- Perum Ayodhya Citra II-G3, Yogyakarta 55282, Indonesia
- 2744437886
Wayan Suparta
Universitas Pembangunan Jaya, Informatics, Faculty Member
- GPS meteorology, Space Meteorology and Aerospace Science, Remote Sensing, Smart Systems, GNSS and GPS, Embedded Systems, and 27 moreElectrical and Computer Engineering (ECE), Data Mining, Big Data, Machine Learning, Artificial Neural Networks, Smart environments, Environmental Sustainability, Smart Cities, space weather / Computing science, Space Sciences, Sensors and Sensing, NFC ( Near Field Communication ), Computer Science and Engineering, Information Systems, Time Series Analysis and Forecasting, Weather Forecasting, Smart Farming, Materials Science and Engineering, Microelectronics And Semiconductor Engineering, Electronics & Telecommunication Engineering, Instrumentation and Measurement Science, Microcontrollers, Matlab Programming, R programming language, Python Programming, Raspberry Pi, and Arctic and Antarctic politics, science policy, geopolitics of the polar regions, international relationsedit
- WAYAN SUPARTA is former Associate Professor at the Space Science Centre (ANGKASA), Universiti Kebangsaan Malaysia. Hi... moreWAYAN SUPARTA is former Associate Professor at the Space Science Centre (ANGKASA), Universiti Kebangsaan Malaysia. His expertise is in the field of electrical and electronics, smart systems, and atmospheric remote sensing. He is a teacher, lecturer, and researcher for 24 years working experience. He latest position was a lecturer at the Department of Electrical Engineering, Sanata Dharma University, Yogyakarta, Indonesia (Feb 2018 – July 2018), an Adjunct Professor at the Civil Engineering Department of the University of Technology Yogyakarta (Sep 2017 – Jan 2018), and associates faculty member of Doctor of Computer Science (DCS) at Bina Nusantara University (BINUS), Jakarta from 2015 until now. Before returning to his home country in 2017, he was associated with the National University of Malaysia (Universiti Kebangsaan Malaysia – UKM) in the capacity of Associate Professor from April 4, 2012, to April 3, 2017 (5 years). From April 4, 2017, onwards, he was a de facto full professor at the Space Science Centre (ANGKASA), UKM, but due to high-level political intrigue in UKM with the grounds of financial issues, he decided to resign on June 16, 2017. He believes that if one door of sustenance is closed then another door will open. He started his career as a teacher of Physics, Electronics, and Computer at the senior high school of SMUK Cor Jesu Malang (1994-1997), Indonesia and a teacher at SMUK Santo Aloysius Bandung (1997 – 2000). After difficult to find a job in Indonesia, he eventually became an Engineering Lecturer at the College Legenda Group, Malaysia (2000 – 2004). In 2003, he pursues a Ph.D. in Electrical, Electronic, and System Engineering of UKM and graduated in March 2008. After Post Doctoral Fellow (2007-2008), he appointed as a Senior Lecturer (1 July 2008 – 3 April 2012) at the Institute of Space Science, UKM. During his span carrier in UKM, his research interests involved GPS Meteorology applications, Aerospace Sciences, Communication Systems (Remote Sensing), Artificial Neural Network applications and Space meteorology. Details of his research project, publication, award and networking, and biography can be found at Wikipedia (Dr. Wayan Suparta profile), ORCID repository, or Google Scholar.
WAYAN SUPARTA is former Associate Professor at the Space Science Centre (ANGKASA), Universiti Kebangsaan Malaysia. His expertise is in the field of electrical and electronics, smart systems, atmospheric remote sensing, solar-terrestrial studies and has experienced in Polar Regions observations to quantify the atmospheric water vapor for sun-earth coupling studies. He also studied the estimation of water vapor using neural network techniques for lightning prediction and space launcher recommendation.edit
Consciousness of the importance of accurate weather forecasts and climate modeling to manage our daily activities or to better understand physical processes of the atmosphere, many efforts have been made. Among the activity is monitoring... more
Consciousness of the importance of accurate weather forecasts and climate modeling to manage our daily activities or to better understand physical processes of the atmosphere, many efforts have been made. Among the activity is monitoring and data collection, especially measurements of water vapor in the atmosphere, which was previously using conventional technology. One technology currently being developed and widely used in communications is Global Positioning System (GPS). The new GPS technology can be implemented in the field of meteorology in the early 1990s, where scientists have successfully developed a technique for determining the water vapor by exploiting the signal delays during the propagation from satellite to a receiver, which has significantly improve weather forecasting accuracy. The result of these efforts created a new science, namely GPS Meteorology.
