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Ketut Wikantika

Kelompok Keahlian Penginderaan Jauh Dan Sistem Informasi Geografis, Fakultas Ilmu Dan Teknologi Kebumian, Institut Teknologi Bandung

Author-ID : 417867

Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Green Open Space and Barren Land Mapping for Flood Mitigation in Jakarta, the Capital of Indonesia Retno Dammayatri; Tri Muji Susantoro; Ketut Wikantika
Indonesian Journal of Geography Vol 55, No 2 (2023): 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.76452

High levels of rainfall, tidal flooding, land subsidence, intensified urban development, scarce barren land and a shortage of green open spaces (GOS) are contributing factors to the persistent flooding in Jakarta. Therefore, this study was conducted to map the GOS, built-up, and barren land in the city in order to calculate the biopore infiltration hole (LRB) potential for water infiltration as part of Jakarta's flood mitigation efforts using the Landsat 8 operational land imager (OLI). The Landsat data acquired on September 11, 2019, with path/row 122/064 were processed using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method for the radiometric correction, and geometric correction with a root mean square error (RMSE) of 7.57 meters. Moreover, the normalized difference vegetation index (NDVI) was applied to classify the GOS, the normalized difference built-up index (NDBI) for the built-up areas, and the normalized difference barren land index (NDBaI) for barren land areas which were further confirmed using NDBI to distinguish them from the built-up areas. It is also important to note that the LRB potential was calculated by adding the GOS and barren land, dividing the result by the ideal land area multiplied by the ideal number of holes. The results showed that the GOS, built-up area, and barren land were 8.34%, 85.29%, and 2.48%, respectively. Furthermore, the LRB potential through the optimization of GOS and barren land was found to be 70.06 km2 and produced 16,816,248 LRB (18.27% of total needed). The realization of this value is expected to reduce the potential inundation in Jakarta by 15.6%.

ANALYZING SURFACE ROUGHNESS MODELS DERIVED BY SAR AND DEM DATA AT GEOTHERMAL FIELDS Tahjudil Witra; Asep Saepuloh; Agung Budi Harto; Ketut Wikantika
Bulletin of Geology Vol 1 No 2 (2017): Bulletin of Geology
Publisher : Fakultas Ilmu dan Teknologi Kebumian (FITB), Institut Teknologi Bandung (ITB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/bull.geol.2017.1.2.1

Surface roughness is a physical property which is used in many applications such as hydrological analyses, erosivity of rocks, and identification of geothermal surface manifestations. In this study, the surface roughness was calculated by a pin-meter. This tool is expected be able to measure the fragmental size at ground surface. However, there is a possibility that the tool still has some errors from the effect of topography undulation. In previous research, detrending method was used to minimise the topographical effect in the measured surface roughness. In this paper, we used Synthetic Aperture Radar (SAR) data from Sentinel-1A, and Digital Elevation Model (DEM) SRTM to evaluate the effectiveness of detrending method of pin-meter. Therefore, the measured surface roughness originated solely from fragmental materials. The selected research areas were Wayang Windu and Patuha geothermal field in Indonesia. Modelling the surface roughness by Sentinel-1A image was conducted by utilising backscattering coefficient and local incidence angle. While surface roughness model from DEM is formed by the Root mean square (RMS) for each grid with the optimum size 19×19 pixels. Both models were compared to pin-meter data which have been detrended. Then, the comparison was analyzed based on determination correlation value (R2). Surface roughness model derived by Sentinel-1A produced R2 about 0.1130 higher than DEM about 0.060. It might indicate that the surface roughness measured by the pin-meter following detrending process is free from the effect of topography undulation. Then, surface roughness model derived by Sentinel-1A data was used to identify surface manifestation. Analysis was performed based on pH measurement at field and scatter plot pattern. According to the selected model, the surface roughness at geothermal surface manifestation zones are inversely proportional to the soil pH.

