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All Journal Indonesian Journal of Geography Ecotrophic, Journal of Environmental Science Jurnal Pembangunan Wilayah dan Kota Geoplanning : Journal of Geomatics and Planning Majalah Geografi Indonesia AGRIVITA, Journal of Agricultural Science Jurnal Ilmiah Perikanan dan Kelautan Forum Geografi Jurnal Wilayah dan Lingkungan IJEMS (Indonesian Journal of Environmental Management and Sustainability) Jurnal Serambi Engineering Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital International Journal of Remote Sensing and Earth Sciences (IJReSES) Social, Humanities, and Educational Studies (SHEs): Conference Series Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments) International Journal of Disaster Management

Masita Dwi Mandini Manessa

Department Of Geography, Faculty Of Mathematics And Natural Sciences, Universitas Indonesia

Author-ID : 2733923

Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Articles

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Kerentanan Wilayah Terhadap Covid-19 di Kota Pariaman Sri Fauza Pratiwi; S. Supriatna; Masita Dwi Mandini Manessa
Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Vol 5, No 2 (2021): Desember 2021
Publisher : Program Studi Pendidikan Geografi Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/geodika.v5i2.4075

Hampir seluruh wilayah di Indonesia terpapar virus Covid-19 termasuk Kota Pariaman. Kota Pariaman merupakan salah satu destinasi wisata seperti pariwisata pantai sehingga banyak didatangi wisatawan saat hari libur sehingga menyebabkan tingginya tingkat interaksi manusia dan kontak langsung antara manusia terjadi secara intensif. Penelitian ini bertujuan untuk memprediksi kerentanan wilayah terhadap Covid-19 di Kota Pariaman. Adapun data yang digunakan sebagai variabel dalam penelitian ini berupa data sekunder dan data primer. Data sekunder berupa penduduk usia rentan dan kerapatan jalan, sedangkan data primer berupa jarak dari Rumah Sakit rujukan dan persebaran lokasi vital. Teknik pengumpulan data tersebut yaitu dengan mencari berbagai referensi dari penelitian sebelumnya dan juga pengamatan langsung di lapangan. Metode yang digunakan yaitu analisis spasial deskriptif dengan metode overlay terhadap variabel-variabel yang digunakan. Hasilnya menunjukkan bahwa kerentanan tertinggi terhadap Covid-19 berada di bagian barat Kota Pariaman tepatnya di Kecamatan Pariaman Tengah karena terdapat kelompok usia rentan paling banyak, dan persebaran lokasi vital seperti cafe, pasar tradisional, dan stasiun berada disana, meskipun rumah sakit rujukan berada di kawasan tersebut.

Terrorism vulnerability assessment in Java Island: a spatial multi-criteria analysis approach Asep Adang Supriyadi; Masita Dwi Mandini Manessa
Indonesian Journal of Geography Vol 52, No 2 (2020): 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.45691

Terrorism is one of the Indonesia’s national security threat. The attack mostly happens in Java Island, attracted by the dense population, also because the island is a center for economic and governance. The spatial pattern of terrorism attack shows correlations with the spatial density of the targeted attack. Therefore, this study assesses the spatial vulnerability of Java Island using a spatial multi-criteria analysis (SMCA). The main attributes analyzed were the density of the past terrorist attack, arrested area, police/military facility, government facility, business center, densely populated area, and church, determine that in the case of a terrorist attack is strongly affected by the attraction of the area.

Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery Nurwita Mustika Sari; R. Rokhmatuloh; Masita Dwi Mandini Manessa
Geoplanning: Journal of Geomatics and Planning Vol 8, No 2 (2021)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.8.2.75-84

The existence of vegetation in an area has an important role to maintain the carrying capacity of the environment and create a comfortable environment as a place to live. In an effort to create a sustainable environment, there are various pressures on vegetation that cause a decrease in vegetation area. Economic activity, population growth and other anthropogenic activities trigger the dynamics of vegetation cover in an area that causes land cover changes from vegetation to non-vegetation. Majalengka Regency as one of the areas with intensive regional physical development in line with the operation of BIJB Kertajati and the Cipali toll road became the study area in this research. This study aims to monitor the dynamics of vegetation cover with the proposed method namely the integration of the OBIA and Random Forest classifier using multi temporal Sentinel-2 satellite imagery. The results show that there is a decrease in the area of vegetation in the research area as much as 4,329.6 hectares to non-vegetation areas in the period 2016-2020. The vegetation area in 2020 is 84,716.07 hectares and non-vegetation area is 35,708 hectares. Thus, there has been a decrease in the percentage of vegetation area from 73.94% in 2016 to 70.35% in 2020, meanwhile for non-vegetation areas there has been an increase from 26.06% in 2016 to 29.65% in 2020.

