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Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS Wayan Suparta; Wahyu Sasongko Putro
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.

Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS Wayan Suparta; Wahyu Sasongko Putro
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.

Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS Wayan Suparta; Wahyu Sasongko Putro
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.

Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

ANALISIS AWAL KEBAKARAN TANGKI 36 T-102 PERTALITE PADA KAWASAN REFINERY UNIT IV CILACAP – JAWA TENGAH Budi Utama; Wayan Suparta; Dulhadi Dulhadi
KURVATEK Vol 7 No 1 (2022): Energy Management and Sustainable Environment
Publisher : Institut Teknologi Nasional Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33579/krvtk.v7i1.3129

This paper analyzes an induced lightning strike which is thought to have caused a fire in tank 36 T-102 (containing pertalite) at Pertamina refinery unit IV Cilacap – Central Java. This tank fire incident occurred when heavy rain was accompanied by a thunderstorm, on Saturday, November 13, 2021. The statement issued by BMKG that the fire occurred was caused by the phenomenon of lightning induction to the pertalite fuel storage tank, namely lightning induction at the first point a distance of 45 km and at the second point is 12 km. The analysis was carried out by simulation using the Matlab program which was oriented to determine the lightning induced voltage that invades the metal structure of the tank and compares it with the ignition voltage of the 36 T-102 tank. As an indicator parameter for the occurrence of ignition of flammable fuel, simulations of lightning induced voltages as far as 120 meters are also carried out for comparison parameters. The simulation results with comparison indicators revealed that there was a fire in the pertalite fuel tank in tank 36 T-102 it is not caused by lightning strikes as far as 45 km and as farl as 12 km but possibly due to induced lightning strikes in an area as far of hundreds of meters, the sample comparison indicator as far as 120 meters.

A low-cost development of automatic weather station based on Arduino for monitoring precipitable water vapor Wayan Suparta; Aris Warsita; Ircham Ircham
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp744-753

Water vapor is the engine of the weather system. Continuous monitoring of its variability on spatial and temporal scales is essential to help improve weather forecasts. This research aims to develop an automatic weather station at low cost using an Arduino microcontroller to monitor precipitable water vapor (PWV) on a micro-scale. The surface meteorological data measured from the BME280 sensor is used to determine the PWV. Our low-cost systems also consisted of a DS3231 real-time clock (RTC) module, a 16×2 liquid crystal display (LCD) module with an I2C, and a micro-secure digital (micro-SD) card. The core of the system employed the Arduino Uno surface mount device (SMD) R3 board. The measurement results for long-term monitoring at the tested sites (ITNY and GUWO) found that the daily mean error of temperature and humidity values were 1.30% and 3.16%, respectively. While the error of air pressure and PWV were 0.092% and 2.61%, respectively. The PWV value is higher when the sun is very active or during a thunderstorm. The developed weather system is also capable of measuring altitude on pressure measurements and automatically stores daily data. With a total cost below 50 dollars, all major and support systems developed are fully functional and stable for long-term measurements.

Pengamatan Badai Cuaca Di Selat Makassar Untuk Mendukung Aktivitas Peluncuran Satelit Wayan Suparta
WIDYAKALA: JOURNAL OF PEMBANGUNAN JAYA UNIVERSITY Vol 6, No 1 (2019): Urban Development & Urban Lifestyle
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat UPJ

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.

Marine Heat as a Renewable Energy Source Wayan Suparta
WIDYAKALA: JOURNAL OF PEMBANGUNAN JAYA UNIVERSITY Vol 7, No 1 (2020): Urban Development & Urban Lifestyle
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat UPJ

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 robotics and informatics.

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