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Water vapor is a key in the hydrological cycle and the main driver of atmospheric events. Remote sensing of water vapor using the Global Positioning System (GPS) is essential for weather and climate research. Advances in GPS application... more
Water vapor is a key in the hydrological cycle and the main driver of atmospheric events. Remote sensing of water vapor using the Global Positioning System (GPS) is essential for weather and climate research. Advances in GPS application has allowed the measurements of ionospheric total electron content (TEC) and precipitable water vapor (PWV) in all weather conditions and on a global scale with fine temporal and spatial resolution. Using TEC as a measure of solar activity and PWV as the terrestrial response, a new approach for determining and quantifying the solar influence on PWV by indirectly correlating the PWV and TEC variations on a time basis is presented in this book. (Imprint: Novinka)
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This paper constructs an adaptive neuro-fuzzy inference system (ANFIS) model to estimate precipitable water vapor (PWV) in Southeast Asia, particularly in the Peninsular Malaysia, Sabah, and Singapore region. The input to the model is... more
This paper constructs an adaptive neuro-fuzzy inference system (ANFIS) model to estimate precipitable water vapor (PWV) in Southeast Asia, particularly in the Peninsular Malaysia, Sabah, and Singapore region. The input to the model is developed using the surface pressure, temperature, and relative humidity. The models are trained and tested using PWV values derived from the global positioning system (GPS). The data used are for May 2012 taken at the Nanyang Technology University of Singapore (NTUS) and Universiti Malaysia Sabah, Kinabalu (UMSK); and for February 2009 taken at the Universiti Kebangsaan Malaysia Bangi (UKMB). The validation process is conducted using June 2012 data for NTUS and UMSK and March 2009 data for UKMB. The performance the ANFIS model is compared with a multilayer perceptron (MLP), Elman neural networks, and multiple linear regression (MLR) models. Results from validations at the three stations showed that the ANFIS model performed well as compared with MLP, ...
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The importance of optimization in the manufacturing industry to achieve efficiency and effectiveness, especially in production machines. This research focuses on developing a real-time monitoring system based on the internet to monitor... more
The importance of optimization in the manufacturing industry to achieve efficiency and effectiveness, especially in production machines. This research focuses on developing a real-time monitoring system based on the internet to monitor power consumption and production machines' speed accurately. The data taken by the real-time monitoring system is integrated into the ERP System, which is stored as time series data of power consumption and engine speed. The importance of making production cost estimates, especially on the power consumption of a production machine for the future, and functioning as a tool for real-time monitoring, the data obtained is processed by regression to get an estimate of the total power consumption for several expected outputs. The data is processed using a linear regression algorithm to predict power consumption at the desired speed on the intended production machine. To get a better tool than the one that existed before, the method in its development uses PIECES analysis. In its implementation, this real-time monitoring system runs according to the objectives, and the data obtained is as expected. This model was developed on production machines in the flexible packaging industry that uses rotogravure machines. It is possible that it can be used on production machines in other industries that use electrical power correlated with the speed measured based on the rotation of a motor.
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Highlights new findings in the field, in conjunction with the 2017 International Conference on Space Science and Communication (IconSpace2017), held in Kuala Lumpur, Malaysia on 3-5 May 2017
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In this paper, we propose and evaluate a web-based software to check similarities of documents. The resemblance value of those documents will be compared based on the percentage of its word resemblance. The similarity value will help to... more
In this paper, we propose and evaluate a web-based software to check similarities of documents. The resemblance value of those documents will be compared based on the percentage of its word resemblance. The similarity value will help to detect plagiarism in documents. Methods used in this application are winnowing algorithm and web-based k-gram. We evaluate the accuracy of the system by comparing the system result with the human result. The differences between the systems and the respondents are 7% with k-gram 25 and 4% with k-gram 20. Moreover, processing time of our application are also discussed.
