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All Journal Ecotrophic, Journal of Environmental Science ILMU KELAUTAN: Indonesian Journal of Marine Sciences Jurnal Pendidikan Geografi Undiksha IJOG : Indonesian Journal on Geoscience JTSL (Jurnal Tanah dan Sumberdaya Lahan) Journal of Degraded and Mining Lands Management Forum Geografi JURNAL MANAJEMEN TRANSPORTASI & LOGISTIK Jurnal Penelitian Karet Jurnal Geografi, Edukasi dan Lingkungan International Journal of Remote Sensing and Earth Sciences (IJReSES) Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi International Journal of Disaster Management Environmental and Materials Jurnal Geografi LIngkungan Tropik (Journal of Geography of Tropical Environments) Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital The International Journal of Remote Sensing and Earth Sciences (IJReSES)
MASITA DWI MANDINI MANESSA, MASITA DWI MANDINI
PSMIL PPs Unud
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Physics Social Sciences Transportation

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SPATIAL ANALYSIS OF LAND USE AND LAND COVER VARIATIONS AFFECTING TEA PRODUCTION IN GUNUNGMAS PLANTATION THROUGH REMOTE SENSING TECHNIQUES Paramita, Elok Lestari; Manessa, Masita Dwi Mandini; Tambunan, Mangapul Parlindungan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3888

Abstract

Tea is a manufactured beverage that is popular around the world. In value chain analysis to increase efficiency, remote sensing technology can be developed to monitor the phenomenon of land use land cover (LULC) change and vegetation health conditions. This study aims to identify LULC in tea plantations, identify the health condition of tea plantations, then analyze spatial trends of changes in tea productivity in Gunungmas Afdeling-1 due to changes in tea area or tea vegetation health condition. Identification of changes in LULC in tea plantations can be carried out using remote sensing technology and machine learning, in this study, Google Earth Engine (GEE) LULC identification was generated using a supervised classification with the random forest algorithm on the GEE. Tea productivity trends decreased from 2019 to 2020, but increased from 2020 to 2021. They show that the trend of changes in the area of tea plantation classification is decreasing. According to the NDVI result, most of the reduced area of tea plantations is in areas with healthy vegetation. The trends in tea productivity changes are not in line with changes in the LULC area of tea plantation classification class and tea vegetation health condition.
UTILIZING REMOTE SENSING AND MACHINE LEARNING FOR ECOSYSTEM SERVICES MAPPING AT GUNUNG MAS TEA PLANTATION Fitria, Annisa; Manessa, Masita Dwi Mandini; Tambunan, Rudy Parluhutan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 20, No 2 (2023)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3880

Abstract

Land use and land cover changes are one of the main factors affecting ecosystems and the services they provide. Conversion from natural vegetation to agricultural and urban land can lead to the degradation of ecosystem services and loss of biodiversity. Puncak area, Bogor, which is a highland area, has become an area that is synonymous with tea plantations because it has an ecosystem that is suitable for being a tea plantation area. Gunung Mas tea plantation managed by PTPN VIII is one of the largest tea plantations and a contributor to foreign exchange in Indonesia. The tourism potential in the plantation and agricultural business sectors has a high selling value as a tourist object and attraction. The purpose of this study is to find out the distribution of ecosystem services for climate regulation, water flow and flood regulation, and ecotourism and cultural recreation services at Gunung Mas tea plantation which is displayed in the form of an Ecosystem Service Map. The land cover classification was extracted from the Sentinel 2A image, which was then scored based on expert judgment. The scoring results are then processed using the AHP Pairwise Comparison method. The results of the study show that the research area has very high climate regulation ecosystem services, very high water flow and flood regulation, and high cultural recreation and ecotourism ecosystem services. Keywords: AHP, Ecosystem Services, Land Use and Land Cover, Supervised classification, Tea Plantation.
Generating Evacuation Route for Tsunami Evacuation Based on Megathrust Scenario Hazard Model in Palabuhanratu Village, Sukabumi, West Java Indira, Indira; Manessa, Masita Dwi Mandini
International Journal of Disaster Management Vol 6, No 1 (2023)
Publisher : TDMRC, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/ijdm.v6i1.31148

