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All Journal JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Danang Surya Candra, Danang Surya
Pusat Teknologi dan Data Penginderaan Jauh - LAPAN
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Environmental Science

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PEMBUATAN SISTEM INFORMASI GEOGRAFIS KESESUAIAN LAHAN TANAMAN TEBU BERBASIS WEB DI KABUPATEN MERAUKE Marwato, Marwato; Candra, Danang Surya
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 4 No. 1 (2007)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v4i1.3195

Abstract

This research is aims to have develop land resource information system for a spatial management and land use allocation by commudity development based on web and land suitable evaluation for sugar cane in Merauke Regency, Papua Province. The method which is used to organized land resource information system in development of area commudity (sugar cane) is automatization evaluation land suitability is to detect potency area of sugar cane with combined remote sensing technology and information technology based on web. The result of the evaluation land suitability for sugar cane in Merauke Regency, for the most extremely suitable land (S1) are Kl9maam Island (19,3291 hectare), Merauke (11,550 hectare), Kurik (7,746 hectare) and Semangga (524 hectare).
DENOSING OF HIGH RESOLUTION REMOTE SENSING DATA USING STATIONARY WAVELET TRANSFORM Candra, Danang Surya
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 5 No. 1 (2008)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v5i1.3234

Abstract

PEMETAAN BATAS PETAK LAHAN SAWAH PERSIL MENGGUNAKAN ALGORITMA DEEP LEARNING DI KECAMATAN KEPANJEN, KABUPATEN MALANG, JAWA TIMUR Savitri, Elvin; Putra, Aditya Nugraha; Candra, Danang Surya
JTSL (Jurnal Tanah dan Sumberdaya Lahan) Vol. 13 No. 1 (2026)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2026.013.1.19

Abstract

This research was conducted to mapping the boundaries of paddy fields in Kepanjen District, Malang Regency, using the U-Net deep learning model based on Convolutional Neural Network (CNN) for image classification per pixel. The input used was WorldView-2 imagery, and the model was trained with 25, 75, and 150 epochs variety to evaluate its performance in accurately classifying paddy field use down to the parcel scale. The 75 epochs variation model was used for image classification because it has balance between model performance and training period. Validation test were conducted using paired T-test to identify statisfically significant differences between the classified image paddy area and the actual field conditions. The results showed that U-Net model with various epoch variations did not differ significantly in terms of performance, the time taken for the model to learn the dataset per epoch, or accuracy, which reached 90%. The model was able to accurately classify paddy and non-paddy land use in WorldView-2 imagery down to the plot scale. Based on WorldView-2 image segmentation to identify paddy field boundaries, the paddy field area is 1217 Ha. The results were validated by comparing the area of rice fields in the image with the actual area of rice fields in the field. Validation test result with actual paddy area showed a calculated T (0,5486) lower than the T table (1,9603) and a p value (0,5833) greather than 0,05. This indicates no significant mean difference between the two data sets (ground check and imagery). The lack of significant difference provides strong evidence that the U-Net model is effective for mapping paddy plot boundaries on a large scale.
Co-Authors Aditya Nugraha Putra, Aditya Nugraha Marwato, Marwato Savitri, Elvin