ANALISIS TRANSFORMASI INDEKS NDVI, NDWI DAN SAVI UNTUK IDENTIFIKASI KERAPATAN VEGETASI MANGROVE MENGGUNAKAN CITRA SENTINEL DI PESISIR TIMUR PROVINSI LAMPUNG Simarmata, Nirmawana; Wikantika, Ketut; Tarigan, Trika Agnestasia; Aldyansyah, Muhammad; Tohir, Rizki Kurnia; Fauziah, Afi; Purnama, Yustika
JURNAL GEOGRAFI Geografi dan Pengajarannya Vol 19 No 2 (2021): JURNAL GEOGRAFI Geografi dan Pengajarannya
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jggp.v19n2.p69-79

Abstrak: Perolehan informasi keberadaan hutan mangrove yang memiliki potensi, peran dan fungsi besar dalam kehidupan, dapat diperoleh melalui data penginderaan jauh. Teknologi penginderaan jauh memiliki efisien tinggi dan banyak kelebihan untuk keperluan monitoring hutan mangrove. Penelitian ini bertujuan untuk mengidentifikasi kerapatan ekosistem mangrove dengan menggunakan transformasi indeks vegetasi serta menguji efektivitas beberapa indeks vegetasi dalam hal ini NDVI, NDWI dan SAVI untuk identifikasi jenis dan kerapatan mengrove. Berdasarkan hasil analisis citra Sentinel dengan menggunakan transformasi indeks NDVI, SAVI, dan NDWI untuk identifikasi kerapatan vegetasi pada transformasi NDVI didominasi kelas kerapatan tinggi yaitu pada rentang nilai 0,67 – 1 yaitu seluas 46975,96 Ha, pada transformasi SAVI didominasi kelas kerapatan sangat jarang yaitu pada rentang nilai 0,99 – 1,38 yaitu seluas 48775,18 Ha, pada transformasi NDWI didominasi kelas kerapatan rendah yaitu pada rentang nilai 0,1 – 0,17 yaitu seluas 27442,26 Ha. Hasil uji akurasi yang dilakukan menggunakan 30 sampel uji diperoleh akurasi sebesar 83,33%. Kata kunci: mangrove, Sentinel, NDVI, NDWI, SAVI

IDENTIFIKASI POTENSI REMBESAN MIKRO DI LAPANGAN MIGAS MELALUI DETEKSI MINERAL LEMPUNG MENGGUNAKAN CITRA LANDSAT 8 OLI/TIRS, STUDI KASUS LAPANGAN MIGAS CEKUNGAN JAWA BARAT BAGIAN UTARA Susantoro, Tri Muji; Wikantika, Ketut; Saepuloh, Asep; Harsolumakso, Agus Handoyo
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 15 No. 1 (2018)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2018.v15.a2779

Clay minerals in the oil and gas field have changed with an increase of the quantities in the middle of the oil and gas field and reduction in the edges. This reduction is the effect of micro seepage from oil and gas from the subsurface. The aims of the research is to identify the potential oil and gas seepage through clay mineral mapping. The data used where Landsat 8 OLI/TIRS with recording dated September 25, 2015. The method used in the mapping of clay minerals using the ratio of 1.55-1.75 µm (Short Wave Infrared 1) and 2.08-2.35 µm (Short Wave Infrared 2). The result of Landsat 8 OLI/TIRS data processing shows the potential of anomalies in edges of the oil and gas field. The anomaly is a change in the index value of clay minerals that tend to be lower with values 1.0 to 1.5 than the middle of oil and gas field with values 1.5 to 2.0. The potential pattern of the anomaly follows the border of the oil and gas field. Field surveys show that oil and gas field based on grain size analysis is dominated by clay-sized soil. The dominant clay minerals from X-Ray Diffraction analysis are smectite (56%) and kaolinite (6%).

MONITORING OF MANGROVE GROWTH AND COASTAL CHANGES ON THE NORTH COAST OF BREBES, CENTRAL JAVA, USING LANDSAT DATA Tri Muji Susantoro; Ketut Wikantika; Lissa Fajri Yayusman; Alex Tan; M. Firman Ghozali
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3221

Severe abrasion occurred in the coastal area of Brebes Regency, Central Java between 1985 and 1995. Since 1997, mangroves have been planted around the location as a measure intended to prevent further abrasion. Between 1996 and 2018, monitoring has been carried out to assess coastal change in the area and the growth and development of the mangroves. This study aims to monitor mangrove growth and its impact on coastal area changes on the north coast of Brebes, Central Java Province using Landsat series data, which has previously proven suitable for wetland studies including mangrove growth and change. Monitoring of mangrove growth was analysed using the normalised difference vegetation index (NDVI) and the green normalised difference vegetation index (GNDVI) of the Landsat data, while the coastal change was analysed based on the overlaying of shoreline maps. Visual field observations of WorldView 2 images were conducted to validate the NDVI and GNDVI results. It was identified from these data that the mangroves had developed well during the monitoring period. The NDVI results showed that the total mangrove area increased between 1996 and 2018 about 9.82 km2, while the GNDVI showed an increase of 3.20 km2. Analysis of coastal changes showed that the accretion area about 9.17 km2 from 1996 to 2018, while the abrasion being dominant to the west of the Pemali River delta about 4.81 km2. It is expected that the results of this study could be used by government and local communities in taking further preventative actions and for sustainable development planning for coastal areas.