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University Credibility is one of the important factors in maintaining the sustainability of the university. However, to maintain credibility, trust from the public on service and learning capabilities as well as recognition extensively... more
University Credibility is one of the important factors in maintaining the sustainability of the university. However, to maintain credibility, trust from the public on service and learning capabilities as well as recognition extensively are needed. To measure capability in service and learning can be displayed by the technology used during all activities at the university. In this study, an analysis was conducted on the influence of ICT and Public Recognition on University Credibility by using Multiple Linear Regression test. The data used are questionnaire with 13 items representing three variables, namely Information and Communication Technology (ICT), Public Recognition, and University Credibility. Each instrument that is distributed and obtained was used to test the validity and reliability of the items. The results show that 61% of ICT and Public Recognition are proved to influence the credibility of the university. This shows that ICT and Public Regulations play a significant r...
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This paper observes the characteristics of Antarctic precipitable water vapor (PWV) as a climate parameter affected by solar radiation at Scott Base (77.85°S, 166.76°E), Davis (68.58°S, 77.97°E) and Syowa (69.00°S, 39.58°E) using the... more
This paper observes the characteristics of Antarctic precipitable water vapor (PWV) as a climate parameter affected by solar radiation at Scott Base (77.85°S, 166.76°E), Davis (68.58°S, 77.97°E) and Syowa (69.00°S, 39.58°E) using the ground-based GPS and the surface meteorological measurements. As the Sun plays a central role in the hydrological cycle and precipitation, the associations between water vapor and solar radiation have been studied to understand the current global climate change. Analysis of data gathered for the period from 2003 to 2008 showed that the observations between PWV and solar radiation shows strong relationships with correlation coefficients 0.84 for diffuse and 0.79 for global radiation. Based on their strong relationship, PWV data and analysis have done suggests that GPS can be used as a new approach to study the interaction of solar- induced climate change. As seen in the latest report of the Intergovernmental Panel on Climate Change (IPCC), increased in t...
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Highlights new findings in the field, in conjunction with the 2017 International Conference on Space Science and Communication (IconSpace2017), held in Kuala Lumpur, Malaysia on 3-5 May 2017
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This paper mainly discusses the atmospheric precipitable water vapor (PWV) variation using a ground-based GPS receiver at Antarctic Peninsula to analyze extreme precipitation. Base Bernardo O'Higgins (OHI3), Palmer (PALM) and Rothera... more
This paper mainly discusses the atmospheric precipitable water vapor (PWV) variation using a ground-based GPS receiver at Antarctic Peninsula to analyze extreme precipitation. Base Bernardo O'Higgins (OHI3), Palmer (PALM) and Rothera (ROTH) stations located around Antarctic Peninsula were selected for study. We looked into extreme participation of more than 0.5 mm/hr for three selected days in the months of March and April in the year 2013 where they were categorized as Case 1 (Day 63), Case 2 (Day 90), and Case 3 (Day 104). The GPS PWV showed a delay about 13 to 17 hours as compared to the precipitation peak taken from the Tropical Rainfall Measurement Mission (TRMM). GPS PWV was also compared with the surface meteorological data in order to study the precipitation changes. It is found that Case 1 and Case 3 gave similar change while a small difference was detected in Case 2. The changes were supported by precipitation map from NCEP/NCAR where extreme precipitation occurred at PALM for Case 1 and Case 3, and at ROTH for Case 2.
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The ocean, which covers two-thirds of the land surface, receives heat from the sun's rays. Ocean water also receives heat that comes from geothermal heat, which is magma located under the seafloor. Ocean surface temperatures are... more
The ocean, which covers two-thirds of the land surface, receives heat from the sun's rays. Ocean water also receives heat that comes from geothermal heat, which is magma located under the seafloor. Ocean surface temperatures are warmest near the equator, with temperatures from 25°C to 33°C between 0 degrees and 20 degrees north and south latitude. This temperature difference can be utilized to run the driving machine based on the thermodynamic principle. A technology called Ocean Thermal Energy Conversion (OTEC) is capable of converting the temperature difference into electrical energy. OTEC is a power plant by utilizing the difference in the temperature of seawater on the surface and the temperature of deep seawater. This paper briefly overviews of how ocean heat can be utilized as a renewable energy source to produce electrical energy. The development and exploitation of renewable marine energy in the future are feasible and this will involve multidisciplinary fields such as r...
