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PREDIKSI PEMANENAN ENERGI PADA ARRAY PIEZOELEKTRIK KONFIGURASI SERI-PARALEL BERBASIS RANDOM FOREST REGRESSION Fairuz Attalah; Yurni Oktarina; Pola Risma; Assyifa Mourlina Faraquinsha; Hendra Marta Yudha
JURNAL TELISKA Vol 19 No I (2026): TELISKA Maret 2026
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/teliska.v19iI.11829

Abstract

This study analyses the electrical characteristics and predicts the power output of a piezoelectric smart carpet system through a comparative analysis of series and parallel circuit configurations using the Random Forest Regression (RFR) algorithm. The system was designed using 16 piezoelectric ceramic elements per block integrated into a 100 cm carpet structure. Experimental data were collected from five subjects with body masses ranging from 54–98 kg through walking and running activities, yielding 100 observational samples. Results show the series configuration produced an average power output of 1649.3 mW, outperforming the parallel configuration by 104.8%, which yielded only 805.1 mW, with peak voltage reaching 80 V during running. The RFR model optimized using GridSearchCV with 5-fold cross-validation achieved a coefficient of determination (R²) of 88.25%, a Mean Absolute Error (MAE) of 145.06 mW, and a Root Mean Squared Error (RMSE) of 179.15 mW. Feature importance analysis revealed that circuit configuration (47.59%) and step frequency (47.18%) are the dominant predictive factors, while body mass contributed only 5.22% due to mechanical saturation in the carpet structure. This study confirms that RFR is effective as a predictive model for optimizing biomechanical energy harvesting systems in public infrastructure.
Multistage Fertile Egg Prediction via Texture Using Convolutional Neural Network Bimo, Muhammad; Dewi, Tresna; Maulidda, Renny; Oktarina, Yurni; Risma, Pola; Yudha, Hendra Marta
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/q58ezz91

Abstract

Accurate early detection of egg fertilisation status is necessary for effective incubation management in chicken production in order to avoid energy waste and decreased hatchery productivity brought on by infertile or non-viable eggs. Due to their comparable perceptual traits, conventional candling inspection relied on manual observation, which introduced subjectivity and made it challenging to distinguish between fertilised and blighted eggs early on. This study suggested an automated multistage fertilisation prediction method based on candling image analysis, utilising a convolutional neural network framework to get around this restriction. Rather than using traditional binary classification, the suggested system allowed for progressive monitoring of embryonic growth. On incubation days 1, 7, 14, and 21, candling photos were taken from native chicken eggs and classified into three groups: fertilised, infertile, and blighted. To enhance feature extraction efficiency under constrained dataset conditions, a transfer learning technique utilising the MobileNetV2 architecture was implemented. To guarantee consistent learning performance, image preprocessing, augmentation, model training, and validation were carried out. Precision, recall, F1-score, and classification accuracy were used as assessment measures. According to experimental findings, the suggested model produced consistent classification results for both fertilised and infertile eggs, with validation accuracy ranging from 90 to 95% throughout the incubation period. The results of multistage prediction showed consistent decision-making throughout the observation of embryo development. However, during intermediate incubation stages, visual uncertainty with fertilised eggs led to decreased performance in recognising blighted eggs. All things considered, the suggested method showed great promise as a nondestructive intelligent system for early fertilisation prediction. To increase the accuracy of blighted egg classification, more dataset expansion and model improvement were still required.
Benchmarking YOLOv8 and vision transformers for intelligent fish monitoring in aquaponics and controlled aquarium environments Tresna Dewi; Yurni Oktarina; Sri Rezki Artini; Gita Ayu Julianka; Jhoni Satria
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.022

Abstract

Sustainable aquaculture requires reliable and accurate fish monitoring systems capable of operating across heterogeneous environmental conditions. Conventional monitoring approaches are labor-intensive and prone to human error, while recent advances in deep learning have enabled vision-based automation for aquatic environments. Convolutional object detectors such as YOLO and emerging Vision Transformer (ViT) models have demonstrated promising performance; however, most existing studies remain limited to single-environment evaluations and rarely address energy-constrained, real-world deployment. To bridge this gap, this study presents a systematic benchmark of YOLOv8 and ViT across two complementary settings: a controlled aquarium environment and a solar-powered, off-grid aquaponics system. The proposed framework integrates 1080p CCTV video acquisition, dataset annotation and augmentation, and standardized training and evaluation using COCO metrics. Experimental results show that ViT consistently outperforms YOLOv8 in detection accuracy and prediction stability across both environments. ViT achieves 99.73% accuracy in the controlled aquarium and ≥99.6% accuracy performance (99.68–99.73%) in aquaponics, while YOLOv8 records 87.90% accuracy in the aquarium and 93.92–97.92% across aquaponics fish classes, exhibiting higher sensitivity to background clutter. Statistical validation using McNemar’s test (p < 0.001) confirms that these differences are statistically significant. Beyond accuracy, the results reveal a trade-off between robustness and computational efficiency. ViT provides superior resilience under occlusion and glare, whereas YOLOv8 offers faster inference suitable for real-time operation on resource-limited edge devices. End-to-end deployment on a solar-powered NVIDIA Jetson Xavier NX demonstrates the feasibility of continuous, off-grid aquaculture monitoring and provides practical guidance for context-aware model selection in intelligent aquaculture systems.
ANALISIS DAN PREDIKSI TEGANGAN PIEZOELEKTRIK PADA SISTEM LANTAI PINTAR BERBASIS INTERNET OF THINGS MENGGUNAKAN MODEL ARIMAX M. Dwi Permana; A. Rahman; Yurni Oktarina
Technologic Vol 17 No 1 (2026): TECHNOLOGIC
Publisher : LPPM Politeknik Astra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52453/technologic.v17i1.518