Mapping Iron Oxide Distribution on the Ground Surface of the Tugu Barat Oil and Gas Field Using Landsat 8 OLI and Field Data Tri Muji Susantoro; Suliantara Suliantara; Ketut Wikantika; Asep Saepuloh; Agung Budi Harto; Herru Lastiadi Setiawan; Fitriani Agustin; Adis Jayati; Kurdianto Kurdianto; Sayidah Sulma; Sukristiyanti Sukristiyanti
Scientific Contributions Oil and Gas Vol 47 No 3 (2024)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.47.3.1634

Previous studies have demonstrated that Landsat series data can be utilized to map rock change in arid and semi-arid environments. In this study, Landsat 8 Operational Land Imager (OLI) was used to map the presence of iron oxide (ferrous, ferric, and hematite) in the topsoil of the Tugu Barat Oil and Gas Field, Northwest Java Basin, Indonesia. The aim is to map the distribution of iron oxide and analyze it for detection of the presence of microseepage. The results show that the concentration of the mineral hematite indicates an anomaly, where the edge of the field is very low and tends to rise in the middle, but this condition is unclear, because of the presence of red soil containing high hematite in the north. Based on analysis indicates an anomaly, where the edge of the field is very low and tends to rise in the middle, but this condition is unclear, because of the presence of red soil containing high hematite in the north. Based on analysis of Landsat 8 OLI data, ferrous oxide concentrations show an increase at the edge of the field, especially in the southeast. However, this condition is less visible in the west because of the high vegetation density. The ferric oxide concentration shows the opposite pattern to the ferrous oxide concentration. These results are supported by the ferrous oxide index results from soil reflectance spectra recorded using Analytical Spectral Devices (ASD). Where the ferrous oxide concentration is low at the edge then tends to rise in the middle of the field. Meanwhile, the analysis of ferric oxide from the spectral reflectance of soil from ASD results does not show clear differences. The Normalized Iron Oxide Difference Index (NIODI) analysis shows the presence of small amounts of hematite and no geotite. The research results show evidence of microseepage indications at the edge of the field, especially at the southeastern edge. Iron oxide mapping has the potential to support oil and gas exploration through analysis of alteration processes which are thought to be the impact of micro-seepage.

Advanced Machine Learning Techniques for Tidal Marsh Classification: A Random Forest Approach using Sentinel-2A Simarmata, Nirmawana; Wikantika, Ketut; Darmawan, Soni; Harto, Agung Budi
Geosfera Indonesia Vol. 9 No. 3 (2024): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v9i3.52186

Tidal marshes play a vital role in coastal ecosystems, functioning in climate change mitigation, water filtration, and protection from coastal erosion. However, mapping and monitoring of these ecosystems is often hampered by difficult accessibility and dynamic environmental conditions. This research aims to improve tidal marsh classification accuracy by applying a Random Forest (RF) algorithm supported by Sentinel-2A satellite imagery. This image provides various spectral parameters and vegetation indices, including B1, GNDVI, BSI, and NDWI. Three RF models with varying parameters were tested to determine their effectiveness in tidal marsh classification. The model with 26 parameters (Model 3) performed best, with the lowest RMSE value of 0.22, the highest AUC of 0.87, and the highest overall accuracy of 95%. These results show that combining critical spectral parameters in the RF model can significantly improve the classification accuracy and biomass estimation in tidal marshes. This study also confirmed the effectiveness of Random Forest in addressing the challenges of high-accuracy mapping and monitoring. These findings provide a solid foundation for tidal marsh ecosystem conservation and management applications and support the application of machine learning in coastal ecosystem mapping for better and more accurate results.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v9i3.52186

Integrated Flood Risk Mapping and Hazard-Vulnerability Assessment for Mitigation Prioritization in Sumatra Hasan Adi Nugraha; M. Angga Hadi Pratama; Muhammad Alsamtu Tita Sabila Pratama Suhartono; Anjar Dimara Sakti; Ketut Wikantika
Geoplanning: Journal of Geomatics and Planning Vol 13, No 1 (2026): Accepted Manuscripts
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.13.1.%p

