Arif Kurnia Wijayanto, S.TP, M.Sc

@article{nokey,

title = {Machine Learning for Precise Rice Variety Classification in  Tropical Environments Using UAV-Based Multispectral Sensing},

author = {Arif K Wijayanto and Ahmad Junaedi and Azwar A Sujaswara and Miftakhul B.R. Khamid and Lilik B Prasetyo and Chiharu Hongo and Hiroaki Kuze},

url = {http://algm.ipb.ac.id/wp-content/uploads/2025/02/agriengineering-05-00123.pdf},

doi = {10.3390/agriengineering5040123},

issn = {2624-7402},

year  = {2023},

date = {2023-11-01},

urldate = {2023-11-01},

journal = {AgriEngineering},

volume = {5},

pages = {2000-2019},

abstract = {An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types at different ages (six, nine, and twelve weeks after planting), analyzing data from four spectral bands and vegetation indices using various algorithms for classification. The results show that the neural network (NN) algorithm is superior, achieving an area under the curve value of 0.804. The twelfth week post-planting yielded the most accurate results, with green reflectance the dominant predictor, surpassing the traditional vegetation indices. This study demonstrates the rapid and effective classification of rice varieties using UAV-based multispectral sensors and NN algorithms to enhance agricultural practices and global food security.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Prasetyo2022,

title = {Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches},

author = {Lilik B Prasetyo and Yudi Setiawan and Aryo Adhi Condro and Kustiyo and Eriyanto Indra Putra and Nur Hayati and Arif K Wijayanto and Almi Ramadhi and Daniel Murdiyarso},

url = {https://www.mdpi.com/1999-4907/13/6/828},

doi = {10.3390/f13060828},

year  = {2022},

date = {2022-05-25},

journal = {Forests},

volume = {13},

number = {6},

abstract = {In recent decades, catastrophic wildfire episodes within the Sumatran peatland have contributed to a large amount of greenhouse gas emissions. The El-Nino Southern Oscillation (ENSO) modulates the occurrence of fires in Indonesia through prolonged hydrological drought. Thus, assessing peatland vulnerability to fires and understanding the underlying drivers are essential to developing adaptation and mitigation strategies for peatland. Here, we quantify the vulnerability of Sumatran peat to fires under various ENSO conditions (i.e., El-Nino, La-Nina, and Normal phases) using correlative modelling approaches. This study used climatic (i.e., annual precipitation, SPI, and KBDI), biophysical (i.e., below-ground biomass, elevation, slope, and NBR), and proxies to anthropogenic disturbance variables (i.e., access to road, access to forests, access to cities, human modification, and human population) to assess fire vulnerability within Sumatran peatlands. We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). We found that the ensemble model performed better compared to a single algorithm for depicting fire vulnerability within Sumatran peatlands. The NBR highly contributed to the vulnerability of peatland to fire in Sumatra in all ENSO phases, followed by the anthropogenic variables. We found that the high to very-high peat vulnerability to fire increases during El-Nino conditions with variations in its spatial patterns occurring under different ENSO phases. This study provides spatially explicit information to support the management of peat fires, which will be particularly useful for identifying peatland restoration priorities based on peatland vulnerability to fire maps. Our findings highlight Riau’s peatland as being the area most prone to fires area on Sumatra Island. Therefore, the groundwater level within this area should be intensively monitored to prevent peatland fires. In addition, conserving intact forests within peatland through the moratorium strategy and restoring the degraded peatland ecosystem through canal blocking is also crucial to coping with global climate change.},

keywords = {ENSO, fire, land fire, peat land},

pubstate = {published},

tppubtype = {article}

}

@article{Setiawan2020,

title = {The effect of utilization patterns of green open space on the dynamics change of air quality due to the Covid-19 pandemic in Jabodetabek region},

author = {Yudi Setiawan and Siti Badriyah Rushayati and Rachmad Hermawan and Lilik B Prasetyo and Arif K Wijayanto},

