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All Journal Indonesian Journal of Geography Jurnal Penelitian Karet Journal of Geoscience, Engineering, Environment, and Technology JOIV : International Journal on Informatics Visualization MAJALAH ILMIAH GLOBE Jurnal Geografi Jurnal Penginderaan Jauh Indonesia geoedusains: Jurnal Pendidikan Geografi JURNAL GEOGRAFIKA (GEOGRAFI LINGKUNGAN LAHAN BASAH)
Pramaditya Wicaksono, Pramaditya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Education Engineering Environmental Science Physics Social Sciences Other

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PENGARUH RESOLUSI SPASIAL CITRA TERHADAP HASIL PEMETAAN KANDUNGAN HARA NITROGEN PERKEBUNAN KARET Saputra, Jamin; Kamal, Muhammad; Wicaksono, Pramaditya
Jurnal Penelitian Karet JPK : Volume 36, Nomor 1, Tahun 2018
Publisher : Pusat Penelitian Karet - PT. Riset Perkebunan Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/ppk.jpk.v36i1.545

Abstract

Nitrogen merupakan salah satu unsur hara yang dibutuhkan dalam jumlah banyak oleh tanaman. Tanaman yang mengalami kekurangan unsur hara nitrogen akan menyebabkan terhambatnya pertumbuhan dan penurunan produktivitas tanaman. Penerapan sistem pertanian presisi pada kegiatan pemupukan di perkebunan karet dilakukan dengan cara dosis pemupukan dibuat berdasarkan kandungan hara tanah dan kandungan hara pada tanaman. Pada areal yang luas membutuhkan biaya analisa hara tanaman yang cukup mahal. Oleh karena itu sangat dibutuhkan suatu teknologi yang dapat mengestimasi kondisi hara tanaman dengan cepat dan biaya yang murah. Teknologi penginderaan jauh merupakan alternatif yang dapat digunakan untuk areal yang luas dan dengan waktu yang cepat serta biaya yang relatif murah. Penelitian ini bertujuan untuk mengetahui pengaruh resolusi spasial citra terhadap peta hasil estimasi kandungan nitrogen perkebunan karet. Citra multi resolusi yang digunakan antara lain GeoEye-1 (2 m) Sentinel-2A (10 dan 20 m) dan Landsat 8 OLI (30 m). Metode yang digunakan adalah membangun hubungan semi-empiris antara band tunggal dan indeks vegetasi citra dengan kandungan hara nitrogen perkebunan karet. Hasil penelitian menunjukkan bahwa peta hasil estimasi kandungan hara nitrogen perkebunan karet menggunakan citra Sentinel-2A (SE 0,369) memiliki akurasi yang lebih tinggi dibandingkan dengan menggunakan citra GeoEye-1 (SE 0,519) dan Landsat 8 OLI (SE 0,462).
ANALISIS SALURAN SPEKTRAL YANG PALING BERPENGARUH DALAM IDENTIFIKASI KESEHATAN TERUMBU KARANG: Studi Kasus Pulau Menjangan Besar dan Menjangan Kecil, Kepulauan Karimunjawa Murti, Sigit Heru; Wicaksono, Pramaditya
MAJALAH ILMIAH GLOBE Vol 16, No 2 (2014)
Publisher : Badan Informasi Geospasial

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (945.85 KB) | DOI: 10.24895/MIG.2014.16-2.57

