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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 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) International Journal of Disaster Management Journal of Fisheries & Marine The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Masita Dwi Mandini Manessa
Department Of Geography, Faculty Of Mathematics And Natural Sciences, Universitas Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology 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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CA-Markov Chain Model-based Predictions of Land Cover: A Case Study of Banjarmasin City Supriatna Supriatna; Mutia Kamalia Mukhtar; Kartika Kusuma Wardani; Fathia Hashilah; Masita Dwi Mandini Manessa
Indonesian Journal of Geography Vol 54, No 3 (2022): 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.71721

Abstract

Land cover change is a prevalent thing in Indonesia. This phenomenon often causes deforestation rates to continue to increase every year, which can cause various natural disasters. This study will look at changes in land cover, make land cover prediction models, and see the relationship between land cover changes and the flood disaster that occurred in Banjarmasin City and its surroundings. Remote sensing is used to see changes in land cover from year to year with GlobeLand30 satellite imagery. Satellite imagery processing is carried out using the Cellular Automata – Markov Chain method to see the land cover prediction. The results show that the most significant land cover change from 2000 to 2020 is experienced by built-up land and forests, while in 2030, forests are predicted to experience deforestation of 356 km2 from 2020. The deforestation will cause catastrophic flooding in 2021, where flooding extends to areas that are not estimated to be high flood hazards, with 111 flood points located in the plantation area.
Analisis Spasial Potensi Ekonomi dengan Fuzzy Overlay di Sekitar Bandara Internasional Jawa Barat Nurina Rachmita; Mangapul Parlindungan Tambunan; Masita Dwi Mandini Manessa; Rudy Parluhutan Tambunan
Jurnal Pembangunan Wilayah dan Kota Vol 17, No 4 (2021): JPWK Volume 17 No. 4 December 2021
Publisher : Universitas Diponegoro, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/pwk.v17i4.34681

Abstract

Hadirnya Bandara Internasional Jawa Barat (BIJB) di Kecamatan Kertajati telah memberikan dampak positif terhadap pembangunan infrastruktur di wilayah tersebut. Dengan dibangunnya berbagai infrastruktur diharapkan dapat memicu dan meningkatkan pertumbuhan perekonomian. Peningkatan pertumbuhan ekonomi dapat tercapai jika didukung oleh aktivitas atau kegiatan perekonomian di sekitarnya. Penelitian ini bertujuan untuk mengidentifikasi kesesuaian wilayah kegiatan ekonomi di Kecamatan Kertajati, Jatitujuh dan Ligung kemudian menganalisa kesesuaian wilayah tersebut terhadap Rencana Tata Ruang Wilayah (RTRW). Parameter yang digunakan pada penelitian ini adalah data jaringan jalan, permukiman dan lokasi kegiatan perekonomian yang telah ada sebelumnya. Metode yang digunakan dalam penelitian ini adalah analisis spasial dengan pemodelan logika fuzzy pada software ArcGIS. Dari penelitian ini menghasilkan tiga kategori wilayah kesesuaian: ‘Cukup Sesuai’, ‘Sesuai’ dan ‘Sangat Sesuai’. Ditemukan lokasi wilayah yang bertampalan dengan kawasan yang dilindungi berupa kawasan resapan air seluas 0,55 Km2. Hal ini dapat dijadikan pertimbangan dan masukan bagi investor dan Pemerintah Daerah Kabupaten Majalengka dalam melakukan pengembangan dan penataan wilayah.
SMORPH Application for Analysis of Landslide Prone Areas in Sirimau District, Ambon City Glendy Somae; S Supriatna; Masita Dwi Mandini Manessa; Heinrich Rakuasa
Social, Humanities, and Educational Studies (SHES): Conference Series Vol 5, No 4 (2022): Social, Humanities, and Educational Studies (SHEs): Conference Series
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1116.773 KB) | DOI: 10.20961/shes.v5i4.68936

