budiman Cholid Fauzi Daffa Muzhaffar Didik Suwito Pribadi Djoko Cahyo Utomo Lieharyani Djoko Cahyo Utomo Lieharyani Eddy Bambang Soewono Fabillah, Dzakira Fardan Al Jihad Firdaus, Lukmannul Hakim Gema Rullyana Ghifari Munawar Gianluigi Julian Hamdani, Nisrina Wafa Zakiya Hayati, Hashri Hilmy Oktoharitsa Itikap, Sabar Muhamad Iwan Awaludin Jonner Hutahaean Kadhafi, Irvan Lukmannul Hakim Firdaus Lukmanul Hakim Firdaus Melinia, Gina Mugi Pangestu Muhammad Fikri Muhammad Riza Alifi Muhammad Rizqi Sholahuddin Muhammad Rizqi Solahudin Nandhifa Azzahra Naufal Taufik Naylassana, Fauza Nurjannah Syakrani Nurjannah Syakrani Permana, Heru Pratidina, Caturiani Priyanto Hidayatullah Rachmat, Setiadi Raditya Pasya Rafli Fadhilah Raihan Fuad Ratna Wulan, Sri Refdinal Tubagus Ridho Wiratama, Anshar Safira, Decia Sari, Aprianti Nanda Satryo Haryo Sitepu, Rezky Wahyuda Siti Dwi Setiarini Sofy Fitriani Suprihanto Suprihanto Syakrani, Nurjannah Urip Teguh Setijohatmo Wisnuadhi, Bambang Yayang Setia Budi