Ribut cuaca adalah salah satu parameter terpenting yang perlu diperhatikan dalam skenario peluncuran roket atau peluncuran satelit menuju orbitnya. Tulisan ini bertujuan untuk mengukur terjadinya ribut badai berdekatan daerah Selat... more
Ribut cuaca adalah salah satu parameter terpenting yang perlu diperhatikan dalam skenario peluncuran roket atau peluncuran satelit menuju orbitnya. Tulisan ini bertujuan untuk mengukur terjadinya ribut badai berdekatan daerah Selat Makassar sebagai langkah awal untuk membangun model badai cuaca dalam rangka peluncuran satelit. Data meteorologi permukaan harian seperti tekanan, suhu, kelembaban relatif, tutupan awan, uap air, kecepatan angin dan arahnya telah dianalisis. Analisis juga mempertimbangkan musim kemarau dan musim hujan di dekat kawasan target peluncuran. Hasil penelitian menunjukkan bahwa aktivitas ribut badai pada bulan Mei dan Oktober terdeteksi lebih tinggi daripada bulan-bulan lainnya. Investigasi awal ditemukan bahwa aktivitas ribut badai di daerah ini lebih dipengaruhi oleh kelembaban relatif dan uap air, khususnya di musim peralihan (Monsun). Sementara bulan-bulan yang diprediksi aman untuk peluncuran roket adalah Juni, Juli, dan Agustus.
Adaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between... more
Adaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between ANFIS and Global Positioning System (GPS) for areas in Pekan, Pahang, Malaysia. The PWV value was estimated with the ANFIS model with the surface meteorological data as inputs. The accuracy of PWV from ANFIS has been validated with PWV from GPS measurements for the period of 2010. The result showed that the ANFIS PWV has a similar trend with the GPS PWV (r = 0.999 at the 99% confidence level) and found a difference of 0.024%. The PWV from ANFIS was calculated 0.035% higher compared to GPS PWV and found a similar character in two seasonal monsoons. This indicates that the PWV obtained with ANFIS model agreed very well with GPS measurements and it can be implemented to monitor atmospheric variability as well as climate change studies in the absence o...
Job satisfaction can be fulfilled if there is a good organizational culture and high commitment from employees. The purpose of this study was to analyze the influence of organizational culture and organizational commitment on ITDC... more
Job satisfaction can be fulfilled if there is a good organizational culture and high commitment from employees. The purpose of this study was to analyze the influence of organizational culture and organizational commitment on ITDC employee job satisfaction. The population of this study were all employees at ITDC. The sample selection process uses a total sampling method with the criteria of permanent employees who have worked for at least 2 years at ITDC, so that respondents have had experience working at PT ITDC so that they have experience and are able to feel the organizational culture at PT ITDC. Based on these criteria, the number of samples in this study were 117 permanent ITDC employees. The results of the analysis show that organizational culture and organizational commitment have a positive and significant effect on ITDC employee job satisfaction. This means that the better organizational culture that exists at ITDC, and the higher the commitment that ITDC employees have, t...
The Antarctic continent is known to be an unpopulated region due to its extreme weather and climate conditions. However, the air quality over this continent can be affected by long-lived anthropogenic pollutants from the mainland. The... more
The Antarctic continent is known to be an unpopulated region due to its extreme weather and climate conditions. However, the air quality over this continent can be affected by long-lived anthropogenic pollutants from the mainland. The Argentinian region of Ushuaia is often the main source area of accumulated hazardous gases over the Antarctic Peninsula. The main objective of this study is to report the first in situ observations yet known of surface ozone (O3) over Ushuaia, the Drake Passage, and Coastal Antarctic Peninsula (CAP) on board the RV Australis during the Malaysian Antarctic Scientific Expedition Cruise 2016 (MASEC'16). Hourly O3 data was measured continuously for 23 days using an EcoTech O3 analyzer. To understand more about the distribution of surface O3 over the Antarctic, we present the spatial and temporal of surface O3 of long-term data (2009-2015) obtained online from the World Meteorology Organization of World Data Centre for greenhouse gases (WMO WDCGG). Furt...
