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Muhammad Amri Yahya
Politeknik Negeri Sriwijaya
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SISTEM KEAMANAN KEBOCORAN GAS BERBASIS INTERNET OF THINGS DENGAN FITUR PEMADAMAN KOMPOR OTOMATIS Anggun Imelda; A. Rahman; Muhammad Amri Yahya
JURNAL TELISKA Vol 18 No III (2025): TELISKA November 2025
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17767901

Abstract

LPG (Liquefied Petroleum Gas) leakage is a major problem that has the potential to trigger fires and explosions. An Internet of Things (IoT)-based security system offers an innovative solution to detect and prevent gas leaks by sending automatic notifications and providing responses. In this study, MQ-6 gas sensors along with fan actuators and circuit breaker relays are used in the gas leak detection system. The level of leakage and ambient temperature are used in the fuzzy logic decision-making process. Fan activation, and WhatsApp notifications are the results. The system was tested to ensure that the leak detection and automatic response were successful. The test results showed that the system has the ability to handle leaks quickly and accurately.
Exploring YOLO-Based Deep Learning Approaches for Fish Detection in Intelligent Aquatic Monitoring Systems Tresna Dewi; Riyo Irawan; Agum Try Wardhana; Muhammad Amri Yahya; Lukman Nul Hakim; Dini Septiyani AR
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1007

Abstract

The Advancements in precision aquaculture demand robust visual monitoring systems capable of accurate, real-time fish detection in complex underwater environments characterized by turbidity, occlusion, and dynamic illumination. While YOLO (You Only Look Once) architectures have demonstrated high efficiency in object detection tasks, their comparative performance for underwater fish detection remains underexplored, particularly across recent variants such as YOLOv5, YOLOv8, and YOLOv11. This study presents a systematic evaluation of three state-of-the-art YOLO models using a curated GlowFish dataset consisting of 533 annotated images across three fluorescent species. Data were acquired under controlled but visually diverse conditions using multi-angle imaging and standardized illumination. A uniform training pipeline, consistent annotation using the COCO format, and identical hyperparameters were applied across models to ensure fair benchmarking. Key evaluation metrics include precision, recall, mAP@0.5, and mAP@0.5:0.95. Experimental results reveal that YOLOv5 achieved the highest precision (0.963) and mAP@0.5 (0.967), while YOLOv8 delivered superior recall (0.930) and more balanced detection across species classes. YOLOv11 demonstrated architectural potential but showed greater sensitivity to class imbalance and reduced confidence stability. Visual analysis and confusion matrices further confirmed model-specific trade-offs in classification reliability and localization precision. This work contributes critical empirical insights into the selection of YOLO architectures for intelligent aquaculture systems, offering practical guidance for real-time aquatic monitoring deployments. Future research will extend this framework to multi-species, multi-environment datasets, integrate spatiotemporal behavioral tracking, and investigate deployment on resource-constrained edge-AI platforms, advancing the field toward interpretable and autonomous aquatic monitoring solutions.
Modeling and Forecasting Piezoelectric Energy Harvesting Using Deep LSTM–ANN Architectures Yurni Oktarina; Tresna Dewi; Muhammad Amri Yahya; Assyifa Mourlina Faraquinnsha; Denny Juraijin
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1002

Abstract

Piezoelectric energy harvesting (PEH) enables maintenance-free micro-power generation for autonomous sensing and ultra-low-power electronics by converting ambient mechanical excitation into electrical energy. Despite substantial progress in piezoelectric materials and device structures, forecasting PEH electrical outputs remains difficult because the response is nonlinear, excitation is stochastic, and performance can drift under repeated loading. This paper proposes a hybrid deep learning architecture that integrates Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) components to forecast voltage, current, and power from footstep-driven PEH time-series data. The dataset is constructed by sampling the harvester voltage under controlled walking-induced excitation and organizing the continuous signal into supervised samples using a sliding-window scheme; features are normalized and paired with future targets for multi-output regression. The model is trained and evaluated against standalone LSTM, standalone ANN, and classical forecasting baselines using RMSE, MAE, MSE, and R2. Experimental results show high voltage prediction accuracy (R2=0.9896, RMSE = 0.0035, MAE = 0.0022), while current and power are predicted with acceptable performance consistent with their higher noise sensitivity and nonlinear coupling. These findings indicate that combining temporal memory with nonlinear regression improves forecasting stability for PEH outputs within the defined experimental setting and provides a practical basis for energy-aware scheduling and monitoring in self-powered sensing applications. Future work will extend the dataset to broader excitation conditions and incorporate uncertainty-aware modeling for robust edge deployment.
Co-Authors A. Rahman Abda Abda Abi Burhan Adha, Ufairi Africano, Fernando Agum Try Wardhana Agum Try Wardhana Aldila Sari Utami Ali Muhtar Anggun Imelda Assyifa Mourlina Faraquinnsha BillY Dewantara Denny Juraijin Desiana, Lidia Dini Septiyani AR Dyah Utari Yusa Wardhani Ebtaria Nadeak, Ebtaria Eka Putri Manurung, Nancy Evelina Evelina Fredi Prima Sakti Gemala Cahya Hermialingga, Septi Lukman Nul Hakim Manurung, Nancy Eka Putri Muhammad Fikri Nancy Eka Putri Manurung Nugraha, Yoga Aji Nyayu Latifah Husni, Nyayu Latifah Putri Manurung, Nancy Eka Ramadhoni, M. Renny Maulidda Riyo Irawan Sari Utami, Aldila Septi Hermialingga Tegar Arifin Prasetyo Tegar Prasetyo Tresna Dewi Yurni Oktarina