Abstract

Berdasarkan data kejadian longsor, Kecamatan Sirimau merupakan daerah yang rawan longsor di Kota Ambon. Salah satu langkah awal dalam mitigasi bencana longsor di Kecamatan Sirimau adalah dengan memetakan daerah-daerah yang berpotensi longsor. Tujuan dari penelitian ini adalah untuk mengetahui sebaran spasial kawasan rawan longsor di Kecamatan Sirimau. Metode SMORPH digunakan untuk identifikasi dan klasifikasi daerah yang berpotensi longsor berdasarkan matriks bentuk lereng dan sudut kemiringan lereng. Kajian ini menghasilkan 4 tingkatan daerah yang berpotensi longsor, yaitu potensi sangat rendah, rendah, sedang, dan tinggi. Desa dengan potensi longsor tinggi adalah desa Soya dan desa dengan potensi longsor sangat rendah adalah desa Galala. Hasil penelitian ini juga menggambarkan bahwa semakin tinggi kemiringan lereng dan bentuk lereng yang cembung atau cekung maka potensi terjadinya longsor semakin tinggi. Hasil penelitian diharapkan dapat membantu pemerintah Kecamatan Sirimau dalam upaya membuat penataan ruang berbasis mitigasi bencana.
Spatial Distribution of Coral Reef Degradation with Human Activities in the Coastal Waters of Samatellu Lompo Island, South Sulawesi Muhammad Rafi Andhika Pratama; Masita Dwi Mandini Manessa; Supriatna Supriatna; Farida Ayu; Muhammad Haidar
Geoplanning: Journal of Geomatics and Planning Vol 9, No 2 (2022)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.9.2.121-132

Abstract

A healthy coral reef ecosystem can be beneficial for the survival of fish habitats and aquatic ecosystems. This study aims to analyze the influence of human activities on the spatial distribution of coral reefs in the coastal waters of Samatellu Lompo Island, Pangkajene Islands Regency, South Sulawesi in 2000, 2014, 2018, and 2021. The spatial distribution of coral reefs was obtained through a field survey using the underwater transect photo method. Then, satellite images were processed by using the Lyzenga algorithm for water column correction, and aquatic objects were classified by using unsupervised classification. Human activities that affect coral reef destruction were obtained through interviews and it was strengthened with related literature studies. The results showed that the coral reefs in the coastal waters of Samatellu Lompo decreased from 2000-2021. In 2000, the live coral area was 13.53 ha, whereas in 2021 it was 8,031 ha. Destructive fishing activities such as using bombs and poison in catching fish are the main factors of coral reef destruction. In addition, destructive fishing activities commonly occur in the western and northern waters of Samatellu Lompo that causing the live coral into dead coral or rubble.
Pemodelan Dampak Perubahan Iklim dan WIUP terhadap Potensi Habitat Burung Walik Benjol di Pulau Obi Kartika Pratiwi; Supriatna Supriatna; Masita Dwi Mandini Manessa; Aris Poniman K; Mangapul P. Tambunan
Jurnal Serambi Engineering Vol 8, No 1 (2023): Januari 2023
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jse.v8i1.5571

Abstract

AbstractOne of the impacts of climate change is the degradation quality of natural habitats of flora and fauna in Indonesia, risking the loss of most of the existing biodiversity. Obi Island as a natural habitat for several endemic species cannot be avoided from the threat of climate change impacts. The existence of mining concessions also can have a direct impact on their habitat potential. The purpose of this study was to analyze the distribution pattern of endemic species under current climatic conditions, synthesize models of the impact of climate change and analyze mining concessions on the distribution of potential habitats of Carunculated Fruit-dove on Obi Island. The occurrence data of Carunculated Fruit-dove and 19 bioclimatic variables are used as input in the process of making a habitat model with MaxEnt. As a result, the habitat potential model of Carunculated Fruit-dove with AUC = 0.955 has a very suitable habitat potential with an area of 66.02 km2 or 2.68%. The climate change in 2041-2060 with 4 different climate scenarios has an impact on the Carunculated Fruit-dove habitat potential model. Mining Business Permit Areas on Obi Island that have been issued until March 2022 amount to 19 locations with a total area of 373.14 km2 which will have a direct or indirect impact on the potential habitat of Carunculated Fruit-dove on Obi Island. 14.17 km2 of 66.67 km2 of potential habitat that is very suitable for Carunculated Fruit-dove will be directly affected by the existence of these mining areas.Keywords: endemic species, mining business permit area, climate change, obi island, maximum entropy
Generating Evacuation Route for Tsunami Evacuation Based on Megathrust Scenario Hazard Model in Palabuhanratu Village, Sukabumi, West Java Indira Indira; Masita Dwi Mandini Manessa
International Journal of Disaster Management Vol 6, No 1 (2023): April
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.
TEA PLANT HEALTH RESEARCH USING SPECTROMETER Dwi Hastuti; Masita Dwi Mandini Manessa; Mangapul Parlindungan Tambunan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 19 No. 2 (2022)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3831

