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All Journal Journal of Tropical Soils JTSL (Jurnal Tanah dan Sumberdaya Lahan) Journal of Degraded and Mining Lands Management Habitat Jurnal Pengelolaan Laboratorium Pendidikan Jurnal Ilmiah Wahana Pendidikan Jurnal HPT (Hama Penyakit Tumbuhan)
Aditya Nugraha Putra, Aditya Nugraha
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Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Education Environmental Science Social Sciences Other

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Penerapan Desain dalam Feeds dan Reels Instagram Terhadap Antusiasme Penonton dalam Berdonasi Putra, Aditya Nugraha; Romadhona, Mahimma
Jurnal Ilmiah Wahana Pendidikan Vol 10 No 12 (2024): Jurnal Ilmiah Wahana Pendidikan 
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.12176129

Abstract

On World of Design, Design is used to make a person knowing the acknowledge about design, sometimes it used to be a message or a conveys to the world, like this journal which was written to be a guide for people to spread kindness and to teach people which design is compatible to a message, so many people don’t be outguided with the wrong guide of design basic
EVALUASI KESESUAIAN LAHAN KELAPA SAWIT PADA LAHAN GAMBUT DI PESISIR SELATAN, SUMATERA BARAT Arifin, Syamsul; Nugraha, Irfan Nur Priya; Al Jauhary, Muhammad Rifqi; Putra, Aditya Nugraha; Riza, Sativandi; Setiawan, Adi; Andhika, Yosi; Adril, Adril; Rayes, Mochtar Luthfi
JTSL (Jurnal Tanah dan Sumberdaya Lahan) Vol. 13 No. 1 (2026)
Publisher : Departemen Tanah, Fakultas Bio-industri Pertanian dan Kehutanan, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2026.013.1.4

Abstract

Indonesia's current population growth rate has reached 1.13% per year, leading to increased demand for land and consequent land use conversion, including peatlands. The conversion of peatlands into oil palm plantations raises several negative issues, such as increased carbon emissions. Therefore, it is crucial to consider the characteristics of peatlands when determining their suitability for crop cultivation. This study aims to assess peat quality, nutrient retention, and soil classification in the research area and evaluate the suitability of peatlands for oil palm cultivation in Pancung Soal. The research was conducted in an oil palm plantation in Nagari Tluk Amplu Inderapura, West Sumatra, using a survey method and parameters such as peat depth, ash content, and pH. The boundaries of land suitability classes were determined based on oil palm production results. The findings indicate variations in peat quality, nutrient retention, and suitability for oil palm cultivation. The analysis initially reveals land suitability classes ranging from S3 to N. However, with modifications to certain parameters, the suitability classes can be improved to S2 and S3. These results approximate suitability based on actual field productivity. Boundary delineation for specific parameters is necessary to enhance land suitability for oil palm in the region.
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 Bio-industri Pertanian dan Kehutanan, 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.
PEMETAAN DISTRIBUSI SPESIES POHON BERBASIS DEEP LEARNING DAN PENGINDERAAN JAUH DI KEBUN RAYA PURWODADI Pratiwi, Nisrina Salwa; Putra, Aditya Nugraha; Latifah, Evy
JTSL (Jurnal Tanah dan Sumberdaya Lahan) Vol. 13 No. 1 (2026)
Publisher : Departemen Tanah, Fakultas Bio-industri Pertanian dan Kehutanan, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2026.013.1.3

Abstract

Indonesia is rich in germplasm, with approximately 38,000 plant species spread across the archipelago. However, significant deforestation has led to a decline in species distribution, with deforestation rates reaching 1 to 1.8 million hectares from 1985 to 1998. The Purwodadi Botanical Garden serves as a mini prototype of Indonesia's plant diversity but lacks an integrated database on tree species distribution. This study aims to utilize Deep Learning and remote sensing technology for tree species mapping in the Purwodadi Botanical Garden. Using the RCNN Deep Learning model in ArcGIS Pro, the data includes SPOT 6 and 7 imagery, field observations of tree species, and object labeling based on taxonomic levels. The RCNN model successfully detected 235 tree objects with an accuracy of 72%, Precision of 84.45%, Recall of 70.29%, F1-score of 0.76, and an Average Precision of 61%. The species diversity index (H’) was recorded at 3.919161, while the evenness index (E) was 0.973619, indicating high biodiversity. A total of 56 species with 341 individuals were identified, including rare species such as Tectona grandis L. f., Adansonia digitata L., and Syzygium cumini. These findings demonstrate that Deep Learning technology can effectively support biodiversity monitoring.
PELUANG PENGGUNAAN YOLO V3 - DEEP LEARNING UNTUK DETEKSI LONGSOR PADA LEVEL SUB DAERAH ALIRAN SUNGAI Rahmadani, Rizka Nur; Arifin, Syamsul; Nita, Istika; Putra, Aditya Nugraha
JTSL (Jurnal Tanah dan Sumberdaya Lahan) Vol. 13 No. 2 (2026)
Publisher : Departemen Tanah, Fakultas Bio-industri Pertanian dan Kehutanan, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2026.013.2.5