Abstract

Palabuhanratu Village is one of the villages in Sukabumi, West Java, that is susceptible to earthquake and tsunami risks. This research intends to revise the tsunami hazard map, undertake a spatial analysis of the distribution of evacuation sites, and identify optimal tsunami evacuation routes. The tsunami hazard map was updated using tsunami modeling with COMCOT based on the worst-case scenario of potential magnitude moment 8.8 for the Megathrust segment in the south of West Java from PuSGeN. This modeling was used to predict the worst probable tsunami impact. On the basis of field survey data regarding the location of evacuation sites, evaluation of the distribution of evacuation sites was conducted. In addition, service area analysis is utilized to assess the service area of the present evacuation site in relation to each hamlet in Palabuhanratu village. Approximately 57.33 percent of the town could be affected by a tsunami, according to the findings of this study. The greatest tsunami height along the coast is expected to be between 18 and 22 meters, and the arrival time is 22 minutes. From a total of 35 hamlets, we determined that two hamlets in the Palabuhanratu village area were not harmed by the tsunami. Because not everyone can reach the evacuation location in time, the findings of this study show the need for an additional vertical evacuation site.
ESTIMASI BATIMETRI DARI DATA SPOT 7 STUDI KASUS PERAIRAN GILI MATRA NUSA TENGGARA BARAT Setiawan, Kuncoro Teguh; Manessa, Masita Dwi Mandini; Winarso, Gathot; Anggraini, Nanin; Giarrastowo, Gigih; Asriningrum, Wikanti; Herianto, Herianto; Rosid, Syamsu; Supardjo, A. Harsono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 15 No. 2 (2018)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v15i2.3342

Abstract

Indonesia is an archipelagic state consists of five large islands and thousands of small islands surrounded by shallow marine waters. For this reason, complete and accurate bathymetric information is needed. Large scale bathymetry data in Indonesian waters is still limited, including in the shallow sea waters of Gili Matra, NTB Province. To overcome these problems, remote sensing technology is used. The aim of the study was to analyze the effect of shallow marine habitat base objects on estimating bathymetry from SPOT 7 satellite images. Many methods can be used to produce estimated bathymetry with this technology. The analysis used in this study is multiple linear regression (MLR). The data used is SPOT 7 satellite imagery in the shallow sea waters of Gili Matra, West Nusa Tenggara Province. The estimation of bathymetry was carried out using insitu depth data with two modifications. The first modification did not pay attention to the basic habitat object types and the second modification paid attention to the coral habitat, seagrass, macroalgae and substrate objects. The results of this study provide the value of determination R2 which increased from 72.1% to 78.6% and decreased the RMSE value from 3.3 meters to 2.9 meters.
Analisis Spasial Kerawanan Banjir Menggunakan Metode Spatial Multi Criteria Analysis di Desa Ciputri Jawa Barat Alyudin, Dyah Rizky; Manessa, Masita Dwi Mandini; Purwaningsih, Yuli; Yuningsih, Yuningsih
Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Vol 8 No 2 (2024): September 2024
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/geodika.v8i2.27097

Abstract

Banjir merupakan fenomena yang seringkali menganggu kehidupan masyarakat. Banjir dalam pandangan masyarakat dianggap sebagai bencana yang dikarenakan meluapnya air dari sungai akibat hujan deras dimana tidak hanya menyebabkan kerugian secara materil, namun juga dapat mengganggu kehidupan bermasyarakat. Penelitian ini bertujuan untuk mengetahui kerawanan banjir menggunakan metode Spatial Multi Criteria Analysis (SMCA) di Desa Ciputri, Cianjur, Jawa Barat. Berbagai variabel pembentuk banjir seperti curah hujan, elevasi, kelerengan, jarak dari sungai dan tutupan lahan digunakan untuk membuat peta kerawanan banjir melalui tiga skenario pembobotan. Pembobotan variabel terbaik menggunakan skema jarak dari sungai sebesar 30%, tutupan lahan 20%, elevasi 20%, curah hujan 15%, dan kelerengan 15%. Ketika dibandingkan dengan peta kerawanan banjir hasil wawancara masyarakat Ciputri mengenai histori banjir, pemetaan ini dapat menghasilkan nilai validasi PRC sebesar 60%. Sehingga, dapat disimpulkan bahwa skenario pembobotan variabel ini dapat digunakan dengan cukup baik untuk melakukan pemetaan kerawanan banjir di tingkat pedesaan. Walaupu begitu, penambahan variabel dan pengembangan dalam pembobotan disarankan untuk perkembangan studi lebih lanjut.
Suitability of Mangrove Tourism Areas in Cilamaya Wetan District, Karawang Regency Kurniawansyah, Angga; Susiloningtyas, Dewi; Manessa, Masita Dwi Mandini
Forum Geografi Vol 37, No 1 (2023): July 2023
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v37i1.19852