url = {https://journal.ipb.ac.id/index.php/jpsl/article/view/32550},

doi = {10.29244/jpsl.10.4.559-567},

issn = {2460-5824},

year  = {2020},

date = {2020-12-31},

journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)},

volume = {10},

number = {4},

pages = {559-567},

abstract = {The  Covid-19  pandemic  has  had  a  global  impact  on  all  sectors including the environment. The spread of covid-19 is very much influenced by human activity and mobility. Human activities are also closelyrelated to air pollutant emissions. High concentrations of air pollutants during the Covid-19 pandemic will increase the risk of being exposed to Covid-19. Jakarta and its  surroundingarea (known  locally  as  Jabodetabek) havehigh  population density.  Thesecities  are  economic  and  industrial  centers.  Air  pollutant emissions in these cities are very high. High concentrations of air pollutants during  the  Covid-19  pandemic  will  increase  the  risk  of  being  exposed  to Covid. To anticipate this problem, the government made a Large-Scale Social Restriction Policy (PSBB). Limited human activities, in addition to having an impact  on  reducing  the  risk  of  humans  being  exposed  to  Covid-19  from  the droplets  released  by tested-positive  of  Covid-19,  also  have  an  impact  on reducing emissions of air pollutants so that they can reduce the risk of being exposed  to  Covid-19. Several  variables  that  influence  vulnerability  and  risk to exposure to Covid-19 are the distribution of settlements, roads, economic centers  (markets,  business  centers,  industrial  centers),  and  human  mobility. In this study, we will also analyze the role of green open space on the risk of exposure to Covid-19. Green open space plays an important role in reducing air pollutants so that it will also affect the risk of being exposed to Covid-19. This study aimedto 1) examine the distribution of air pollutants based on the vulnerability  and  risk  of  COVID-19  in  Jakarta,Bogor,  Depok,  Tangerang, and Bekasi (Jabodetabek), and 2) examine the results of the overlay between land cover and vulnerability and risk to Covid-19},

keywords = {Covid-19, Kualitas udara, ruang terbuka hujau},

pubstate = {published},

tppubtype = {article}

}

@article{Wijayanto2020,

title = {Jakarta and Surabaya land surface temperature before and during the Covid-19 pandemic},

author = {Arif K Wijayanto and Siti Badriyah Rushayati and Rachmad Hermawan and Yudi Setiawan and Lilik B Prasetyo},

url = {http://www.aes.bioflux.com.ro/docs/2020.213-221.pdf},

issn = {2066-7647},

year  = {2020},

date = {2020-12-02},

journal = {AES Bioflux},

volume = {12},

number = {3},

pages = {213-221},

abstract = {The first incidence of the novel coronavirus or Covid-19 was reported in late 2019, and in the following year, the disease was declared a global pandemic. In Indonesia, the first case was reported in early March, 2020, and ever since, the government has appealed to the public to reduce outdoor activities in order to curtail the spread of the virus. Consequently, many companies and institutions implemented the ‘Work from Home’ (WFH) policy. At the end of April, the provincial government of Jakarta issued large-scale social restrictions, locally called PSBB. These restrictions were later implemented in other cities such as Surabaya. Jakarta was the epicentre of the spread of the virus in Indonesia, followed by Surabaya, the second largest city in the country. Therefore, this study aimed to analyze the Thermal Humidity Index (THI) of both cities, before and during the pandemic. Data were obtained from the MODIS Terra Land Surface Temperature and Emissivity 8-Day Global 1km, from the 1st to 14th May, 2019 (before the pandemic), and during the same period the following year (during the pandemic). Furthermore, data analysis was carried out using Google Earth Engine (GEE), a cloud-based platform for geo-spatial data analysis. The hypothesis in this study was that the social restriction policy caused a difference in the THI before and during the pandemic. Therefore, this hypothesis was proven by the results, as the policy caused a decrease in the THI during the pandemic.},

