Marine Heat as a Renewable Energy Source

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

Development of quadcopter for atmospheric data collection

This research aims to develop a quadrotor system as unmanned aircraft vehicles (UAVs, or drones) for monitoring atmospheric conditions in a targeted area. The system consists of an APM 2.8 arducopter flight controller, Ublox NEO M8N GPS module with compass, Racerstar 920kV 2-4S Brushless Motor, Flysky Receiver FS-iA6B with FS-i6 Remote Control Transmitter, DJI F450 quadcopter frame kits with tall landing gear skid, and a LiPo Battery 3300 mAh 35C. The system is set up and run through a Mission Planner. As for monitoring atmospheric conditions, the system consists of an Arduino Uno ATmega328P, BME280 sensors, and several modules (DS3231 Real-Time Clock (RTC),  micro SD card, and 16×2 LCD). Our vehicle with a total weight of 1 kg can fly into space and maneuver to an altitude of more than 200 meters in an average of 10 minutes. Atmospheric conditions such as air temperature, relative humidity, air pressure, altitude, and precipitable water vapor can be measured and logged properly from drones. By this development, the system can be applied in the future to detect or measure weather extremes, air pollution, or monitoring aerial topography automatically when equipped with gas sensors and cameras, respectively.This research aims to develop a quadrotor system as unmanned aircraft vehicles (UAVs, or drones) for monitoring atmospheric conditions in a targeted area. The system consists of an APM 2.8 arducopter flight controller, Ublox NEO M8N GPS module with compass, Racerstar 920kV 2-4S Brushless Motor, Flysky Receiver FS-iA6B with FS-i6 Remote Control Transmitter, DJI F450 quadcopter frame kits with tall landing gear skid, and a LiPo Battery 3300 mAh 35C. The system is set up and run through a Mission Planner. As for monitoring atmospheric conditions, the system consists of an Arduino Uno ATmega328P, BME280 sensors, and several modules (DS3231 Real-Time Clock (RTC),  micro SD card, and 16×2 LCD). Our vehicle with a total weight of 1 kg can fly into space and maneuver to an altitude of more than 200 meters in an average of 10 minutes. Atmospheric conditions such as air temperature, relative humidity, air pressure, altitude, and precipitable water vapor can be measured and logged properly from drones. By this development, the system can be applied in the future to detect or measure weather extremes, air pollution, or monitoring aerial topography automatically when equipped with gas sensors and cameras, respectively

Pengamatan Badai Cuaca Di Selat Makassar Untuk Mendukung Aktivitas Peluncuran Satelit

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

Estimation of Atmospheric Water Vapor from ANFIS Technique and Its Validation with GPS Data

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 of GPS data.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 of GPS data

Analisis Komparasi Hapiness Index 5 Negara di Asean

The limitation of economic indicators in representing the level of community welfare has increased the world's attention to social aspects of development. Development progress, which has been seen more by economic indicators, such as economic growth and poverty reduction, is considered insufficient to reflect the right level of welfare. This study aims to determine the effect of GDP per capita, environmental index, and unemployment on the happiness index of 9 countries in ASEAN. Estimation results show that the variable GDP per capita significantly and negatively influences the happiness index. The environmental index has a positive effect on the Happiness Index, and unemployment has a positive impact on the happiness index. Based on the results of special effects, there are individual effect values ​​in 9 ASEAN countries. Singapore is the country with the most significant personal impact, and the Philippines is the country with the smallest particular effect. &nbsp

Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS

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%

Hubungan Tenaga Kerja,Rumah Tangga Dan Produksi Perikanan Dalam Aglomerasi Industri Di Kabupaten Tanggamus

This study aims to research the causality relationship between industrial agglomeration in labor, fisheries production, and fisheries households in Tanggamus district. The agglomeration index measured using the Balassa Hoover Index analysis tool and Geographic Information System (GIS) using Geoda. The data used are secondary data obtained directly from the Central Statistics Agency (BPS), the Department of Fisheries and Maritime Affairs of Tanggamus Regency. The number of cross-section data is 20 districts in the period 2012-2016. analyzed using the Granger Causality Analysis method. The Hoover Ballassa index results show a strong degree of agglomeration in only 6 districts. The results of the analysis of the relationship between states of origin state that there is a unidirectional causality between the agglomeration and labor variables, which statistically significantly influence the agglomeration and do not apply otherwise. Unidirectional causality occurs between agglomeration variables and fisheries production only fisheries production which statistically significantly influences agglomeration and does not apply vice versa. Unidirectional causality occurs between agglomeration variables and fisheries households ie only fishery households that statistically significantly influence agglomeration and do not apply otherwise

Pengaruh Persentase Orang Bekerja, Inflasi dan IPM Terhadap Indeks Kebahagiaan Pulau Sumatera

Fokus global pada aspek sosial dan pembangunan telah meningkat sebagai akibat dari keterbatasan dalam menggunakan indikator ekonomi untuk menggambarkan kesejahteraan masyarakat secara menyeluruh. Penelitian ini melakukan analisis data panel menggunakan Random Effect Model. Tujuan dari penelitian ini adalah untuk menemukan faktor-faktor yang mempengaruhi kebahagiaan penduduk Pulau Sumatera pada tahun 2014, 2017, dan 2021. Hasil penelitian menunjukkan bahwa persentase orang yang bekerja berdampak positif pada indeks kebahagiaan Pulau Sumatera. tetapi negatif oleh inflasi. Selain itu, variabel Indeks Pembangunan Manusia (IPM) di Pulau Sumatera berpengaruhi positif dan signifikan terhadap indeks kebahagiaan. Oleh karena itu, variabel-variabel ini dianggap sebagai penentu indeks kebahagiaan, sehingga untuk meningkatkan kebahagiaan masyarakat di daerah tersebut, pemerintah dapat memperhatikan faktor-faktor yang meningkatkan atau menurunkan nilai masing-masing variabel yang digunakan dalam penelitian