Comparing several pixel-based classification methods for vegetation structural composition mapping using Sentinel 2A imagery in Salatiga area, Central Java

2021 ◽  
Author(s):  
Haeydar Anggara Hadi ◽  
Projo Danoedoro
Keyword(s):  
Classification Methods ◽  
Structural Composition ◽  
Central Java ◽  
Sentinel 2A

scholarly journals Assessment of Coral Reef Life-Form Classification Scheme using Multiresolution Images on Parang Island, Indonesia

2021 ◽  
Vol 6 (3) ◽  
pp. 377
Author(s):  
Wahyu Lazuardi ◽  
Pramaditya Wicaksono
Keyword(s):  
Coral Reef ◽  
Life Form ◽  
Classification Scheme ◽  
Spatial Information ◽  
Satellite Image ◽  
Creative Commons ◽  
Central Java ◽  
Object Based ◽  
Efficient Data ◽  
Sentinel 2A

Spatial information on the varying composition of coral reefs is beneficial for the management and preservation of natural resources in coastal areas. Its availability is inseparable from environmental management goals; however, it can also be used as a means of supporting tourism activities and predicting the emergence of certain living species. A satellite image is one of the effective and efficient data sources that provide spatial information on coral reef variations. This study aimed to evaluate the classification scheme of coral reef life-form using images with different spatial resolutions on Parang Island, Karimunjawa Islands, Central Java. These images were from PlanetScope (3m), PlanetScope resampling (6m), and Sentinel-2A MSI (10m), whose spatial resolutions functioned as the base for building the 3m, 6m, and 10m classification schemes producing 12, 11, and 9 classes, respectively. As for the classification method, it integrated both object-based and pixel-based approaches. The results showed that the highest overall accuracy (60%) was obtained using Sentinel-2A MSI image (10m), followed by PlanetScope (3m) with 48% accuracy, and PlanetScope resampling (6m) with 40% accuracy. This finding indicates that multiresolution images can be used to produce complex coral reef life-form maps with different levels of information details. Keywords: Coral reef; Life-form; Planetscope; Spatial resolution; Classification scheme   Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals Pengaruh Tingkat Kompresi Citra ALOS AVNIR-2 terhadap Akurasi Hasil Transformasi Indeks Vegetasi dan Klasifikasi Penutup Lahan Wilayah Salatiga dan Ambarawa, Jawa Tengah

2020 ◽  
Vol 34 (2) ◽  
pp. 130
Author(s):  
Projo Danoedoro
Keyword(s):  
Image Compression ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Original Data ◽  
Data Availability ◽  
Classification Methods ◽  
Quality Of Data ◽  
Pixel Classification ◽  
Central Java ◽  
Object Based

