Article Per Year (5 Year)
p-Index From 2021 - 2026
0.961
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Pengabdian kepada Masyarakat Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Computers and Digital Business Journal of Computing Innovations and Emerging Technologies
Muhammad Akbar Syahbana Pane
Universitas Negeri Medan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Classification of Plant Pests Using the Real-Time Detection Transformer (RT-DETR) Algorithm in Oil Palm Plants Bayu Ath Thariq Syams; Ratu Mutiara Siregar; Muhammad Akbar Syahbana Pane
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27029

Abstract

Plant Pests (PP) are one of the main factors causing a decline in oil palm productivity. This study focuses on the classification of PP, limited to the three main pest species: the rhinoceros beetle (Oryctes rhinoceros), the fireworm (Setora nitens), and the Tioman rat (Rattus tiomanicus). The dataset consists of digital images representing these three pest types. The method used is the Real-Time Detection Transformer (RT-DETR), which is capable of real-time, end-to-end object detection. The research stages include data collection, preprocessing, model training, and evaluation using confusion matrix, precision, recall, and F1-score metrics. The research results are expected to produce an accurate and efficient crop pest classification system to support decision-making in pest management in oil palm plantations.
EMPOWERING PRIMARY EDUCATORS IN THE AI ERA: AN EVALUATION OF GEMINI AI TRAINING SATISFACTION AT AN-NIZAM PRIMARY SCHOOL Ariatna Ariatna; Ricky Drimarcha Barus; Adi Widarma; Muhammad Akbar Syahbana Pane; Ayu Lestari
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Juni 2026
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid development of Generative Artificial Intelligence (GenAI) has transformed primary education by offering solutions to administrative burdens and creating new opportunities for innovative instructional design. This study aimed to measure the professional satisfaction and perceived usefulness of an intensive Gemini AI prompt engineering training and mentoring program among 23 homeroom teachers at SD An-Nizam. The program focused on utilizing structured prompting techniques to create Deep Learning materials based on the Task-Based Language Teaching (TBLT) approach, specifically embodying Mindful, Meaningful, and Joyful (MMJ) principles. By applying specialized tools such as Nano Banana for educational illustrations, Gemini Canvas for task-based classroom games, and Deep Research for robust lesson planning (RPP), the training guided teachers in building a sustainable Prompt Library. Using a descriptive survey design with a 5-point Likert scale and open-ended questions, the results showed a highly positive reception toward the training content and instructor quality. A large majority of teachers (19 respondents) reported feeling significantly more confident in applying Generative AI-driven instructional design. Qualitative feedback highlighted that specific tool like Nano Banana effectively simplified complex concepts for younger students by combining text and vibrant images. The study concludes that this training successfully improved educators' prompt engineering skills and instructional efficiency, providing a useful blueprint for future professional development that incorporates peer-mentoring and localized AI integration in primary schools. While these results are promising, the study is limited by its small, single-site sample size and a focus on immediate outcomes rather than long-term pedagogical shifts. Nevertheless, these findings establish a foundation for larger-scale implementations, emphasizing that sustainable AI adoption in early education depends on human-centered support and context-specific curriculum design.
The Relationship Between Blended Learning Experience and Students' Problem-Solving Skills: A Quantitative Correlational Study Novialdi Ashari; Muhammad Akbar Syahbana Pane; Ulfah Oktarida Sihaloho
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.29

Abstract

   
KLASIFIKASI KADAR C-ORGANIK TANAH BERBASIS CITRA DIGITAL MENGGUNAKAN SUPPORT VECTOR MACHINE Pamitta Maulina Sijabat; Andi Prayogi; Muhammad Akbar Syahbana Pane
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7911