Abstract

ABSTRAKSalah satu faktor yang berpengaruh dalam penggunaan teknologi penginderaan jauh untuk identifikasi tingkat kesehatan terumbu karang adalah konfigurasi saluran spektral sensor. Pemahaman tentang saluran spektral yang berpengaruh positif terhadap proses identifikasi kesehatan terumbu karang sangat penting dalam efisiensi pemetaan, baik dari segi waktu maupun akurasi yang didapatkan. Penelitian ini bertujuan untuk mencari saluran spektral yang berkontribusi positif terhadap identifikasi kesehatan terumbu karang, dengan menggunakan bantuan analisis PCA (Principle Component Analysis) dan Factor Loadings pada citra Landsat 7 ETM+ dan ASTER. Tingkat kesehatan terumbu karang dilihat dari persentase tutupan karang hidupnya dan dibagi menjadi empat kelas yaitu Sangat Baik (>75% tutupan karang hidup), Baik (50-74%), Sedang (25-49%) dan Rusak (<25%). Untuk mengetahui saluran spektral yang paling baik dalam identifikasi kesehatan terumbu karang, klasifikasi multispektral dilakukan pada kombinasi PC (Principle Component) band dan dilakukan uji akurasi. Hasil uji akurasi dipasangkan dengan hasil analisis Factor Loadings untuk melihat kontribusi tiap saluran spektral pada tiap akurasi. Hasil penelitian menunjukkan bahwa saluran hijau merupakan saluran yang berkontribusi paling tinggi dan saluran merah adalah saluran memberikan kontribusi paling rendah. Saluran biru, yang merupakan saluran dengan penetrasi tubuh air paling baik, memberikan kontribusi yang lebih rendah dibandingkan dengan saluran hijau karena tingginya hamburan Rayleigh yang terjadi pada saluran biru tersebut.Kata Kunci: Landsat 7 ETM+, ASTER, PCA, factor loadings, terumbu karangABSTRACTOne of the major factors to determine the success of remote sensing identification for coral reefs health is the spectral resolution of the sensor. The understanding about the characteristic of spectral bands contribute positively to the identification of coral reefs health is very important for the effective and satisfactory mapping results. This research aimed to identify the most effective spectral band for the coral reefs health identification, using Principle Component Analysis (PCA) and factor loadings analysis. Landsat 7 ETM+ and ASTER VNIR images were used in this research. Coral reefs health condition is determined from the percentage of live coral cover and divided into four ordinal classes: very good (>75% live coral reefs cover), good (50-74%), medium (25-49%), and bad (<25%). To find the most effective bands for coral reefs health identification, multispectral classification was applied on Principle Component (PC) bands combinations. Afterward, the mapping accuracy of each PC bands combination was assessed. Each accuracy assessment result was evaluated with factor loadings analysis result to understand the contribution of different spectral bands on the resulting mapping accuracy. The results show that green band is the most effective spectral band which provides the highest contribution to the mapping, while red band provide the lowest contribution. Blue band, which is the best water penetration band, was less efficient than green band due to the strong Rayleigh scattering that affects more significantly on shorter wavelengths.Keywords: Landsat 7 ETM+, ASTER, PCA, factor loadings, coral reefs
Aplikasi Citra WorldView-2 Untuk Pemetaan Batimetri Di Pulau Kemujan Taman Nasional Karimunjawa Rahman, Waskito; Wicaksono, Pramaditya
Jurnal Penginderaan Jauh Indonesia Vol 1 No 1 (2019): JPJI
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (717.881 KB)

Abstract

The Application of WorldView-2 Image for Bathymetry Mapping in Kemujan Island Karimunjawa National Park The development of remote sensing technology gives an opportunity to extract bathymetry information on the optically shallow water area. This was done by utilizing the reflectance of spectral bands with the ability to penetrate water body. The aim of this research is to map bathymetry of Kemujan Island using remote sensing empirical modeling.  Quickbird image was used in this study. It has four spectral bands namely blue, green, red and near infrared band. These bands were rationed and acquired 12 band ratios. In total, 120 samples were used to produce bathymetry model and 379 samples were used for validation. The models were created for up to the depth of 7 m.  The result showed that the model from band ratio of green and blue band produced the highest accuracy with R² of 0.632 and SE of 1.2 m. The result proved that blue band is the most effective band to be combined with other bands for band ratio input for bathymetry modeling.
Vegetation Change Detection Analysis Using Multi-sensor Hyperspectral Imagery Nugraha, Wahyu Ananta; Wicaksono, Pramaditya; Arjasakusuma, Sanjiwana
Jurnal Geografika (Geografi Lingkungan Lahan Basah) Vol 5, No 1 (2024): GEOGRAFIKA
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jgp.v5i1.11709

Abstract

Vegetation is a fundamental component of ecosystems that maintains carbon levels, hydrological cycles, mitigating greenhouse gases, and ensures climate stability. In recent years, the impacts of global climate change have led to changes in vegetation cover at various levels. Efforts to monitor changes in vegetation are important and beneficial for various fields such as forest monitoring, agriculture, and plantations, among others. The main objective of this research is to detect changes both increase and decrease in vegetation using multi-sensor hyperspectral imagery. The hyperspectral images used in this study are Hyperion 2014 and PRISMA 2021. The method involves creating different levels of spectral resolution simulations from hyperspectral images to detect vegetation changes. Meanwhile, the vegetation change Clustering method employs unsupervised (k-means) techniques. The cluster results can indicate vegetation changes such as vegetation degradation, vegetation, devegetation, or no change, though they currently have low accuracy. The highest accuracy is by Simulated RapidEye image simulations, is 33.5%. The low accuracy results attributed insufficient preprocessing, particularly in topographic correction. Additionally, this research indicates that the spectral resolution levels do not have a significant impact on vegetation change detection, as the differences in change classes at each level are very small.
Bibliometric Analysis of Indonesian Journal of Geography from 2015-2022 Istiana, Purwani; Wicaksono, Pramaditya
Indonesian Journal of Geography Vol 57, No 1 (2025): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.99939