Abstract

Tea leaves are the most important part for consumption. Leaves that are healthy have a distinct color, while leaves that are not healthy have a color that is very different from the original. Chlorophyll in leaves effects the reflection of infrared light, allowing healthy plants to reflect more infrared light than unhealthy plants. Leaf color and chlorophyll have an important role in showing the growth and health of tea plants. Remote sensing consists of collecting information about objects and features without contacting the equipment. The Normalized Difference Vegetation Index (NDVI), one of the first remote sensing analysis products used to simplify the complexity of multispectral imaging, is now the most commonly used index for botanical assessment. inconsistencies in NDVI depending on sensor-specific spatial and spectral resolutions. Different parts of the leaf have discolored spots due to health conditions or nutritional stress, so there are different spectral values on different parts of the leaf. Unhealthy tea leaves have low NIR values due to disease, insects, and sunburn, which damage the chloroplast structure of the leaves, weaken the absorption of the appropriate band, and increase reflectance. There is a difference between the measurement results of the NDVI spectrometer and the sentinel image. This is due to the fact that the Sentinel-2 image can only retrieve image pixels with a resolution and not diseased leaf parts, as with the use of a spectrometer, which directly extracts the value of the infected area from the normal part of the plant
SPATIAL MACHINE LEARNING FOR MONITORING TEA LEAVES AND CROP YIELD ESTIMATION USING SENTINEL-2 IMAGERY, (A Case of Gunung Mas Plantation, Bogor) Dini Nuraeni; Masita Dwi Mandini Manessa
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 19 No. 2 (2022)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3830

Abstract

Indonesia's tea production and export volume have fluctuated with a downward trend in the last five years, partly due to the increasingly competitive world tea quality. Crop yield estimation is part of the management of tea plucking, affecting tea quality and quantity. The constraint in estimating crop yields requires technology that can make the process more effective and efficient. Remote sensing technology and machine learning have been widely used in precision agriculture. Recently, big data processing, especially remote sensing data, machine learning, and deep learning have been carried out using a cloud computing platform. Therefore, we propose using GeoAI, a combination of Sentinel-2A imagery, machine learning, and Google Collaboratory, to predict ready for plucking tea leaves at optimal plucking time at Gunung Mas Plantation Bogor. We used selected bands of Sentinel-2A and extracted more features (i.e., NDVI) as a training set. Then we utilized the tea blocks boundary and tea plucking data to generate labels using Random Forest (RF) and Support Vector Machine (SVM). The classification results were further used to estimate the production of crop tea yield. The RF classifier is able to achieve overall accuracy at 51% and SVM at 54%. Meanwhile, accuracy at optimally aged tea blocks is able to achieve at 75.62% for RF and 52.88% for SVM. Thus, the SVM classifier is better in terms of overall accuracy. Meanwhile, the RF classifier is superior in predicting ready for plucking tea at optimally aged tea blocks.
A SPATIAL STUDY OF LAND AND FOREST FIRE-PRONE AREAS IN SITUBONDO REGENCY, EAST JAVA PROVINCE Haeropan Daniko Putra; Masita Dwi Mandini Manessa; Rokhmatulloh Rokhmatulloh; Anisya Feby Efriana; Muhammad Haidar
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 17 No 1 (2023)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/EJES.2023.v17.i01.p01

Abstract

The increasing area of land burned in 2021 makes the government urgent to map areas prone to forest fires in Situbondo Regency. This study analyzes areas prone to forest and land fires using the SMCA method. The research analysis used variables of land cover type, the greenness of vegetation, vegetation humidity, land surface temperature, and human factors. The human elements in question are accessibility (distance from the road network) and distance from human activities (distance from settlements, fields, and plantations). The conclusion analysis of forest fire-prone areas is divided into three classes that are high, medium, and low. From the vulnerability model that has emerged, it was found that most of Situbondo Regency have a high grade of forest fire vulnerability with an area of 652.66 km² (39.08%). The areas with the level of vulnerability of the middle, low, and non-vulnerable classes, respectively, are 532.12 km² (31.87%), 306.46 km² (18.35%), and 178.65 km² (10, 70%). The results of statistical tests using the ordinal logistic regression method show that natural factors for forest and land fires had a higher level of influence (? = 4.824) on forest and land fire vulnerability compared to human factors (? = 1.051). Keywords: Forest and Land Fires; GMA method; Natural Factors; Human Factor
Optimized Artificial Neural Network for the Classification of Urban Environment Comfort using Landsat-8 Remote Sensing Data in Greater Jakarta Area, Indonesia Nurwita Mustika Sari; Dony Kushardono; Mukhoriyah Mukhoriyah; Kustiyo Kustiyo; Masita Dwi Mandini Manessa
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v4i2.1760