keywords = {Covid-19, Land Surface Temperature, urban heat island},

pubstate = {published},

tppubtype = {article}

}

@article{Condro2020,

title = {Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform},

author = {Aryo Adhi Condro and Yudi Setiawan and Lilik B Prasetyo and Rahmat Pramulya and Lasriama Siahaan},

url = {https://www.mdpi.com/2073-445X/9/10/377https://algm.ipb.ac.id/wp-content/uploads/2020/11/land-09-00377.pdf},

doi = {10.3390/land9100377},

year  = {2020},

date = {2020-10-08},

journal = {Land},

volume = {9},

number = {10},

pages = {377},

abstract = {Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia.},

keywords = {commodity, GEE},

pubstate = {published},

tppubtype = {article}

}

@article{Irlan2020,

title = {Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data},

author = {Irlan and Muhammad Buce Saleh and Lilik B Prasetyo and Yudi Setiawan},

url = {http://journal.ipb.ac.id/index.php/jmht/article/view/30179},

doi = {10.7226/jtfm.26.2.123},

issn = {2089-2063},

year  = {2020},

date = {2020-08-13},

journal = {Jurnal Manajemen Hutan Tropika},

volume = {26},

number = {2},

pages = {123-132},

abstract = {Application of LiDAR for tree detection and tree canopy segmentation has been widely used in conifer plantation forest in temperate countries with high accuracy, however its application on tropical natural forest especially peat swamp forest hardly found. The objective of this study was evaluated algorithms of individual tree detection and canopy segmentation used LiDAR data in peat swamp forest. The algorithms included (a) Local Maxima (LM) with various variable window size combined with growing region, (b) LM with various variable window size combined with Voronoi Tessellation, (c) LM with various fixed window size combined with growing region, (d) LM with various fixed window size combined with Voronoi Tessellation, and (e) Tree Relative Distance algorithm. The results show that algorithm with the best accuracy was the Tree Relative Distance algorithm with the highest overall F-score of 0.63. The tree relative distance algorithm also provides the highest accuracy in determining three tree parameters which are position, height and diameter of tree canopy with a RMSE value 1.08 m, 6.45 m and 1.19 m, respectively.},

keywords = {LiDAR, peat swamp, segmentation},

pubstate = {published},

tppubtype = {article}

}

@article{Hultera2020,

title = {Spatial Model Of The Deforestation Potential 2020 & 2024 And The Prevention Approach, Kutai Barat District},

author = {Hultera and Lilik B Prasetyo and Yudi Setiawan},

url = {https://journal.ipb.ac.id/index.php/jpsl/article/view/29821},

doi = {10.29244/jpsl.10.2.294-306},

issn = {2086-4639},

year  = {2020},

date = {2020-07-03},

journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan},

volume = {10},

number = {2},

pages = {294-306},

abstract = {Kutai Barat have high forest cover and high deforestation rates. The study purpose to make spatial model, potential distribution of deforestation 2020 and 2024, analysis of the drivers of deforestation, compile and map the approach to reducing deforestation. Deforestation modeling done using MaxEnt and Zonation software. Deforestation sample data used from land cover maps 2009, 2013 and 2016. Deforestation rates used to estimate potential deforestation 2020 and 2024. The drivers of deforestation analyze from land cover change matrix. Prevention strategy approach by overlaying potential deforestation modeling results with RTRW maps. The model has good performance with AUC value 0.873. The validation show very good accuracy for the prediction of area to be deforested by 94%, the accuracy of the spatial distribution of the model 31%. Environmental variables have the highest contribution to the model is the distance from previous deforestation 37.4%. The potential of deforestation 2020 is 85,908 ha and 171,778 ha 2024. Oil palm, agriculture, rubber, HTI and mining are the driver of deforestation. Social forestry is expected to prevent potential deforestation 120,861 ha. Others expected programs to contribute to the deforestation reduction are community land intensification 30,316 ha and implementation of the HCV in plantation 20,120 ha.},