Abstract

This bibliometric analysis aimed to determine the growth trend of document publications, the development of journal citations, the distribution of author countries, the most influential documents, and the dominant research topics in the Indonesian Journal of Geography from 2015 to 2022. The data used were extracted from the Scopus database, comprising a total of 280 documents. A bibliographic data mapping was conducted using the VOSviewer version 1.6.18 and Analyze Search Results tools on the Scopus database. The results showed that the number of documents published in the Indonesian Journal of Geography has been increasing over time. Furthermore, journal impact on scientific development has also increased. The authors of the documents included originate from 29 countries. The most influential document addressed the topic of urban research, while key topics developed from 2015 to 2022 included remote sensing and GIS used as main support and technology for understanding and analyzing various geographical phenomena. This research serves as a reference material for prospective authors and also provides an evaluation of Indonesian Journal of Geography in the future.Received: 2024-09-15 Revised: 2025-01-31 Accepted: 2025-03-19 Published: 2025-04-28
Multi-spatial Resolution Imagery to Estimate Above-Ground Carbon Stocks in Mangrove Forests Purnamasari, Eva; Kamal, Muhammad; Wicaksono, Pramaditya; Hidayatullah, Muhammad Faqih; Susetyo, Bigharta Bekti
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2237

Abstract

Mangroves are a type of vegetation that can absorb carbon and have an essential role in controlling CO2 levels in the atmosphere. Mangroves can absorb carbon better than terrestrial ecosystems because of their ability to bury carbon in sediment. This research aims to compare and measure the carbon stock content above the surface of mangroves in the field using multi-spatial resolution imagery, namely, Landsat 8 OLI, Sentinel 2A, and Planetscope. Field carbon calculations were carried out using the allometric method based on mangrove species. The calculation results are then linked through regression analysis with the vegetation index Difference Vegetation Index (DVI) results. The total carbon obtained from PlanetScope imagery was 535.27 tons, Sentinel 2A imagery was 549.23 tons, and Landsat 8 OLI imagery was 533.57 tons. Among the three images used, based on Sentinel 2A statistical analysis reflects the possibility of overfitting or the best with higher r and R2 values in the calculations. However, based on SE accuracy tests, PlanetScope has better accuracy than the other two images. Apart from that, the accuracy test results using a 1:1 goodness of fit plot for each image, the distribution pattern of mangrove carbon stock estimates shows that the entire model in mapping mangrove carbon stocks is over-estimated. The overestimated results are possible because more objects around the mangrove, especially canopy density, are recorded by remote sensing sensors compared to tree diameter as input for field carbon results.
Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado Cera, Fela Pritian; Danoedoro, Projo; Wicaksono, Pramaditya; Yasir, Moh
JURNAL GEOGRAFI Vol. 15 No. 2 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i2.30795

Abstract

The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 “ 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests
Pemanfaatan Random Forest Dalam Pemetaan Status Trofik Di Danau Semayang Menggunakan Citra Sentinel-2 MSI Oktoberdinata, Muhammad Dika; Wicaksono, Pramaditya; Sudaryatno
geoedusains: Jurnal Pendidikan Geografi Vol. 6 No. 2 (2025)
Publisher : Pendidikan Geografi Fakultas Keguruan dan Ilmu Pendidikan Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/geoedusains.v6i2.5362

Abstract

Pemantauan status trofik perairan menjadi aspek penting dalam menjaga kualitas ekosistem danau secara berkelanjutan. Penelitian ini bertujuan untuk memetakan dan menganalisis distribusi spasial status trofik Danau Semayang menggunakan metode Regresi Random Forest berdasarkan Indeks Carlson. Data yang digunakan meliputi hasil pengukuran parameter kualitas air (Klorofil-a, Total Fosfor, dan kedalaman cakram Secchi) pada Oktober 2024 serta citra Sentinel-2 MSI. Analisis dilakukan dengan membangun model hubungan antara nilai reflektansi band citra dan parameter kualitas air untuk memperoleh nilai Trophic State Index (TSI). Hasil penelitian menunjukkan bahwa model Random Forest mampu memprediksi nilai TSI dengan tingkat akurasi yang bervariasi, dengan nilai R² masing-masing sebesar 0,3709 untuk kedalaman cakram Secchi, 0,2877 untuk Klorofil-a, dan 0,0277 untuk Total Fosfor. Secara spasial, Danau Semayang didominasi oleh kondisi mesotrofik dengan kecenderungan eutrof ringan, terutama di bagian utara dan timur laut danau. Temuan ini menunjukkan bahwa penggunaan citra Sentinel-2 MSI memiliki potensi besar untuk mendukung pemantauan status trofik perairan secara efisien dan berkelanjutan, khususnya pada ekosistem danau paparan banjir yang dinamis seperti Danau Semayang.
Multitemporal Analysis of Aboveground Carbon Stock of Seagrass Meadows using Sentinel-2 in the Coastal Area of Bontang City in 2020-2024 Ravi, Avra Abida El; Wicaksono, Pramaditya
Journal of Geoscience, Engineering, Environment, and Technology Vol. 11 No. 3 (2026): JGEET Vol 11 No 03 : September (2026)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2026.11.3.28311