Abstrak Penggunaan teknik kompresi untuk menghemat ukuran penyimpanan citra digital telah banyak dijumpai dalam aplikasi keseharian. Di sisi lain, kompresi citra juga dapat memberikan konsekuensi berupa kehilangan detil data, yang akan berpengaruh pada integritas data. dan secara teoretis juga akan berpengaruh pada kualitas turunan data.  Penelitian ini mengkaji pengaruh tingkat kompresi citra digital multispektral ALOS-AVNIR2 yang terdiri dari empat saluran dengan resolusi spasial 10 meter terhadap akurasi hasil transformasi indeks vegetasi dan  klasifikasi penutup lahan untuk wilayah Salatiga-Ambarawa, Jawa Tengah.  Citra dikompresi pada sembilan tingkat, yaitu dari tidak kehilangan detil sama sekali (100%, atau sama dengan data asli) hingga 10%, dengan interval 10%. Indeks Vegetasi yang diterapkan meliputi NDVI, TVI dan MSARVI. Klasifikasi multispektral yang diujicobakan meliputi  klasifikasi per-piksel  dan klasifikasi berbasis objek.  Hasil penelitian ini menunjukkan bahwa transformasi indeks vegetasi dan klasifikasi per-piksel mengalami penurunan akurasi secara drastis, sejalan dengan meningkatnya kompresi citra, sementara klasifikasi berbasis objek mengalami perubahan akurasi relatif lebih sedikit dibandingkan analisis per-piksel. Temuan penelitian ini menunjukkan bahwa penggunaan citra terkompresi sebagai masukan proses klasifikasi secara digital sebaiknya dihindari. Meskipun demikian, kalau pun terpaksa dilakukan karena masalah ketersediaan data, maka metode klasifikasi berbasis objeklah yang sebaiknya diterapkan; dan untuk klasifikasi per-piksel maka algoritma jarak minimum terhadap rerata-lah yang  sebaiknya dipilih. Abstract The use of compression techniques for saving storage space of digital imagery has been commonly found in daily applications.  On the other hand, image compression can also provide consequences of losing data details, which will affect data integrity and theoretically will also affect the quality of data derived. This study examined the effect of ALOS-AVNIR2 multispectal image compression level consisting of four channels with 10 m spatial resolution to the accuracies of vegetation index transformation and land cover classification for Salatiga and Ambarawa region, Central Java. This study compressed the image into nine levels, i.e. from lossless details (100%, or equal to original data) up to 10% compression, at 10% intervals. The applied vegetation indices include NDVI, TVI and MSARVI. The multispectral classifications that were piloted include the per-pixel and object-based classification methods. The results of this study indicated that the vegetation index transformation and per-pixel classification have drastically decreased accuracies, in line with the increase in image compression; while the object-based classification has relatively more stable than per-pixel analysis. The findings of this study showed that the use of compressed imagery as an input to digital classification process should be avoided. However, even if it has to be done due to data availability issues, then object-based classification methods should be applied; and especially for per-pixel classification,  the minimum distance to mean algorithm should be chose.

scholarly journals Prediction of Demersal Fishing Ground Associated with Coral Reefs in the Coastal Jepara Regency, Central Java, Indonesia Based on Sentinel 2a Imagery

2019 ◽  
Vol 4 (6) ◽  
pp. 263-269
Author(s):  
Kunarso Kunarso ◽  
Muhammad Zainuri ◽  
Denny Nugroho Sugianto ◽  
Jarot Marwoto ◽  
Hariyadi Hariyadi ◽  
...  
Keyword(s):  
Coral Reefs ◽  
Fishing Ground ◽  
Central Java ◽  
Sentinel 2A

scholarly journals Bagian 1: Kombinasi Metode Klastering dan Klasifikasi (Kasus Pandemi Covid-19 di Indonesia)

2020 ◽  
Vol 4 (3) ◽  
pp. 855
Author(s):  
Agus Perdana Windarto ◽  
Ulfah Indriani ◽  
Mokhamad Ramdhani Raharjo ◽  
Linda Sari Dewi
Keyword(s):  
Decision Tree ◽  
Classification Methods ◽  
Central Java ◽  
Analysis Process ◽  
South Sulawesi ◽  
New Information ◽  
Optimal Value ◽  
Green Zone ◽  
High Cluster

The purpose of this research is to combine the classification and classification methods that are part of data mining. The case raised was the number of the spread of the Covid-19 pandemic in Indonesia as of July 7, 2020 with 34 records. Data sources were obtained from Ministry of Health Data, sampled and processed from covid19.go.id and bnpb.go.id. The variables used in the study are the number of positive cases (x1), number of cases cured (x2) and number of deaths (x3) by province. The classification and classification methods used are k-medoids and C4.5. The k-medoids method works to map clusters of regions in Indonesia by province. The mapping labels used are 3 clusters: high cluster (C1 = red zone), alert cluster (C2 = yellow zone), low cluster (C3 = green zone). The results of the mapping are continued using the C4.5 method to see the rules in the form of a decision tree. The analysis process is assisted with the RapidMiner software. Determination of the number of clusters (k) is determined by using the Davies Bouldin Index (DBI) parameter to optimize the cluster results obtained. For k = 3 has an optimal value of 0.740. The mapping results obtained 9 provinces are in the high cluster (C1 = red zone), 3 provinces are in the alert cluster (C2 = yellow zone) and 22 provinces are in the low cluster (C3 = green zone). The value obtained from the decision tree for cluster height (C1 = red zone) based on C4.5 is if the number of positive cases is smaller than 9524 and greater than 4329 (4329> x1 <9524). The nine provinces included in the high cluster (C1 = red zone) are Aceh, Bali, DKI Jakarta, West Java, Central Java, East Java, South Kalimantan, South Sumatra and South Sulawesi. The results of the combination of these methods can be applied and provide knowledge in the form of new information about mapping in the form of clusters to the distribution of the Covid-19 pandemic in Indonesia