Abstract

The development of computer vision technology as a type of artificial intelligence is increasing rapidly in various fields. This method uses deep learning methods based on artificial neural networks, a well-performed algorithm in multi-parameter analysis. One of the development of computer vision models and algorithms is for a thematic digital image classification, such as environmental analysis. Remote sensing based digital image classification is one of the reliable tools for environmental quality analysis. This study aims to perform neural network optimization for the analysis of the urban environment comfort based on satellite data. The input data used are 4 types of geobiophysical indexes as urban environmental comfort parameters derived from cloud-free annual mosaics Landsat-8 remote sensing satellite data. The results obtained in this study indicate that the 1 hidden layer neural network architecture with 16 neurons for the classification of urban environmental comfort and 10 other land cover classes is quite good. The result of the classification using this optimized artificial neural network shows that the distribution of classes is very uncomfortable which dominates the Greater Jakarta area and its surroundings. For other classes in the study area, some are uncomfortable and rather comfortable.  By using this method, we obtained a fast classification training time of 18 seconds for 145 iterations to achieve an RMS Error of 0.01, and has a fairly high classification accuracy overall 89% with a Kappa coefficient of 0.88, while the 2 hidden layer neural network architecture does not succeed in achieving convergence
Co-Authors A. Harsono Supardjo Adisty Pratamasari Agustinus Harsono Supardjo Agustinus Harsono Supardjo Angga Kurniawansyah Angga Kurniawansyah Anisya Feby Efriana Annisa Fitria Aris Poniman Aris Poniman K Ariyo Kanno Atriyon Julzarika Aulia Puji Hartati Ayu Mardalena Devica Natalia BR Ginting Devica Natalia Br. Ginting Dewi Susiloningtyas Diah Kirana Kresnawati Dini Nuraeni Dini Nuraeni Dony Kushardono Dwi Hastuti DWI HASTUTI Eghbert Elvan Ampou Elok Lestari Paramita Faisal Hamzah Farida Ayu Fathia Hashilah Gathot Winarso Gigih Girrastowo Glendy Somae Haeropan Daniko Putra Heinrich Rakuasa Herianto Herianto Hermawan Setiawan Hermawan Setiawan Indira Indira Iqbal Putut Ash Sidik Kartika Kusuma Wardani Kartika Pratiwi Koichi Yamamoto Kuncoro Teguh Setiawan Kuncoro Teguh Setiawan Kustiyo Kustiyo Mangapul P. Tambunan Mangapul P. Tambunan Mangapul Parlindungan Marwah Noer Maryani Hastuti Masahiko Sekine Muhammad Haidar Muhammad Haidar Muhammad Haidar Muhammad Rafi Andhika Pratama Mukhoriyah Mukhoriyah Mutia Kamalia Mukhtar Nana Suwargana Nana Suwargana Nanin Anggraini Nanin Anggraini Nanin Anggraini Ni Ketut Feny Permatasari Niken Anissa Putri Niken Anissa Putri Nurina Rachmita Nurina Rachmita Nurwita Mustika Sari Nurwita Mustika Sari Nurwita Mustika Sari Nuryani Widagti Pramudhian Firdaus Rahmadi Rahmatia Susanti Rokhmatulloh Rokhmatulloh Rokhmatuloh Rokhmatuloh Rudy P. Tambunan Rudy Parluhutan Tambunan Rudy Parluhutan Tambunan S Supriatna S Supriatna S. Supriatna S. Supriatna Setiadi, Hafid Sri Fauza Pratiwi Sri Fauza Pratiwi Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriatna Supriyadi, Asep Adang Surahman Surahman Surahman Syamsu Rosid Syamsu Rosid Syamsu Rosid Syifa Wismayati Adawiah Takaya Higuchi Tambunan, Mangapul Parlindungan Tia Pramudiyasari Tsuyoshi Imai Wikanti Astriningrum Yoniar Hufan Ramadhani Yulia Indri Astuty