Research Interests: Environmental Monitoring, Air pollution, Climate, Biological Sciences, Environmental Sciences, and 15 moreCarbon Monoxide, CHEMICAL SCIENCES, South Pole, Surface Ozone, Antartica, Ozone, Antarctic Peninsula, Seasons, Drake Passage, Air Pollutants, Photochemical Processes, Ushuaia, Coastal Antarctic Peninsula, WDCGG, and depletion process of surface
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This study was based on 23-year period time series data from 1983 to 2005. An OLS method was applied to test the hypothesis that economic variables such as private investments, local government spending, prices of domestic goods, prices... more
This study was based on 23-year period time series data from 1983 to 2005. An OLS method was applied to test the hypothesis that economic variables such as private investments, local government spending, prices of domestic goods, prices of goods and income of the adjacent regions (Jakarta and South Sumatera Province) have effects on the economic growth of Lampung Province. The results of this study show that the independent economic variables have significant effects on the economic growth of Lampung Province. It is found that the interregional linkage is important. A comparison of the two neighbor provinces shows that the linkage of Lampung’s economy with Jakarta’s economy is relatively stronger than that with South Sumatera Province. The implication of this study is that the provincial and local governments of Lampung as well as the people of Lampung should make better use of the spatial spillover effect of DKI Jakarta and South Sumatera Province.
This chapter explains in detail the theoretical background of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The detailed explanation of this method will highlight its importance in the estimation of... more
This chapter explains in detail the theoretical background of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The detailed explanation of this method will highlight its importance in the estimation of ZTD model. Generally, an artificial neural network (ANN) is a system developed for information processing, where it has a similar way with the characteristics of biological neural systems. It was developed based on the human brain, which is capable of processing information, which are complex, nonlinear, and being able to work in parallel, distributed, and local processing and adaptation. ANN is designed to resemble the brain systems such as the construction of architectural structures, learning techniques, and operating techniques. This is the reason that ANN has been widely adopted by scientists because of its accuracy and its ability to develop complex nonlinear models and is used to solve a wide variety of tasks, especially in the field of climate and weather. This section will discuss the capabilities of ANN such as neurons modeling, architecture, and its learning process. 2.1.1 Neuron Modeling In the human brain, there are neurons that are interconnected to one another. These neurons act as a tool that can perform processing of information of human senses. Haykin (2009) described that a biological neuron consists of a cell body, where
Global Positioning System (GPS) receivers are widely installed throughout the Peninsular Malaysia, but the implementation formonitoringweather hazard systemsuch as flash flood is still not optimal. To increase the benefit... more
Global Positioning System (GPS) receivers are widely installed throughout the Peninsular Malaysia, but the implementation formonitoringweather hazard systemsuch as flash flood is still not optimal. To increase the benefit formeteorological applications, the GPS system should be installed in collocationwith meteorological sensors so the precipitablewater vapor (PWV) can bemeasured. The distribution of PWV is a key element to the Earth's climate for quantitative precipitation improvement as well as flash flood forecasts. The accuracy of this parameter depends on a large extent on the number of GPS receiver installations andmeteorological sensors in the targeted area. Due to cost constraints, a spatial interpolationmethod is proposed to address these issues. In this paper, we investigated spatial distribution of GPS PWV and meteorological variables (surface temperature, relative humidity, and rainfall) by using thin plate spline (tps) and ordinary kriging (Krig) interpolation techniques over the Klang Valley in Peninsular Malaysia (longitude: 99.5°–102.5°E and latitude: 2.0°–6.5°N). Three flash flood cases in September, October, and December 2013were studied. The analysiswas performed using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) to determine the accuracy and reliability of the interpolation techniques. Results at different phases (pre, onset, and post) that were evaluated showed that tps interpolation technique is more accurate, reliable, and highly correlated in estimating GPS PWV and relative humidity, whereas Krig is more reliable for predicting temperature and rainfall during pre-flash flood events. During the onset of flash flood events, both methods showed good interpolation in estimating all meteorological parameters with high accuracy and reliability. The finding suggests that the proposed method of spatial interpolation techniques are capable of handling limited data sources with high accuracy, which in turn can be used to predict future floods.