keywords = {deforestation},

pubstate = {published},

tppubtype = {article}

}

@article{Arai2019,

title = {Method for Uncertainty Evaluation of Vicarious Calibration of Spaceborne Visible to Near Infrared Radiometers},

author = {Kohei Arai and Wahyudi Hasbi and A Hadi Syafrudin and Patria Rachman Hakim and Sartika Salaswati and Lilik B Prasetyo and Yudi Setiawan},

url = {https://thesai.org/Publications/ViewPaper?Volume=10&Issue=1&Code=ijacsa&SerialNo=51},

doi = {10.14569/IJACSA.2019.0100151},

year  = {2019},

date = {2019-01-01},

journal = {International Journal of Advanced Computer Science and Applications},

volume = {10},

number = {1},

pages = {387-393},

abstract = {A method for uncertainty evaluation of vicarious calibration for solar reflection channels (visible to near infrared) of spaceborne radiometers is proposed. Reflectance based at sensor radiance estimation method for solar reflection channels of radiometers onboard remote sensing satellites is also proposed. One of examples for vicarious calibration of LISA: Line Imager Space Application onboard LISAT: LAPAN-IPB Satellite is described. Through the preliminary analysis, it is found that the proposed uncertainty evaluation method is appropriate. Also, it is found that percent difference between DN: Digital Number derived radiance and estimated TOA: Top of the Atmosphere radiance (at sensor radiance) ranges from 3.5 to 9.6 %. It is also found that the percent difference at shorter wavelength (Blue) is greater than that of longer wavelength (Near Infrared: NIR). In comparison to those facts to those of Terra/ASTER/VNIR, it is natural and reasonable.},

keywords = {Field experiment, image quality evaluation, vicarious calibration},

pubstate = {published},

tppubtype = {article}

}

@article{setiawan2018identifying,

title = {Identifying Areas Affected By Fires In Sumatra Based On Time Series Of Remotely Sensed Fire Hotspots And Spatial Modeling},

author = {Yudi Setiawan and Lilik B Prasetyo and Hidayat Pawitan and Prita A Permatasari and Desi Suyamto and Arif K Wijayanto},

url = {http://journal.ipb.ac.id/index.php/jpsl/article/view/24760},

doi = {10.29244/jpsl.8.3.420-427},

issn = {2460-5824},

year  = {2018},

date = {2018-01-01},

journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)},

volume = {8},

number = {3},

pages = {420--427},

abstract = {Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots.},

keywords = {fire, hotspot},

pubstate = {published},

tppubtype = {article}

}

@article{setiawan2018pemanfaatan,

title = {Pemanfaatan Fusi Data Satelit Lapan-a3/IPB dan Landsat 8 Untuk Monitoring Lahan Sawah},

author = {Yudi Setiawan and Lilik B Prasetyo and Hidayat Pawitan and Liyantono Liyantono and Syartinilia Syartinilia and Arif K Wijayanto and Prita A Permatasari and Hadi A Syafrudin and Patria R Hakim},