Model Pemberdayaan Usaha Mikro Dan Pengaruhnya Terhadap Kemandirian Usaha

2015 ◽  
Vol 2 (1) ◽  
pp. 75-93
Author(s):  
Mudasetia Hamid ◽  
Evy Rosalina Widyayanti
Keyword(s):  
Human Resources ◽  
Small And Medium Enterprises ◽  
Poverty Rate ◽  
Cultural Centers ◽  
Central Java ◽  
Tourism Education ◽  
The Indian Ocean ◽  
Medium Enterprises ◽  
The City

Yogyakarta is a city and the capital of Yogyakarta Special Region in Java, Indonesia. It is renowned as a center of tourism, education and culture. Yogyakarta is one of the foremost cultural centers of Java. This region is located at the foot of the active merapi vulcano. Yogyakarta is often called the main gateway to the Central Java as where it is geographically located. It stretches from Mount Merapi to the Indian Ocean. This province is one of the most densely populated areas of Indonesia. Yogyakarta is popular tourist destination in indonesia after Bali. These have attracted large number of visitors from across Indonesia and abroad to the city. This status makes Yogyakarta is one of the most heterogeneus cities in Indonesia. In edition, Yogyakarta has attracted large number of people to reside in this city for business. One of these comers is small entrepreneurs with their market munchies enterprise (specially a traditional snack trader). This business is one of famous business in Yogyakarta, we will find rows of pavement vendors selling market munchies. The students and tourists are their main target customers. Market munchies enterprise is part of small and medium enterprises SMEs as livelihood activities. SMEs has an important role in economic growth of Indonesia. Therefore, it is very important to develop and strengthen the micro enterprise empowerment. Micro enterprise empowerment is one of strategy to reduce the poverty rate in Indonesia. Major challenger in implement this program are that micro entrepreneurs are conventional and have satisfied with their revenue. It is very important to develop a comprehensive and sustainable micro enterprise empowerment which consist of strengthen the quality of human resources, maximize the government’s roles, empower the enterprise capital and strengthen the partnership and autonomous. Micro enterprise autonomy will contribute to the economic and investment climate. This will lead to establish an accountable enterprise both for the micro enterprise and customers which at the end will strengthen the development of the micro enterprise in Yogyakarta.Keyword: micro entreprise, human resources, government roles, capital, partnership and autonomous.

Analisis Pengaruh Penerimaan Pajak Daerah Dan Retribusi Daerah Terhadap Pendapatan Asli Daerah Kabupaten/Kota Di Jawa Tengah

2015 ◽  
Vol 2 (1) ◽  
pp. 51-61
Author(s):  
Beta Asteria
Keyword(s):  
Local Government ◽  
Government Budget ◽  
Multiple Regressions ◽  
Independent Variables ◽  
Central Java ◽  
Directorate General ◽  
Fiscal Balance ◽  
Research Findings ◽  
Local Government Budget ◽  
The Impact

