Pengembangan Web-Based Dashboard untuk Deteksi Umur dan Status Tanam Pohon pada Perkebunan Kelapa Sawit
Article Sidebar
Main Article Content
Abstract
Adanya kebutuhan terhadap tools untuk pendeteksian kelapa sawit agar dapat digunakan untuk melakukan monitoring, menjadi dasar permasalahan dilakukannya penelitian ini. Tujuan utama dari penelitian ini adalah pembangunan dashboard sederhana sebagai alat implementasi dari model estimasi umur dan status tanam kelapa sawit. Dashboard dibangundengan menggunakan framework Streamlit yang berbasis bahasa pemrograman Python. Dashboard ini memiliki dua halaman, yaitu halaman Home dan halaman pendeteksian. Pada halaman Home, pengguna dapat membaca tutorial penggunaan dashboard dan mengunduh sampel gambar untuk melakukan pendeteksian. Halaman pendeteksian menampilkan informasi evaluasi testing model pendeteksian sekaligus sebagai tempat bagi user untuk mengunggah gambar yang ingin dideteksi. Dashboard ini berpotensi untuk dikembangkan guna memantau pertumbuhan kelapa sawit.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.
References
E. Agasta, “Prediksi Jumlah Produksi Kelapa Sawit Dengan Menggunakan Metode Extreme Learning Machine ( ELM ) ( Studi kasus: PT . Sandabi Indah Lestari Kota Bengkulu ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 5751–5759, 2018.
Y. C. Putra, A. W. Wijayanto, and G. A. Chulafak, “Oil palm trees detection and counting on Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicles (UAV) data using image processing thresholding approach,” Ecol. Inform., 2022.
M. V. Chiriacò, M. Bellotta, J. Jusić, and L. Perugini, “Palm oil’s contribution to the United Nations sustainable development goals: Outcomes of a review of socio-economic aspects,” Environ. Res. Lett., vol. 17, no. 6, 2022, doi: 10.1088/1748-9326/ac6e77.
A. Syahza, “Effort to Spur Economic Growth in Rural Areas,” Int. Reseacrh J. Bus. Stud., vol. 4, no. 3, pp. 171–188, 2011.
Y. C. Putra and A. W. Wijayanto, “Automatic detection and counting of oil palm trees using remote sensing and object-based deep learning,” Remote Sens. Appl. Soc. Environ., vol. 29, no. July 2022, p. 100914, 2023, doi: 10.1016/j.rsase.2022.100914.
A. C. Fitrianto, A. Darmawan, K. Tokimatsu, and M. Sufwandika, “Estimating the age of oil palm trees using remote sensing technique,” IOP Conf. Ser. Earth Environ. Sci., vol. 148, no. 1, 2018, doi: 10.1088/1755-1315/148/1/012020.
T. M. Liliesand and R. W. Kiefer, “Remote Sensing and Image Interpretation,” Geol. Mag., vol. 132, no. 2, 1994, doi: DOI: 10.1017/S0016756800012024.
J. Zheng et al., “Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images,” ISPRS J. Photogramm. Remote Sens., vol. 173, no. August 2020, pp. 95–121, 2021, doi: 10.1016/j.isprsjprs.2021.01.008.
Badan Pusat Statistik, Statistik Kelapa Sawit Indonesia 2021. 2021. [Online]. Available: https://www.bps.go.id/publication/2020/11/30/36cba77a73179202def4ba14/statistik-kelapa-sawit-indonesia-2019.html
I. H. Al Amin and F. H. Arby, “Implementation of YOLO-v5 for a Real Time Social Distancing Detection,” J. Appl. Informatics Comput., vol. 6, no. 1, pp. 01–06, 2022, doi: 10.30871/jaic.v6i1.3484.
R. Nasution and A. Muliani, “Web-Based Inventory Data Processing Information System At The Regional Development Planning Agency (Bappeda) North Sumatra Province,” J. Inf. Syst. Technol. Res., vol. 1, no. 1, pp. 32–41, 2022, doi: 10.55537/jistr.v1i1.95.
L. Setiyani, “Desain Sistem: Use Case Diagram,” Pros. Semin. Nas. Inov. Adopsi Teknol. 2021, no. September, pp. 246–260, 2021, [Online]. Available: https://journal.uii.ac.id/AUTOMATA/article/view/19517
I. K. Raharjana and A. Justitia, “Pembuatan Model Sequence Diagram Dengan Reverse Engineering Aplikasi Basis Data Pada Smartphone Untuk Menjaga Konsistensi Desain Perangkat Lunak,” JUTI J. Ilm. Teknol. Inf., vol. 13, no. 2, p. 133, 2015, doi: 10.12962/j24068535.v13i2.a482.
S. Sandfreni, M. B. Ulum, and A. H. Azizah, “Analisis Perancangan Sistem Informasi Pusat Studi Pada Fakultas Ilmu Komputer Universitas Esa Unggul,” Sebatik, vol. 25, no. 2, pp. 345–356, 2021, doi: 10.46984/sebatik.v25i2.1587.
L. Baresi, “Activity Diagrams BT - Encyclopedia of Database Systems,” L. LIU and M. T. ÖZSU, Eds. Boston, MA: Springer US, 2009, pp. 41–45. doi: 10.1007/978-0-387-39940-9_9.
M. Ehmer and F. Khan, “A Comparative Study of White Box, Black Box and Grey Box Testing Techniques,” Int. J. Adv. Comput. Sci. Appl., vol. 3, Jun. 2012, doi: 10.14569/IJACSA.2012.030603.
Nurmasari, Y., Wijayanto, A. W., 2021, Oil Palm Plantation Detection in Indonesia using Sentinel-2 and Landsat-8 Optical Satellite Imagery (Case Study: Rokan Hulu Regency, Riau Province), International Journal of Remote Sensing and Earth Sciences (IJReSES), 18(1), pp. 1-18
Wijayanto, A. W., Afira, N., Nurkarim, W., 2022, Machine Learning Approaches using Satellite Data for Oil Palm Area Detection in Pekanbaru City, Riau, Proceedings of the 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)
Nurkarim, W., Wijayanto, A. W., 2023, Building footprint extraction and counting on very high-resolution satellite imagery using object detection deep learning framework, Earth Science Informatics, 16, pp. 515-532
Zalukhu, B. V. R., Wijayanto, A. W., Habibie, M. I., 2022, Marine Vessels Detection on Very High-Resolution Remote Sensing Optical Satellites using Object-Based Deep Learning, 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 149-154