The Spatial Model of Paddy Productivity Based on Environmental Vulnerability in Each Phase of Paddy Planting Rahmatia Susanti; S. Supriatna; R. Rokhmatuloh; Masita Dwi Mandini Manessa; Aris Poniman; Yoniar Hufan Ramadhani
Geoplanning: Journal of Geomatics and Planning Vol 8, No 2 (2021)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.8.2.127-136

The national primary always growth and increase in line with the increase in population, such as the rise of rice consumption in Indonesia. Paddy productivity influenced by the physical condition of the land and the declining of those factors can detected from the environmental vulnerability parameters. Purpose of this study was to compile a spatial model of paddy productivity based on environmental vulnerability in each planting phase using the remote sensing and GIS technology approaches. This spatial model is compiled based on the results of the application of two models, namely spatial model of paddy planting phase and paddy productivity. The spatial model of paddy planting phase obtained from the analysis of vegetation index from Sentinel-2A imagery using the random forest classification model. The variables for building the spatial model of the paddy planting phase are a combination of NDVI vegetation index, EVI, SAVI, NDWI, and time variables. The overall accuracy of the paddy planting phase model is 0.92 which divides the paddy planting phase into the initial phase of planting, vegetative phase, generative phase, and fallow phase. The paddy productivity model obtained from environmental vulnerability analysis with GIS using the linear regression method. The variables used are environmental vulnerability variables which consist of hazards from floods, droughts, landslides, and rainfall. Estimation of paddy productivity based on the influence of environmental vulnerability has the best accuracy done at the vegetative phase of 0.63 and the generative phase of 0.61 while in the initial phase of planting cannot be used because it has a weak relationship with an accuracy of 0.35.

SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY Masita Dwi Mandini Manessa; Ariyo Kanno; Masahiko Sekine; Muhammad Haidar; Koichi Yamamoto; Tsuyoshi Imai; Takaya Higuchi
Geoplanning: Journal of Geomatics and Planning Vol 3, No 2 (2016)
Publisher : Department of Urban and Regional Planning, Diponegoro University

In empirical approach, the satellite-derived bathymetry (SDB) is usually derived from a linear regression. However, the depth variable in surface reflectance has a more complex relation. In this paper, a methodology was introduced using a nonlinear regression of Random Forest (RF) algorithm for SDB in shallow coral reef water. Worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. Furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in RF and then compared with conventional methods of Multiple Linear Regression (MLR) at ten times cross validation. Moreover, the performance of each possible pair from six visible bands was also tested. Then, the estimated depth from two methods and each possible pairs were evaluated in two sites in Indonesia: Gili Mantra Island and Panggang Island, using the measured bathymetry data. As a result, for the case of all bands used the RF in compared with MLR showed better fitting ensemble, -0.14 and -1.27m of RMSE and 0.16 and 0.47 of R2 improvement for Gili Mantra Islands and Panggang Island, respectively. Therefore, the RF algorithm demonstrated better performance and accuracy compared with the conventional method. While for best pair identification, all bands pair wound did not give the best result. Surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy.

Study of Sea Surface Temperature (SST), Does It Affect Coral Reefs? Eghbert Elvan Ampou; Masita Dwi Mandini Manessa; Faisal Hamzah; Nuryani Widagti
Jurnal Ilmiah Perikanan dan Kelautan Vol. 12 No. 2 (2020): JURNAL ILMIAH PERIKANAN DAN KELAUTAN
Publisher : Faculty of Fisheries and Marine Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jipk.v12i2.20316

