Article Per Year (5 Year)
p-Index From 2020 - 2025
0.888
P-Index
This Author published in this journals
All Journal POSITRON Jurnal Natural Aceh International Journal of Science and Technology Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Wirandha, Freddy Sapta
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Environmental Science Immunology & microbiology Neuroscience Physics Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Analisis Normalized Difference Vegetation Index pada Unmanned Aerial Vehicle untuk Identifikasi Pertumbuhan Tanaman Padi di Kabupaten Aceh Besar Provinsi Aceh Marwan, Marwan; Wirandha, Freddy Sapta; Husna, Mayriska; Saria, Cut Oktavia; Nizzamuddin, Nizzamuddin; Yanis, Muhammad
POSITRON Vol 11, No 2 (2021): Vol. 11 No. 2 Edition
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/positron.v11i2.48102

Abstract

Teknologi unmanned aerial vehicle (UAV) berkembang pesat saat ini untuk berbagai kebutuhan, termasuk dalam bidang pertanian. Salah satu pemanfaatan UAV dalam bidang pertanian yaitu untuk mengamati tingkat kesuburan tanaman padi (Oryza sativa L). UAV mampu menerbangkan berbagai jenis kamera dan sensor, salah satunya adalah sensor normalized difference vegetation index (NDVI) mapir survey 3. Sensor NDVI ini menghasilkan nilai multispektral yang digunakan untuk menghitung selisih indeks vegetasi yang ternormalisasi. Nilai indeks vegetasi tersebut mencerminkan tingkat kesuburan tanaman padi. Tujuan dari penelitian ini untuk mengklasifikasikan tingkat kesuburan tanaman padi secara cepat dengan menggunakan metode unsupervised dan memperoleh luas lahan masing-masing indeks vegetasi yang telah dikelaskan.  Tahapan awal penelitian ini adalah melakukan pengambilan data NDVI dengan menggunakan UAV yang telah terpasang sensor NDVI. Hasil foto udara tersebut digabungkan dengan proses  mozaik citra dan klasifikasi kelas hasil analisis NDVI. Tahap akhir adalah melakukan klasifikasi hasil analisis NDVI untuk menghitung luasan daerah atau lahan masing-masing kelas NDVI. Hasil klasifikasi unsupervised menunjukkan 4 indeks kelas yaitu kelas indeks non vegetasi dengan nilai NDVI yaitu –0,51 hingga –1 dan luas lahan 1,462845 Ha, kelas vegetasi rendah dengan nilai NDVI yaitu –0,5 hingga –0,01 dan luas lahan 3,412978 Ha, kelas vegetasi sedang dengan nilai NDVI 0,01 hingga 0,49 memiliki luas lahan 4,589696 Ha dan kelas vegetasi tinggi dengan nilai NDVI 0,51 hingga 1 memiliki luas lahan 5,744039 Ha.
PELATIHAN PENGELOLAAN SUMBER DAYA GEOLOGI DI GAMPONG BARO, ACEH BESAR, UNTUK MENDUKUNG PELESTARIAN LINGKUNGAN Muhammad Ridha Adhari; Rifqan, Rifqan; Dewi Sartika; Gunarsih, Dina; Wirandha, Freddy Sapta
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 2 No. 4 (2024): Agustus
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v2i4.1183

Abstract

Kegiatan pengabdian kepada masyarakat dilaksanakan di Desa Gampong Baro, Kabupaten Aceh Besar, Provinsi Aceh. Daerah kegiatan pengabdian ini memiliki kenampakan bentang alam yang unik dan indah seperti pantai, karst, pegunungan, dan dataran alluvial. Kondisi bentang alam yang ada dilokasi pengabdian telah dimanfaatkan sebagai daerah pemukiman warga oleh penduduk sekitar. Akan tetapi, penduduk setempat belum memahami pentingnya penerapan praktik terhadap pengelolaan yang baik untuk mendukung keberlanjutan bentang alam tersebut. Untuk mengatasi masalah ini, kami melaksanakan kegiatan pengabdian kepada masyakarat di desa tersebut denfan dua tujuan, yakni : (1) untuk mendidik penduduk desa denfan pengetahuan geologi yang relevan; dan (2) untuk melakukan pelatihan tentang praktik pengelolaan yang baik untuk mendukung keberlanjutan pemanfaatan bentang alam di Desa Gampong Baro. Untuk mencapai tujuan ini, kami menggunakan teknik analisis kualitatif seperti observasi, wawancara, dan kuesioner. Temuan kami menunjukkan hasil bahwasanya penduduk Desa Gampong Baro tidak begitu memahami terkait konsep geologi, tetapi mereka ingin belajar dan memiliki komitmen yang kuat untuk mencoba memahami ceramah yang diberikan kepada mereka. Selain itu, penduduk desa sangat antusias untuk mengikuti pelatihan yang kami berikan. Diharapkan kegiatan pengabdian kepada masyakarat ini dapat memberikan manfaat bagi masyarakat Desa Gampong Baro dalam mengelola bentang alam geologi yang ada di desanya. Kata Kunci : Bentang Alam, Pengabdian, Geologi, Pengelolaan
Utilization of Unmanned Aerial Vehicle (UAV) for Topographic Survey Using Ground Control Points (GCP) from Geodetic GNSS Nizamuddin, Nizamuddin; Wirandha, Freddy Sapta; Ardiansyah, Ardiansyah
Aceh International Journal of Science and Technology Vol 12, No 1 (2023): April 2023
Publisher : Graduate School of Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.1.31606