Abstract

The research described here was conducted at the Tangkolak Marine Center (TMC) tourist attraction in Cilamaya Wetan District, Karawang Regency, Indonesia in November and December 2019. This research aimed to analyze the suitability of the mangrove tourism area using PlanetScope sensor Dove-R satellite imagery. The research method consisted of literature review, observation, calculation of the NDVI (Normalized Difference Vegetation Index) formula using PlanetScope sensor Dove-R satellite imagery, and direct measurements of transects and sample plots. The variables used were thickness, density, mangrove types, biota objects, tides, area characteristics, and accessibility. The results showed that mangrove tourism in TMC could be classified into two categories: suitable (65%-80%) and conditionally-compliant. According to the classification, the area is characterized by a mangrove thickness of up to 175.0 meters, a mangrove density between 15-25 tree/100 m2, 3-5 types of mangrove species, and associated biota including mudskipper fish, shrimp, crab, and crane. Meanwhile, the other area classified as conditionally compliant is characterized by a thickness of up to 48.2 meters, a mangrove density of 5-10 tree/100 m2, 2 species of mangrove, and associated biota in the form of mudskipper fish, shrimp, and crab. The research highlights the successful application of remote sensing data, specifically PlanetScope satellite imagery, for studying mangrove tourism areas, indicating its potential as a valuable alternative data source for such investigations.
ANALISIS PERKEMBANGAN LAHAN TERBANGUN BERDASARKAN METODE SUPERVISED CLASSIFICATION MENGGUNAKAN GOOGLE EARTH ENGINE (STUDI KASUS: DESA CIPUTI, KECAMATAN PACET, KAB.CIANJUR) Prabandari, Amanah Anggun; Manessa, Masita Dwi Mandini
Jurnal Tanah dan Sumberdaya Lahan Vol. 11 No. 2 (2024)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2024.011.2.11

Abstract

Monitoring the development of built-up areas can be done by observing remote sensing time series data such as Satellite Imagery. Google Earth Engine (GEE) makes it easy for users to access satellite image data, data processing and data analysis. GEE provides various machine learning algorithms to extract land cover data. This research aims to analyze the development of built-up areas using time series of remote sensing data, namely Sentinel 2A images recorded in 2019 and 2023 and comparing Random Forest (RF), Classification and Regression Tree (CART), Support Vector Machine (SVM) and Gradient Tree Boost (GTB) algorithms and predicts built-up areas in 2027. Based on the results of this research, RF is the algorithm with the highest accuracy in mapping land cover in Ciputri Village with an Overall Accuracy (OA) of 92% and a Kappa Coefficent (KC) of 0.89 in both the 2019 and 2023 classification results, while the lowest accuracy is the SVM algorithm. A comparison of the built-up land area between the 2019 and 2023 classification results shows a decrease in the built-up land area of 3.08 ha. Meanwhile, the prediction results for 2027 show an increase in built-up areas to 114.72 ha.
The Movement of Cimandiri Fault, Sukabumi, West Java: A Vertical Deformation Analysis Using DInSAR-True Vertical Displacement Method Stevany, Demi; Manessa, Masita Dwi Mandini
Indonesian Journal on Geoscience Vol. 11 No. 1 (2024)
Publisher : Geological Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17014/ijog.11.1.35-44

Abstract

If deformation is caused by earthquakes, it is necessary to map the area size and shape. Especially in Indonesia, which is situated within three main tectonic plates, the examination of risks and hazards is vital to establish the right measures for mitigating and minimizing the risk of earthquakes. Remote sensing is one of the best and most renewable methods for mapping deformation occurrences. DInSAR is a remote sensing technique that can be used to analyze deformation. This work employed the DInSAR technique to examine the Sentinel-1 A SLC-IW satellite and to investigate the deformation of the Cimandiri Fault in Palabuhanratu region, Sukabumi. Within one of these active faults, a magnitude of 4 Richter scale earthquake occurred on September 20th, 2022. Comparing satellite photos before and after the earthquake with the DInSAR yielded a large phase difference value centred on the Cimandiri Fault line with a phase range of -3,092 to 3,031. It indicates that the Cimandiri Fault is the earthquake epicentre on September 20th, 2022, in Palabuhanratu. The results of an investigation of the displacement value happening along the line of sight (LOS) fall between 0.105 and 0.672 m. Due to the incidence angle of the picture observation by satellite, the displacement must be adjusted since it is perpendicular to the ellipsoid referred to as the true vertical displacement. The fixed displacement finding of 0.126 to 0.806 m suggests that the region surrounding the Cimandiri Fault has risen. As GCP points, a number of GPS geodetic data points were incorporated from the field near the Bagbagan bridge, which was the site of the active fault. The outcomes demonstrated that the DInSAR true vertical displacement method can be used to determine the surface deformation.
MARINE CRIME IN INDONESIA: A SPATIO-TEMPORAL ASSESSMENT OF EMERGING TRENDS AND HOTSPOTS Kurniawan, Rahmad; Manessa, Masita Dwi Mandini; Golkariansyah; Wiratama, Eska Yosep; Budiman, Asep
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.7089