Abstract

Soil organic carbon is an important indicator of soil fertility and health, particularly in oil palm plantations. Conventional laboratory methods for organic carbon measurement are costly, time-consuming, and less practical for routine monitoring. This study aims to develop a digital image-based classification model for organic carbon levels using the Support Vector Machine (SVM) algorithm. A total of 96 soil images from the ITSI practice plantation were collected from three soil layers up to a depth of 60 cm. Feature extraction was performed using HSV Color Moment and Gray Level Co-occurrence Matrix (GLCM), producing 15 features per image. The SVM model with RBF kernel was optimized using GridSearchCV, while class imbalance was handled using SMOTE. Experimental results showed an overall accuracy of 60%, precision of 68.57%, recall of 60%, and F1-score of 58.53%. The highest accuracy was obtained in the 0–20 cm and 20–40 cm layers at 57.14%, while the 40–60 cm layer achieved the lowest accuracy at 42.86%. The results indicate that deeper soil layers have more similar visual characteristics, making classification more difficult. This study demonstrates the potential of combining SVM and digital image processing as a low-cost and environmentally friendly alternative for organic carbon monitoring in oil palm plantations.
Perbandingan Model Spasial Kesesuaian Lahan Kelapa Sawit di Pulau Sumatera Menggunakan Algoritma Machine Learning Ferdy Hardiansyah; Ratu Mutiara Siregar; Muhammad Akbar Syahbana Pane; Andi Prayogi
Journal of Computers and Digital Business Vol. 5 No. 2 (2026)
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i2.987

Abstract

Pulau Sumatera merupakan salah satu wilayah utama pengembangan kelapa sawit di Indonesia dengan karakteristik biofisik yang kompleks. Pemanfaatan lahan yang tidak mempertimbangkan kesesuaian biofisik berpotensi menurunkan produktivitas dan meningkatkan degradasi lingkungan. Penelitian ini bertujuan mengintegrasikan pendekatan berbasis aturan FAO dengan metode machine learning untuk memodelkan kesesuaian lahan kelapa sawit secara lebih interpretatif. Algoritma Decision Tree digunakan untuk mempelajari pola klasifikasi dari kriteria FAO dan dibandingkan dengan K-Nearest Neighbor (KNN). Variabel penelitian meliputi kemiringan lereng, curah hujan, suhu udara, pH tanah, tekstur tanah, kedalaman tanah, dan tutupan lahan. Dataset diperoleh dari ekstraksi data raster ke format tabular dengan pembagian data latih dan uji sebesar 80:20. Hasil penelitian menunjukkan kelas S2 mendominasi wilayah penelitian sebesar 61,06%, diikuti S3 sebesar 18,46%, S1 sebesar 14,26%, dan N sebesar 6,22%. Evaluasi cross-validation menunjukkan akurasi Decision Tree sebesar 88,94% dan KNN sebesar 87,18%. Decision Tree memiliki performa lebih stabil dan mudah diinterpretasikan. Penelitian ini menunjukkan integrasi FAO dan machine learning dapat mendukung perencanaan penggunaan lahan yang lebih objektif, transparan, dan berkelanjutan.
Performance Evaluation of YOLOv9, YOLOv10, and YOLOv11 for Real-Time Early Detection of Ganoderma Boninense in Oil Palm Rizky Delianngi; Ratu Mutiara Siregar; Nurliana; Muhammad Akbar Syahbana Pane; Phaklen Ehkan; Andi Prayogi
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.v10i2.7479

Abstract

Early detection of Ganoderma boninense infection is essential to reduce yield losses in oil palm plantations. This study aims to evaluate the performance of three recent YOLO architectures, namely YOLOv9, YOLOv10, and YOLOv11, for real-time detection of early infection symptoms under natural field conditions. A dataset of 2,000 annotated RGB images was used with a 70:20:10 split for training, validation, and testing. Model performance was evaluated using precision, recall, F1-score, mean average precision (mAP50 and mAP50–95), and inference speed. The results show that YOLOv9 achieved the highest detection accuracy with an mAP50 of 0.989 and F1-score of 0.968. Meanwhile, YOLOv11 demonstrated the best computational efficiency with an inference speed of 35 FPS and processing time of 28.5 ms per frame. These findings indicate a trade-off between accuracy and speed, where YOLOv9 is suitable for accuracy-oriented applications, while YOLOv11 is more appropriate for real-time deployment in precision agriculture.
Co-Authors Adi Widarma Andi Prayogi Ariatna Ariatna Ayu Lestari Bayu Ath Thariq Syams Ferdy Hardiansyah Novialdi Ashari Nurliana Pamitta Maulina Sijabat Phaklen Ehkan Ratu Mutiara Siregar Ricky Drimarcha Barus Rizky Delianngi Ulfah Oktarida Sihaloho