HighlightEffect of sea surface temperature on coral reefsCorrelation with NOAA and AQUA MODIS satellite imagery dataSea water quality analysisThe adaptability of coral reefsAbstractThis research aims to identify the influence of Sea Surface Temperature (SST) to coral disease and bleaching using MODIS-Aqua data from 2003-2009 and NOAA Coral Reef Watch data. Field-data collection on coral disease and bleaching was carried out in Bunaken National Park, Wakatobi National Park, and Raja Ampat, in August, October, and November 2009, respectively. The presence of coral disease and bleaching was observed by using time-swim method. A prevalence formula was used to calculate the percentage of coral disease and bleaching colonies. The range of mean SST value from each location: Bunaken from 26.84-31.45oC, Wakatobi from 26.09-31.95oC and Raja Ampat from 27.72-31.36oC. There is an influence of SST anomaly on the presence of dis- ease and coral bleaching. During 2003-2019, the highest SST anomaly that could increase the risk of the coral bleaching phenomenon was found in 2010. Coral disease and bleaching were found at locations with high SST anomaly, low nitrate and available phosphate. However, high SST anomalies were not a main cause of coral disease and bleaching. In many locations in Indonesia, mass-bleaching has occurred and the ability of coral adaptation is the main key in dealing with this phenomenon.

Hotspot Distribution Analysis In East Kalimantan Province 2017-2019 to Support Forest and Land Fires Mitigation Nurwita Mustika Sari; Nurina Rachmita; Masita Dwi Mandini Manessa
Indonesian Journal of Environmental Management and Sustainability Vol. 4 No. 1 (2020): March
Publisher : Research Centre of Inorganic Materials and Complexs

Forest and land fires that have occurred in the territory of East Kalimantan Province have caused immediate disaster to the area from year to year and become a global concern in recent years. Hotspots that potentially cause forest and land fires can be detected using satellites such as NOAA-20. The purposes of this study are to analyze the distribution pattern of hotspots in East Kalimantan Province during 2017-2019, identify areas with the highest risk of fires caused by the high intensity of hotspot. The method used in this study is the Nearest Neighbor Analysis and Kernel Density Estimation analysis. The results showed that the distribution pattern of hotspots in East Kalimantan Province during 2017-2019 was clustered with the highest intensity of hotspots were in Berau, East Kutai and Kutai Kartanegara Districts. And from the result of the analysis, the highest number of days has a peak hotspots on September each year. Keywords: forest and land fires, hotspots, Nearest Neighbor, Kernel Density Estimation

Spatial Distribution Patterns Analysis of Hotspot in Central Kalimantan using FIMRS MODIS Data Adisty Pratamasari; Ni Ketut Feny Permatasari; Tia Pramudiyasari; Masita Dwi Mandini Manessa; Supriatna Supriatna
Journal of Geography of Tropical Environments Vol 4, No 1 (2020): February
Publisher : Open Journal System

One of the ways to observe the hotspot created by forest fires in Indonesia is through Remote sensing imagery, such as MODIS, NOAA AVHRR, etc. Central Kalimantan is one of the areas in Indonesia with the highest hotspot data. In this research, MODIS FIRMS hotspot data in Central Kalimantan collected from 2017 – 2019, covering 13 districts: South Barito, East Barito, North Barito, Mount Mas, Kapuas, Katingan, Palangkaraya City, West Kotawaringin, East Kotawaringin, Lamandau, Murung Raya, Pulang Pisau, Seruyan, and Sukamara. That is four aspects that this research evaluated: 1) evaluating the spatial pattern using the Nearest Neighbor Analysis (NNA); 2) evaluate the hotspot density appearance using Kernel Density; and 3) correlation analysis between rainfall data and MODIS FIRMS. As a result, the hotspot in Central Kalimantan shows a clustered pattern. While the natural breaks KDE algorithm shows the most relevant result to represent the hotspot distribution. Finally, the hotspot is low correlated with rainfall; however, is see that most of the hotspot (~90%) appeared in low rainfall month (less than 3000 mm/month).Keywords: Forest fire, Hotspot, NNA, Kernel density, Central KalimantanDOI: http://dx.doi.org/10.7454/jglitrop.v4i1.74