Abstract

In the past two decades, remote sensing-based landslide detection methods have advanced significantly. However, these methods generally remain broad in scope and are not yet capable of identifying landslides as distinct objects with precise locations. This study aims to integrate YOLO v3 into remote sensing applications to enhance the accuracy of landslide detection in sub-watershed (sub-DAS) areas. The study focuses on two sub-watersheds with a history of frequent landslides, namely Kali Konto and Sumber Brantas. Sentinel-2A imagery from 2024 was downloaded for all months and composited to achieve the most stable visualization. Landslide location points were collected through an exploratory approach, incorporating information from the National Disaster Management Agency (BNPB) and local communities. A total of 155 landslide points were recorded in the field and subsequently used as training and validation data. Seventy percent of these points were analyzed using a deep convolutional neural network—YOLO v3—implemented in ArcGIS Pro, while the remaining 30% were used for validation against actual field occurrences.The detection model developed for the Kali Konto and Sumber Brantas sub-watersheds achieved an accuracy of 77%, demonstrating reliable performance in predicting landslide locations. The landslides identified predominantly consisted of slope failures and rockfalls occurring along cut-fill sections of roadside areas. However, this study has certain limitations: the developed model is unable to classify different types of landslides, and the satellite imagery used has a medium resolution, which is not yet advanced enough to fully meet the requirements for sub-watershed-level analyses.
Reliability of using high-resolution aerial photography (red, green and blue bands) for detecting available soil water in agricultural land Putra, Aditya Nugraha; Nita, Istika
Journal of Degraded and Mining Lands Management Vol. 7 No. 3 (2020)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2020.073.2221

Abstract

The need for irrigation water is influenced by soil water content or more precisely by available water (pF 2.5 and pF 4.2). There is a need for technological breakthroughs in using Unmanned Aerial Vehicle (UAV) to identify water content quickly and broadly and accurately. The study was conducted in an area of ±18 hectares in the Sisim Sub Watershed in September 2019 at 09.00 a.m. Aerial photographs were taken at an altitude of 100 m with DJI Phantom Pro 3.0. The number of observation points was 75 points, where 15 points for validation were calculated based on the map scale. Photo processing was made using Agisoft. The Digital Elevation Model (DEMNAS) with 8.2 m resolution was used to compare the red, green and blue bands. The analysis used was Co-Kriging Geo Statistics Analysis, the compilation of algorithms based on the regression equation and ten index formulations. Validation was done by correlation continued with the regression or paired t-test if the parameter relationship was close. The available water measured in the field ranged from 5.16-48.28%. The results showed that the formulation of soil water content could be run on the Red, Green, and Blue bands, Intensity index, TGI index, ExGreen index and DEMNAS with a weak correlation (below 0.5), where TGI had the highest value (r=0.32). A test of t-pairing was not done because of a weak correlation. The highest estimation of pF 4.2 is DEMNAS (r=0.35), and pF 2.5 was on the TGI index (r=0.4).
Comparison of soil physical properties and soil-vegetation indices to predict rice productivity in Malang Regency of East Java Putra, Aditya Nugraha; Sudarto, S; Ridwan, Ananda Ginanthian Alpheratz; Aditama, Aftomi Firman; Janahtin, Sifa’ul
Journal of Degraded and Mining Lands Management Vol. 8 No. 4 (2021)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2021.084.2891

Abstract

Rice has become the leading food commodity in Indonesia, with total production reached +54.60 million tons in 2019. However, the production tended to decrease by around 8% from 2018 to 2019, while the rice consumption increased by +1.53 tons. This study aims to develop a rice production estimation model using the soil-vegetation index transformation (MSAVI and SAVI) and soil physical properties, which has the advantage of being faster, cheaper, and more accurate than conventional methods. The soil physical properties were taken based on soil mapping units and analyzed with soil physical parameters. The results showed strong relationships between rice productivity - soil physical characteristics and rice productivity – MSAVI and EVI with r values of 0.97, 0.83, and 0.74, respectively. The soil physical properties have a better coefficient of determination and accuracy than soil-vegetation index. The prediction model of rice production by soil physical properties is formulated inward γ = -8.96+0.01 (Top Soil Sand) + 0.01 (Top Soil Silt) + 6.28 (Bulk Density) - 14.07 (Penetration) - 0.13 (Sub Soil Permeability). There is no difference in the productivity value between model and laboratory analysis result. These results indicate that the rice yield prediction model can be used for estimation purposes.
Co-Authors Abda Abda Achmad Noerkhaerin Putra Adelyanti, Martiana Aditama, Aftomi Firman Adril, Adril Al Jauhary, Muhammad Rifqi Alia Fibrianingtyas Andhika, Yosi Ayuningtyas, Priska Danang Surya Candra, Danang Surya Didik Suprayogo Erekso Hadiwijoyo Evy Latifah Fachry Abda El Rahman Fata, Yulia Amirul Fauziah, Dwi Christina Hadi, Maruf Hidayatullah, Rifqi Rahmat Istika Nita Iva Dewi Lestariningsih, Iva Dewi Janahtin, Sifa’ul Kartika Budi Utami Kristiawati, Wanda Kurniawan Sigit Wicaksono, Kurniawan Sigit Lestari, Nina Dwi Luqmanul Hakim Melati Julia Rahma Mochtar Luthfi Rayes Nugraha, Irfan Nur Priya Prasetya, Novandi Rizky Pratiwi, Nisrina Salwa Rahmadani, Rizka Nur Ridwan, Ananda Ginanthian Alpheratz Rika Ratna Sari Riza, Sativandi Rizki Maulana Ishaq Romadhona, Mahimma S Sudarto, S Samudra, Ferdianto Budi Saputra, Danny Dwi Savitri, Elvin Sektiono, Antok Wahyu Sitorus, Albert Fernando Sudarto Sudarto Sugeng Prijono Sugiarto, Michelle Talisia Sukamto, Ika Sumiyarsi Suryoprojo, Adithya Riefanto Syamsul Arifin