Flood risk in tropical regions such as Sumatra is increasing due to intensified rainfall extremes and rapid urbanization. Although flood hazard mapping is widely applied, many studies do not clearly distinguish physical hazard from socio-economic vulnerability, limiting their usefulness for targeted mitigation. This study proposes an integrated geospatial framework combining multi-parameter flood hazard assessment and a socio-demographic vulnerability index through a bivariate hazard–vulnerability matrix to support risk reduction. The framework was applied to the November 2025 flood events in Aceh, North Sumatra, and West Sumatra, using nine hazard parameters and four vulnerability indicators. Results show that High to Very High Hazard zones cover 21% of the study area, in lowland basins, while 12% of the area falls into the Very High-Risk category. Validation using observed damage data shows that medium-risk (Risk 2) zones, rather than only the highest-risk cores, account for 87.5%–100% of the exposed population across five major cities, capturing 97.5% of affected residents in Aceh Tamiang. The proposed 5×5 bivariate matrix separates risk dominated by physical hazard, socio-economic vulnerability, or their interaction This enables stratified mitigation strategies, ranging from integrated structural and social interventions to targeted engineering and community-based measures, while providing spatially explicit guidance to strengthen flood risk management and support Sendai Framework objectives under climate change.

Co-Authors Abd. Rasyid Syamsuri Adhi Wibowo Adis Jayati Adriana Hiariej, Adriana Agung B. Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agus Handoyo Harsolumakso Agus Handoyo Harsolumakso, Agus Handoyo Agus Sutanto Agus Sutanto Ahmad Luthfi Hadiyanto Akihiko Kondoh Aldyansyah, Muhammad Alex Tan Aminah Kastuari Anesta, Aqilla Fitdhea Anggun Tridawati Anjar Dimara Sakti Aqilla Fitdhea Anesta Armi Susandi Armi Susandi Ary Setijadi Prihatmanto Asep Saepuloh Asep Yusup Saptari Asep Yusup Saptari, Asep Yusup Asmi M. Napitu Asmi M. Napitu Aswin Rahadian Bambang Widarsono Bobby S. Dipokusumo Dandy A. Novresiandi Darmawan S Darmawan S, Darmawan Dedi Irawadi Deni Suwardi Desti Ayunda Dudung M Hakim Dudung Muhally Hakim Dudung Muhally Hakim Fahmi, Muhammad Nurul Farah Nafisa Ariadji Fauziah, Afi Fenny M. Dwivany FENNY MARTHA DWIVANY Fitriani Agustin Ghazali, Mochamad Firman Ghozali, M. Firman Giasintha Stefani Golok Jaya, La Ode Muhammad Hary Nugroho Hasan Adi Nugraha Herru Lastiadi Setiawan Herru Lastiadi Setiawan Himasari Hanan Husna Nugrahapraja I Nyoman Dibia I NYOMAN RAI I Wayan Nuarsa Imam A. Sadisun Intan Fatmawati Irland Fardani Ishak H. Ismullah Jevon A. Telaumbanua Karlia Meitha Katmoko Ari Sambodo Katmoko Ari Sambodo, Katmoko Ari Kurdianto Kurdianto LILIK BUDIPRASETYO Lissa F. Yayusman Lissa Fajri Yayusman Luky Adrianto Lumbantobing, Marlonroi M. Angga Hadi Pratama M. Firman Ghozali Mamad Sugandi Marlonroi Lumbantobing Mila Olivia Trianaputri Mirelva, Prima Rizky Mochamad Firman Ghazali Mochamad Firman Ghazali Muhammad Alsamtu Tita Sabila Pratama Suhartono Nengah Widiadnyana Nengah Widiadnyana Nisrina Sukriandi Nurjanna Joko Trilaksono Prihanggo, Maundri Prila Ayu Dwi Prastiwi Purnama, Yustika Retno Dammayatri Rian Nurtyawan Riantini Virtriana S. Suliantara Saepuloh, Asep Satria Bijaksana Sayidah Sulma Shafarina Wahyu Trisyanti Sigit Nur Pratama Simarmata, nirmawana Soni Darmawan Sony Darmawan, Sony Sugandi, Mamad Sukristiyanti Sukristiyanti Sukristiyanti Sukristiyanti Suliantara Suliantara Supriadi A Supriadi A, Supriadi Susantoro, Tri Muji Suwardhi, Deni Tahjudil Witra Tan, Alex Tohir, Rizki Kurnia Tombayu A. Hidayat Topik Hidayat Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro, Tri Muji Trianaputri, Mila Olivia Tridawati, Anggun Trika Agnestasia Tarigan Yayusman, Lissa Fajri Yudi Setiawan

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