Abstract

Mapping with UAVs is a large-scale mapping strategy or method that can shorten the implementation time compared to conventional survey methods that sometimes cannot represent an area as a whole. Aerial photos recorded by drones generally still have a level of position error of up to several meters, so tie points are needed to improve the accuracy of aerial photos. This research aims to utilize drone camera technology to produce geospatial data, both in the form of raster data from aerial photographs and vector data in the form of land contours showing the height of the land surface in Gampong Cot, Gampong Blang, Krueng Kalee, and Angan in Darussalam District, Gampong Durung and Neuheun in Masjid Raya District, Gampong Labuy and Lam Ujong in Baitussalam District. Aerial photo processing produces highresolution image data of 5 cm/pixel. Ground Control Points (GCPs) are used as tie points to improve the accuracy of aerial imagery. GCP measurements use geodetic GNSS, which has an accuracy of cm to mm. Aerial photos that previously had an error rate of 2-7 meters, after being bound with GCP points, the error rate decreased to below 1 meter. Aerial photographs also show that the land cover in the study area is partly forest. Some areas in the west and north are C excavation mining areas. Based on the contour extraction results, the ground elevation in block 1 (Gampong Durung, Gampong Cot, Gampong Angan, and Gampong Lam Ujong) is 66 to 301 meters, block 2 (Gampong Neuhen) is 50 to 260 meters, and block 3 (Gampong Lam Ujong and Labuy) is 90 to 188 meters.
Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques ADHARI, MUHAMMAD RIDHA; WIRANDHA, FREDDY SAPTA; YANIS, MUHAMMAD; KARDAWI, MUHAMMAD YUSUF
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v25i3.47470

Abstract

Many scientific fields, including the geosciences, have successfully employed machine learning to address numerous significant issues. Current studies show that the application of machine learning within the geosciences is still in its early stages, and there is a huge potential for this technique that need to be explored. This research focuses on the Late Permian Beekeeper Formation from the Perth Basin, Australia. It aims to improve our understanding of the application of machine learning to characterise subsurface rock formations. The objectives of this study are threefold: (1) to conduct cutting, crossplot, and modern machine learning analyses on a mixed carbonate-siliciclastic reservoir; (2) to compare the results from the aforementioned analyses and to interpret the electrofacies and lithofacies; and (3) to understand the degree of accuracy of the application of machine learning in the characterisation of the subsurface rock formations. Cutting, crossplotting, and modern machine learning analyses have been conducted to achieve the aim and objectives of this study. Seven electrofacies, associated with nine lithofacies, were identified within the studied data, and these were classified into carbonate-dominated facies group, siliciclastic-dominated facies group, and mixed carbonate-siliciclastic facies group. Results also show the presence of stratal and compositional mixing within the Beekeeper Formation. A combination of cutting, crossplot, and machine learning analyses can provide a better, more accurate, and more reliable interpretation of the facies of the Beekeeper Formation. This study is expected to advance our understanding of the application of machine learning in geosciences.
Co-Authors ARDIANSYAH ARDIANSYAH Dewi Sartika Gunarsih, Dina Husna, Mayriska Kardawi, Muhammad Yusuf Marwan Marwan Muhammad Ridha Adhari Muhammad Ridha Adhari, Muhammad Ridha Muhammad Yanis Nizamuddin Nizamuddin Nizzamuddin, Nizzamuddin Rifqan, Rifqan Saria, Cut Oktavia