ESTIMASI BATIMETRI DARI DATA SPOT 7 STUDI KASUS PERAIRAN GILI MATRA NUSA TENGGARA BARAT Kuncoro Teguh Setiawan; Masita Dwi Mandini Manessa; Gathot Winarso; Nanin Anggraini; Gigih Girrastowo; Wikanti Astriningrum; Herianto Herianto; Syamsu Rosid; A. Harsono Supardjo
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 15 No. 2 Desember 2018
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Indonesia merupakan negara kepulauan dengan ribuan pulau besar dan kecil yang memliki perairan laut dangkal. Salah satu informasi yang dibutuhkan dari pulau-pulau tersebut adalah peta batimetri khususnya diperairan laut dangkal. Informasi tersebut masih sangat terbatas pada skala yang besar untuk skala yang lebih detil masih sangat terbatas. Untuk menyelesaikan permasalahan tersebut dibutuhkan teknogi penginderaan jauh. Salah satu pemanfaatan teknologi penginderaan jauh adalah untuk menghasilkan informasi batimetri. Banyak metode yang dapat digunakan untuk menghasilkan informasi batimetri dengan teknologi tersebut. Metode yang digunakan dalam penelitian ini adalah metode regresi linier berganda (MLR) yang dikembangkan oleh Lyzenga, 2006. Data yang akan di gunakan adalah citra satelit SPOT 7 di Perairan Laut Dangkal Gili Trawangan, Gili Meno dan Gili Air Pulau Lombok Provinsi Nusa Tenggara Barat. Metode penentuan batimetri tersebut dilakukan pada data kedalaman insitu dengan melakukan dua modifikasi yaitu yang pertama dengan tidak memperhatikan jenis objek habitat dasar dan yang kedua memperhatikan objek habitat dasar karang, lamun, makroalga dan substrat.Hasil dari penelitian ini memberikan korelasi R2 yang meningkat dari 0,721 menjadi 0,786 serta penuruanan nilai kesalahan RMSE dari 3,3 meter menjadi 2,9 meter.

BATHYMETRY EXTRACTION FROM SPOT 7 SATELLITE IMAGERY USING RANDOM FOREST METHODS Kuncoro Teguh Setiawan; Nana Suwargana; Devica Natalia Br. Ginting; Masita Dwi Mandini Manessa; Nanin Anggraini; Syifa Wismayati Adawiah; Atriyon Julzarika; Surahman Surahman; Syamsu Rosid; Agustinus Harsono Supardjo
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery.

Co-Authors A. Harsono Supardjo Adisty Pratamasari Agustinus Harsono Supardjo Angga Kurniawansyah Angga Kurniawansyah Anisya Feby Efriana Aris Poniman Aris Poniman K Ariyo Kanno Atriyon Julzarika Aulia Puji Hartati Devica Natalia Br. Ginting Dewi Susiloningtyas Dini Nuraeni Dony Kushardono Dwi Hastuti Eghbert Elvan Ampou Faisal Hamzah Farida Ayu Fathia Hashilah Gathot Winarso Gigih Girrastowo Glendy Somae Haeropan Daniko Putra Hafid Setiadi Hafid Setiadi Heinrich Rakuasa Herianto Herianto Hermawan Setiawan Indira Indira Iqbal Putut Ash Sidik Kartika Kusuma Wardani Kartika Pratiwi Koichi Yamamoto Kuncoro Teguh Setiawan Kustiyo Kustiyo Mangapul P. Tambunan Mangapul Parlindungan Tambunan Mangapul Parlindungan Tambunan Masahiko Sekine Muhammad Haidar Muhammad Haidar Muhammad Rafi Andhika Pratama Mukhoriyah Mukhoriyah Mutia Kamalia Mukhtar Nana Suwargana Nanin Anggraini Nanin Anggraini Ni Ketut Feny Permatasari Niken Anissa Putri Nurina Rachmita Nurina Rachmita Nurwita Mustika Sari Nurwita Mustika Sari Nurwita Mustika Sari Nuryani Widagti Rahmatia Susanti Rokhmatulloh Rokhmatulloh Rokhmatuloh Rokhmatuloh Rudy Parluhutan Tambunan S Supriatna S Supriatna S. Supriatna S. Supriatna Sri Fauza Pratiwi Sri Fauza Pratiwi Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriyadi, Asep Adang Surahman Surahman Syamsu Rosid Syamsu Rosid Syifa Wismayati Adawiah Takaya Higuchi Tia Pramudiyasari Tsuyoshi Imai Wikanti Astriningrum Yoniar Hufan Ramadhani

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