Abstract

Indonesia, with its vast maritime domain, faces significant challenges related to maritime crime, including illegal, unreported, and unregulated (IUU) fishing, piracy, human trafficking, and smuggling. The country’s strategic position, bordering key shipping routes like the Strait of Malacca and the Sunda Strait, exacerbates its vulnerability to transnational crimes. This study provides a spatio-temporal assessment of emerging trends and hotspots of marine crime in Indonesia during the period of 2022-2023. Through an analysis of crime incidents, the research identifies key areas of concern, such as the Java Sea, Sumatra, and Eastern Indonesia, where illegal activities have shown persistent and intensifying patterns. The Strait of Malacca and Aceh emerged as critical zones, with increased incidents of piracy and human trafficking, partly linked to the Rohingya refugee crisis. Additionally, the study highlights the environmental impact of illegal activities in ecologically sensitive regions, such as Papua and the Coral Triangle, where illegal logging, mining, and destructive fishing practices threaten marine ecosystems. The analysis also reveals seasonal trends, with the highest concentration of incidents occurring between July and September, coinciding with peak fishing activities. Despite efforts by the Indonesian government, including the Sinking of Foreign Vessels Policy and regional cooperation initiatives like ReCAAP, enforcement gaps remain, particularly in remote regions. The study identifies critical gaps in maritime security, including the need for improved technological surveillance and enhanced community engagement in enforcement efforts. The findings underscore the importance of spatial-temporal monitoring to inform targeted law enforcement and policy responses, thereby protecting Indonesia’s marine resources and enhancing national security.
Co-Authors Adi Wibowo Alyudin, Dyah Rizky Anggraini, Kurnia Annisa Fitria Asep Budiman Astuty, Yulia Indri Ayu, Farida Bustamam, Alhadi Charlos Togi Stevanus, Charlos Togi Christian, Yoga Alfa Demi Stevany, Demi Dewi Susiloningtyas Dony Kushardono Efriana, Anisya Feby Efriana, Feby Elok Lestari Paramita, Elok Lestari Fadhlurahman, Yusuf Nauval Firdaus, Pramudhian Fitriani, Sarah Putri Gathot Winarso, Gathot Giarrastowo, Gigih Golkariansyah Gultom, Rudy Agus Gemilang Gunawan, Dino Herianto Herianto Indira, Indira Kuncoro Teguh Setiawan, Kuncoro Teguh Kurniawansyah, Angga Kustiyo Kustiyo, Kustiyo Logan, Axel Gilbert Mardalena, Ayu Maulidina, Kintan Muhammad Dimyati Muhammad Haidar Mukhoriyah, Mukhoriyah Mukhtar, Mutia Kamalia Nagasawa, Ryota Nanin Anggraini, Nanin Noer, Marwah Novi Susetyo Adi, Novi Susetyo Nur Azizah Nurwita Mustika Sari, Nurwita Mustika Pamungkas, Fajar Dwi Permatasari, Ni Ketut Feny Prabandari, Amanah Anggun Prakarsa, Geraldo Nazar Pramudiyasari, Tia Pratamasari, Adisty Purwaningsih, Yuli Putra, I Kadek Yoga Dwi Rahmad Kurniawan Rahmadi, Rahmad Retno Lestari Riesnandar, Ariq Anggaraksa Rizqi, Bayu Rosid, Mohammad Syamsu Sarwinda, Devvi Semedi, Jarot Mulyo Setiadi, Hafid Shidiq, Iqbal Putut Ash Sopian, Nadya Ata Meiviana Stevanuse, Charlos Togi Suardana, A.A. Md. Ananda Putra Sunandar, Priyo Supardjo, A. Harsono Supardjo, Harsono Supriatna Supriatna Supriatna Supriyadi, Asep Adang Susetyo, Novi Adi Syamsu Rosid Takahiro Osawa Tambunan, Mangapul Parlindungan Tambunan, Rudy Parlindungan Tambunan, Rudy Parluhutan Ulfa, Kurnia Wikanti Asriningrum, Wikanti Wiratama, Eska Yosep Yuningsih Yuningsih