Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Multidisiplin Sahombu Journal of Innovation in Applied Natural Science
Fitryan, Anggyta
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Energy Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Characterization of Corona Plasma Ozone Generators and Analysis of Microbubble Ozone Size for Efficient Mass Transfer Fitryan, Anggyta; Junaidi, Junaidi; Arif Surtono; Eko Yulianto; Eva Sasmita; Muhammad Nur; Ayu Aprilia; Nida Lidya Susanti; Abdurrahman, Ahmad Faruq
Jurnal Multidisiplin Sahombu Vol. 5 No. 06 (2025): Jurnal Multidisiplin Sahombu, September - October (2025)
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to characterize the performance of a corona plasma ozone generator, measure the concentration of ozone produced, and analyze and compare the size of ozone microbubbles from two diffusers (C50 and C80) to evaluate the efficiency of ozone mass transfer. The method used was experimental with equipment including a VOSOCO ozone generator, Ozone Monitor, dimmer, high voltage probe, Kyoritsu ampere clamp, SANWA multimeter, flowmeter, aquarium, Apexel 12.5x macro lens, C50 and C80 diffusers, and an iPhone 11, with Python-based microbubble size analysis software for digital image processing. The results showed that the ozone generator operated at 1200 V with a linear relationship between voltage, current, and power, where the highest ozone concentration was achieved at a flow rate of 0.8 l/min. The C80 diffuser produced smaller and more homogeneous bubbles (average 3.37 µm) compared to the C50 (4.09 µm), increasing the specific surface area by 21.4% for better mass transfer efficiency. The innovation of this research lies in the development of a Python-based analysis system with an effective and affordable digital image processing approach to characterize ozone microbubble size, providing an accurate alternative for analyzing critical parameters for optimizing microbubble-based ozonation systems.
Beyond the Canopy: Resolving Topographic and Acoustic Complexities with Machine Learning for Karst Avifauna Monitoring Fitryan, Anggyta; Abdurrahman, Ahmad Faruq; Nuryani; Prihanto, Surya; Al Fath, Yusril; Aprilia, Ayu; Junaidi; Surtono, Arif
Journal of Innovation in Applied Natural Science Vol. 1 No. 1 (2025): Journal of Innovation in Applied Natural Science
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jinas.v1i1.52

Abstract

Background of study: Tropical karst landscapes harbor exceptional avian biodiversity but pose unique monitoring challenges due to complex topography, cave reverberation, and humidity-driven sound distortion. Conventional ecoacoustic methods fail in these environments, with indices showing weak correlations (r=0.20-0.43) for avian diversity due to insect masking and abiotic interference. Over 83% of karst-endemic birds lack standardized monitoring protocols despite escalating extinction risks.Aims and scope of paper: This review aims to: (1) quantify limitations of current ecoacoustic methods in karst ecosystems, (2) develop a machine learning-enhanced framework addressing topographic and reverberation effects, and (3) establish conservation-ready protocols for endangered karst avifauna. The study synthesizes evidence from 29 studies across hardware innovation, signal processing, and policy applications.Methods: We systematically analyzed 29 studies on acoustic monitoring in karst ecosystems, focusing on machine learning innovations, topographic adaptations, and conservation applications.Result: Topography drives 47% of soundscape variation, surpassing vegetation effects. Machine learning (CNNs/MFCCs) boosts detection accuracy by 22-80% in reverberant caves. Hybrid protocols enable 25-m resolution habitat mapping and precise disturbance monitoring, overcoming tropical "latitude paradox" limitations.Conclusion: This review establishes the first karst-adapted ecoacoustic framework, integrating machine learning with topographic variables to transform monitoring from biodiversity proxy to precision tool. Critical next steps include developing species-specific call libraries, wind-reverberation filters, and policy integration of acoustic baselines for IUCN assessments. The proposed protocols address urgent conservation needs for Earth's most threatened avian sanctuaries.
A Tetrahedral Sensor Array Prototype for Avian Sound Source Localization in Bioacoustics Conservation Fitryan, Anggyta; Wulandari, Mei; Nuryani; Faruq, Ahmad Abdurrahman; Junaidi; Aprilia, Ayu; Prihanto, Surya; Al Fath, Yusril
Journal of Innovation in Applied Natural Science Vol. 2 No. 1 (2026): Journal of Innovation in Applied Natural Science
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jinas.v2i1.157

Abstract

Background: Effective wildlife monitoring is crucial for conservation, but traditional methods are often invasive or lack spatial precision. Passive acoustic monitoring offers a non-invasive alternative, yet deriving meaningful spatial data from sound recordings remains a technical challenge, limiting its utility for detailed ecological analysis. Aims: This study aims to design and simulate a proof-of-concept, low-cost acoustic localization system. The goal is to translate Time Difference of Arrival (TDOA) data from a simple tetrahedral microphone array into two-dimensional spatial heatmaps, providing a visual and quantitative tool to map animal vocal activity for enhanced biodiversity assessment. Methods: A cross-shaped, four-sensor array was modeled. A custom MATLAB GUI was developed to simulate TDOA data from multiple sound sources at varied positions. The system processed this data to generate and compare four distinct types of spatial heatmaps: Gaussian Smoothing, Kernel Density Estimation, Grid Counting, and Inverse Distance Weighting Result: The simulation successfully generated all four heatmap types, validating the core data processing pipeline. The system provided estimated source coordinates with a root mean square error (RMSE) of 0.15-0.25 meters in a controlled 6x6m area and output key statistical metrics like cluster density and distribution. Conclusion: The prototype establishes a feasible framework for transforming raw acoustic signals into actionable spatial intelligence. This work provides a foundational step towards developing affordable, automated systems for long-term ecological monitoring, with future integration of machine learning promising direct species identification and behavioral insight.
Co-Authors Abdurrahman, Ahmad Faruq Al Fath, Yusril Aprilia, Ayu Ayu Aprilia EKO YULIANTO Eva Sasmita Faruq, Ahmad Abdurrahman Junaidi Junaidi Muhammad Nur Nida Lidya Susanti Nuryani Prihanto, Surya Surtono, Arif Wulandari, Mei