url = {https://journal.ipb.ac.id/index.php/jpsl/article/view/19754},

doi = {10.29244/jpsl.8.1.67-76},

issn = {2460-5824},

year  = {2018},

date = {2018-01-01},

journal = {Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)},

volume = {8},

number = {1},

pages = {67--76},

abstract = {Increasing of economic development is generally followed by the change of landuse from agriculture to other function. If it occurs in large frequency and amount, it will threaten national food security. Therefore, it is necessary to monitor the agricultural land, especially paddy fields regarding to changes in landuse and global climate. Utilization and development of satellite technology is necessary to provide more accurate and independent database for agricultural land monitoring, especially paddy fields. This study aims to develop a utilization model for LAPAN-IPB satellite (LISAT) and other several satellites data that have been used for paddy field monitoring. This research is conducted through 2 stages: 1) Characterization LISAT satellite data to know spectral variation of paddy field, and 2) Development method of LISAT data fusion with other satellites for paddy field mapping. Based on the research results, the characteristics Red and NIR band in LISAT data imagery have a good correlation with Red and NIR band in LANDSAT 8 OLI data imagery, especially to detect paddy field in the vegetative phase, compared to other bands. Observation and measurement of spectral values using spectroradiometer need to be conducted periodically (starting from first planting season) to know the dynamics of the change related to the growth phase of paddy in paddy field. Pre-processing of image data needs to be conducted to obtain better LISAT data characterization results. Furthermore, it is necessary to develop appropriate algorithms or methods for geometric correction as well as atmospheric correction of LISAT data.},

keywords = {Landsat, LAPAN},

pubstate = {published},

tppubtype = {article}

}

@article{wijayanto2016mobile,

title = {Mobile-based Expert System for Selecting Broiler Farm Location Using PostGIS},

author = {Arif K Wijayanto and Kudang B Seminar and Rudi Afnan},

url = {http://www.journal.uad.ac.id/index.php/TELKOMNIKA/article/view/2903},

doi = {10.12928/telkomnika.v14i1.2903},

issn = {23-2-9293},

year  = {2016},

date = {2016-01-01},

journal = {TELKOMNIKA Indonesian Journal of Electrical Engineering},

volume = {14},

number = {1},

pages = {360-367},

abstract = {Massive development of broiler farms has led to many socio-environmental problems. Based on idea that broiler farm must be located at suitable location, an expert system for site selection based on the socio-environmental factors and sustainable principles is urgently needed to cope with this problem. The objective of this research was to develop a mobile-based expert system as a guidance for broiler farmers to choose best location for broiler farm. There were four factors considered in the system: 1) ecology and environmental impact, 2) economic and infrastructure, 3) natural condition, and 4) natural disaster vulnerability, each of which consists of sub-factors. A mobile-based expert system has been developed by using opensource web GIS server and PostgreSQL/PostGIS, and can be installed on Android device. As conclusion, a mobile-based expert system has been developed and can be used to determine suitable location for broiler farm development.},

keywords = {broiler, PostGIS},

pubstate = {published},

tppubtype = {article}

}

@article{wijayanto2015suitability,

title = {Suitability Mapping for Broiler Closed House Farm Using Analytical Hierarchy Process and Weighted Overlay with Emphasize on Environmental Aspects},

author = {Arif K Wijayanto and Kudang B Seminar and Rudi Afnan},

url = {https://scialert.net/abstract/?doi=ijps.2015.577.583},

doi = {10.3923/ijps.2015.577.583},

year  = {2015},

date = {2015-01-01},

journal = {International Journal of Poultry Science},

volume = {14},

number = {10},

pages = {577},

publisher = {Asian Network for Scientific Information (ANSINET)},

abstract = {Massive development of broiler farms has led to many socio-environmental problems. A mapping based on the socio-environmental factors and sustainable principles is urgently needed to cope with this problem. The objective of this research was to create a suitability map for broiler farm development in Parung region, Indonesia-as study area. There were four factors considered in the mapping: (1) ecology and environmental impact, (2) economic and infrastructure, (3) natural condition and (4) natural disaster vulnerability, each of which consists of sub-factors. An Analytical Hierarchy Process (AHP) by using pairwise comparison method was applied to determine weight of each factor and sub-factor based on experts’ valuation. From the AHP process, natural condition was considered as the most important factor, followed by ecological and environmental impact factor. By considering weights resulted from the AHP, the spatial analysis and weighted overlay by GIS software were applied in the data processing and suitability map building. Suitability map for broiler farm in Parung region has been created and can be used as guidance for broiler farm development and also for local government as decision support tool to manage the farming area concerning ecology and environment factor.},

keywords = {AHP, broiler},

pubstate = {published},

tppubtype = {article}

}