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All Journal Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Journal of Computation Physics and Earth Science
Irviandi, Risnu
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Physics

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Akselerometer dan Giroskop MEMs: Aplikasi dalam Sensor Seismik Elektrokimia Situngkir, Yusuf Hotdes Triwan; Irviandi, Risnu
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 Ndaumanu, Daniela Adolfina; Irviandi, Risnu
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.
Cellulose Hydrolysis of Mask Waste Using Aspergillus niger and Eco-Friendly Microwave Pretreatment Gilbran, Adam; Nafilah, Syahraini; Layalia, Afina Rista; Arsyad, Wifqul Muna; Darmawan, Andi; Setiawan, Risqi Prastianto; Irviandi, Risnu; Kusdiyantini, Endang; Nurauliyaa, Aida Habibah; Anda, Martin; Sasongko, Nugroho Adi; Wahyono, Yoyon
Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Vol 22, No 3 (2025): November 2025
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/presipitasi.v22i3.993-1001

Abstract

The management of used medical mask waste has become a significant issue due to the increased volume of waste during and after the pandemic. Medical mask waste contains cellulose compounds that can be converted into derivatives such as glucose, which are then processed into bioethanol as an alternative energy source. This study aims to hydrolyse medical mask waste using cellulase enzymes from Aspergillus Niger to produce glucose. The cellulase enzyme composition was varied (5 ml, 15 ml, and 25 ml) to determine the optimal hydrolysis conditions. The glucose produced was measured using DNS reagent assay with spectrophotometry at a wavelength of 540 nm. The highest amount of glucose was obtained under optimal conditions with 25 ml of cellulase enzyme after 48 hours of hydrolysis, amounting to 88.16 ppm. Subsequently, the glucose from hydrolysis was fermented using Saccharomyces cerevisiae, and the fermentation product was analysed for ethanol concentration using GC-FID. The products of fermentation ware 0.017% ethanol concentration from mask waste fermentation. Hydrolysis is an environmentally friendly alternative solution for handling mask waste.
Co-Authors Anda, Martin Andi Darmawan Arsyad, Wifqul Muna Endang Kusdiyantini Gilbran, Adam Layalia, Afina Rista Nafilah, Syahraini Ndaumanu, Daniela Adolfina Nugroho Sasongko Jati, Nugroho Sasongko Nurauliyaa, Aida Habibah Setiawan, Risqi Prastianto Situngkir, Yusuf Hotdes Triwan Wahyono, Yoyon