Abstract

Seagrass is one of the ecosystems that absorbs large amounts of carbon in the blue carbon ecosystem. This ecosystem also has many benefits, such as becoming the habitat for fish and improving water quality. However, there is degradation of the seagrass ecosystem that requires annual carbon inventory with Sentinel-2 which covers benthic habitat areas and has high spatial resolution. This study aims to map changes in the area and distribution of seagrass meadows, as well as to map and estimate changes in aboveground carbon stocks (AGC) of seagrass meadows in part of the coast of Bontang City, East Kalimantan annually with a range of 2020-2024. The methods used in this study include integration with field data, benthic habitat classification and AGC regression with the Random Forest (RF) algorithm, accuracy testing, and multitemporal mapping to detect changes. The results of this study indicate that the benthic habitat class is dominated by seagrass which remains from 2020 to 2024 covering an area of 2643.92 hectares and the area of seagrass tends to decrease from 2808.14 hectares to 2745.94 hectares. The benthic habitat classification used to classify the seagrass area has an Overall Accuracy of 65.9%. Meanwhile, the total estimation of Seagrass AGC decreased from 287.12 tons of carbon in 2020 to 265.6 tons in 2024 of carbon from an area of 3118.6 hectares. The model from the regression results was tested for accuracy with an RMSE of 2.09 gC/m2, R2 of 0.65, and a Plot Goodness of Fit of 1:1 which explains that the estimated AGC sample is positively correlated with the field AGC sample.
Optimization of Coastline Extraction Using MNDWI Thresholds and Machine Learning on Landsat Images of Wetlands in Northern East Java, Indonesia Shofura Afanin Nuha; Khakhim, Nurul; Wicaksono, Pramaditya
Journal of Geoscience, Engineering, Environment, and Technology Vol. 11 No. 3 (2026): JGEET Vol 11 No 03 : September (2026)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2026.11.3.28479

Abstract

This study evaluates and compares methods for extracting coastlines in the wetland coastal areas of northern East Java, specifically in Sidoarjo and Gresik Regencies, East Java, Indonesia. By analyzing eight scenarios that integrate bands from Landsat 8 imagery, the Modified Normalized Difference Water Index (MNDWI), and the SRTM Digital Elevation Model (DEM) under two machine learning classifiers (Random Forest and Classification and Regression Trees) on the Google Earth Engine (GEE) platform. Using a dataset consisting of 180 ground-truth points, we found that Random Forest (RF) combined with MNDWI and DEM (Scenario 5) achieved the highest overall accuracy (OA = 76%, Kappa = 0.71) in the flat wetlands of Sidoarjo, which are dominated by aquaculture ponds and mangrove vegetation. In contrast to the diverse Gresik coastal area, where some regions are dominated by industry, the pure CART model (Scenario 6) yielded the best results (OA = 66%, Kappa = 0.60), as the addition of DEM and MNDWI caused overfitting due to extreme local spectral and vertical disturbances from the complex coastal structure. These results indicate that ensemble-based classification (RF) with elevation filtering is crucial for distinguishing the microtopography of aquaculture ponds from natural seawater, whereas a single decision tree (CART) performs better with raw spectral inputs in structurally heterogeneous coastal environments.
Co-Authors Bigharta Bekti Susetyo Cera, Fela Pritian Hidayatullah, Muhammad Faqih Jamin Saputra Muhammad Kamal Nugraha, Wahyu Ananta Nurul Khakhim Oktoberdinata, Muhammad Dika Projo Danoedoro Purnamasari, Eva Purwani Istiana Rahman, Waskito Ravi, Avra Abida El Sanjiwana Arjasakusuma, Sanjiwana Shofura Afanin Nuha Sigit Heru Murti Sudaryatno Yasir, Moh