Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Computation Physics and Earth Science
Risnu Irviandi
Diponegoro University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Akselerometer dan Giroskop MEMs: Aplikasi dalam Sensor Seismik Elektrokimia Yusuf Hotdes Triwan Situngkir; Risnu Irviandi
Journal of Computation Physics and Earth Science (JoCPES) Vol 2 No 1 (2022): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/zhrzk442

Abstract

This journal is intended to provide an initial overview or introduction to an electrochemical seismic sensor device assisted with vibration detection features using a liquid resistance mass. This research introduces the first electrochemical seismic sensor that uses a liquid resistance mass (electrolyte solution) as a detecting element to convert environmental vibrations into active ion imbalances between electrodes, resulting in an electric current output. This paper will describe the use of MEMs in motion or vibration (seismic) analysis, validating the validity of concepts that have been widely fabricated, ranging from the use of conventional electrodes to earthquake detection and recording. In addition, this study discusses the operating principle, sensing mechanism, and applications of MEMS- based accelerometer and gyroscope sensors, where accelerometers measure linear acceleration and gyroscopes detect angular motion due to Coriolis acceleration. The comparative analysis shows the important role of MEMS sensors in various fields, such as shipping, aerospace, robotics and smart devices, and reveals the efficiency of MEMS-based electrochemical seismic sensors in earthquake monitoring with lower power and fabrication costs. This research opens up opportunities for the development of MEMS-based seismometers for environmental and geological monitoring applications, with recommendations for continued research for optimization of electrochemical materials and system integration to improve overall seismic response.
Memanfaatkan Teknik Machine Learning dan Deep Learning untuk Meramalkan Curah Hujan dan Cuaca: Sebuah Tinjauan Daniela Adolfina Ndaumanu; Risnu Irviandi
Journal of Computation Physics and Earth Science (JoCPES) Vol 2 No 2 (2022): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/kzexvv23

Abstract

Machine learning and deep learning are vital for achieving precise rainfall and weather forecasting, which is crucial for agricultural planning, managing water resources, and reducing disaster risks. This study reviews a range of literature on weather and rainfall forecasting, emphasizing deep learning techniques. Additionally, it examines the performance of various machine learning models, including Long Short-Term Memory (LSTM) networks and Support Vector Regression (SVR), in improving forecast accuracy. These methods show notable improvements in accuracy over traditional models. The study’s findings suggest that enhanced machine learning and deep learning models can significantly benefit weather forecasting, aiding in climate change adaptation efforts.
Co-Authors Daniela Adolfina Ndaumanu Yusuf Hotdes Triwan Situngkir