This research deals with the impact of Local Tax and Retribution Receipt to Local Government Original Receipt of Regency/City in Central Java from 2008 to 2012. This research utilizes the data of actual of local government budget from Directorate General of Fiscal Balance (Direktorat Jendral Perimbangan Keuangan). Methods of collecting data through census. The number of Regency/City in Central Java are 35. But the data consists of 33 of Regency/City In Central Java from 2008 to 2012. Total of samples are 165. Karanganyar Regency and Sukoharjo Regency were not included as samples of this research because they didn’t report the data of actual of local government budget to Directorate General of Fiscal Balance in 2009.The model used in this research is multiple regressions. The independent variables are Local Tax and Retribution Receipt, the dependent variable is Local Government Original Receipt. The research findings show that Local Tax and Retribution give the significant impact partially and simultaneusly on Local Government Original Receipt at real level 5 percent. All independent variables explain 91,90 percent of the revenue variability while the rest 8,10 percent is explained by other variables.Keywords: Local Tax, Retribution, and Local Government Original Receipt

ECOLOGY AND SUSTAINABLE DEVELOPMENT IN LOCAL BUSINESS:SIMULATION OF LOW IMPACT DEVELOPMENT IN SURAKARTA REGENCY

2018 ◽  
Vol 4 (1) ◽  
pp. 32-38
Author(s):  
Bhimo Rizky Samudro ◽  
Yogi Pasca Pratama
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
River Flow ◽  
Low Impact Development ◽  
Development Model ◽  
Business Activity ◽  
Resources Management ◽  
Central Java ◽  
Specific Culture ◽  
Central Java Province

This paper will describe the function of water resources to support business activities in Surakarta regency, Central Java province. Surakarta is a business city in Central Java province with small business enterprises and specific culture. This city has a famous river with the name is Bengawan Solo. Bengawan Solo is a River Flow Regional (RFR) to support business activities in Surakarta regency. Concious with the function, societies and local government in Surakarta must to manage the sustainability of River Flow Regional (RFR) Bengawan Solo. It is important to manage the sustainability of business activity in Surakarta regency.   According to the condition in Surakarta regency, this paper will explain how the simulation of Low Impact Development Model in Surakarta regency. Low Impact Development is a model that can manage and evaluate sustainability of water resources in River Flow Regional (RFR). Low Impact Development can analys goals, structures, and process water resources management. The system can also evaluate results and impacts of water resources management. From this study, we hope that Low Impact Development can manage water resources in River Flow Regional (RFR) Bengawan Solo.  

PENERAPAN ALGORITMA K-MEANS UNTUK PENGELOMPOKAN DAERAH RAWAN BENCANA DI INDONESIA

2019 ◽  
Vol 1 (2) ◽  
pp. 1-10
Author(s):  
Amril Mutoi Siregar
Keyword(s):  
Natural Disasters ◽  
Accurate Data ◽  
Disaster Victims ◽  
Central Java ◽  
North Sulawesi ◽  
Natural Beauty ◽  
Or Groups ◽  
Disaster Data ◽  
The Government ◽  
Almost All

Indonesia is a country located in the equator, which has beautiful natural. It has a mountainous constellation, beaches and wider oceans than land, so that Indonesia has extraordinary natural beauty assets compared to other countries. Behind the beauty of natural it turns out that it has many potential natural disasters in almost all provinces in Indonesia, in the form of landslides, earthquakes, tsunamis, Mount Meletus and others. The problem is that the government must have accurate data to deal with disasters throughout the province, where disaster data can be in categories or groups of regions into very vulnerable, medium, and low disaster areas. It is often found when a disaster occurs, many found that the distribution of long-term assistance because the stock for disaster-prone areas is not well available. In the study, it will be proposed to group disaster-prone areas throughout the province in Indonesia using the k-means algorithm. The expected results can group all regions that are very prone to disasters. Thus, the results can be Province West java, central java very vulnerable categories, provinces Aceh, North Sumatera, West Sumatera, east Java and North Sulawesi in the medium category, provinces Bengkulu, Lampung, Riau Island, Babel, DIY, Bali, West Kalimantan, North Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, Papua, west Papua including of rare categories. With the results obtained in this study, the government can map disaster-prone areas as well as prepare emergency response assistance quickly. In order to reduce the death toll and it is important to improve the services of disaster victims. With accurate data can provide prompt and appropriate assistance for victims of natural disasters.

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