Abstract

Penerapan piezoelektrik pada sistem lantai pintar memiliki potensi sebagai sumber energi sekaligus sebagai indikator aktivitas manusia berdasarkan karakteristik sinyal yang dihasilkan. Tegangan yang dihasilkan bersifat dinamis akibat variasi tekanan mekanik dari aktivitas manusia. Penelitian ini bertujuan mengembangkan sistem akuisisi data tegangan piezoelektrik berbasis Internet of Things (IoT) serta menganalisis dan memprediksi output tegangan menggunakan metode Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX). Sistem menggunakan mikrokontroler ESP32-S3 dengan catu daya eksternal untuk menjaga kestabilan pengukuran. Data diperoleh dari 10 responden dengan rentang berat badan 51 sampai 80 kg melalui aktivitas berjalan, berlari, dan lompat, masing-masing sebanyak 10 kali, sehingga diperoleh 300 data. Rata-rata tegangan berada pada rentang 1,24 sampai 2,75 Volt. Hasil menunjukkan adanya hubungan positif antara berat badan, jenis aktivitas, dan tegangan output. Model ARIMAX menunjukkan performa prediksi yang baik dengan RMSE sebesar 0,15 sampai 0,25 dan MAE sebesar 0,12 sampai 0,20, serta mampu melakukan estimasi pada kondisi hingga 90 kg.
Implementasi Deep Learning Dalam Prediksi Real-Time Iradian Surya Angga Liwijaya; Pola Risma; Yurni Oktarina; Tresna Dewi
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/pvdpsr36

Abstract

Accurate prediction of solar irradiance plays a critical role in the planning and operation of renewable energy systems, particularly for photovoltaic integration and energy management. This study investigates the use of a deep learning approach based solely on Convolutional Neural Networks (CNN) to forecast short-term solar irradiance values. The model is trained using normalized multivariate time series data, which include several meteorological parameters as input features. The CNN architecture is designed to extract temporal patterns from the input sequences and predict radiation intensity at the next time step. Experimental results show that the proposed model achieves strong predictive performance, with a Mean Squared Error (MSE) of 0.0006, Root Mean Squared Error (RMSE) of 0.0242, Mean Absolute Error (MAE) of 0.0184, and a coefficient of determination (R²) of 0.9607. These findings demonstrate that CNN, despite its simplicity, is capable of effectively learning complex temporal relationships in solar irradiance data. Furthermore, the loss curves for both training and validation sets indicate stable convergence without signs of overfitting. The results suggest that CNN-based forecasting models can offer a lightweight and accurate solution for real-time solar prediction applications, especially when computational resources are limited.
Model Deep Learning Hybrid CNN-AE untuk Klasifikasi Presisi Warna Buah Melon Yurni Oktarina; Tresna Dewi; Dini Septiyani AR
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/d3hydf28

Abstract

Melon fruit color classification is a critical step in assessing fruit ripeness and quality. This study proposes a hybrid deep learning model that integrates Convolutional Neural Network (CNN) and Attention Enhancement (AE) for accurate classification of melon fruit color. The model leverages CNN’s strength in visual feature extraction while enhancing focus on crucial image regions through the attention mechanism. A diverse image dataset of melon fruits was collected under various lighting conditions and angles. Pre-processing steps, including data augmentation, normalization, and image scaling, were applied to improve model generalization. The CNN-Attention hybrid architecture incorporates an attention module into the CNN layers to emphasize significant features. Comparative experiments between the standard CNN and the hybrid model demonstrate that the latter achieves superior classification accuracy, with an average improvement of 5%. Moreover, the hybrid model exhibits better robustness against image noise and lighting variations. These results indicate that incorporating Attention Enhancement can yield a more adaptive and reliable model for melon fruit color classification. The proposed approach is expected to support the development of automated systems for fruit sorting in agriculture and distribution, enhancing speed, accuracy, and efficiency for farmers, traders, and consumers.
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 Ahmad Fudholi Alkausar, Muhammad Fajri Amperawan Amperawan Angga Liwijaya Anggraini, Citra Assyifa Mourlina Faraquinnsha Assyifa Mourlina Faraquinsha Auliya, Annisa Azhar, M. Sayid Bambang, Muhammad Refo Bhakti Yudho Suprapto Bimo, Muhammad Citra Anggraini Dadi Setiadi Daniesar, Muhammad Nouval Denny Juraijin Destri Zumar Sastiani Dewi, Tresna Dewi, Tresna Dewi Dicky Astra Yudha Dini Septiyani AR Edo Triyandi Evelina Ginting Fairuz Attalah Gita Ayu Julianka Hendra Marta Yudha Husni, Nyayu Latifah Jhoni Satria Juwita, Aulia Ratna Lukman Nul Hakim M. Dwi Permana M. Nawawi Mardiyati, Elsa Nurul Mayastri Devana Muhammad Amri Yahya Muhammad Dede Yusuf Muhammad Nawawi Muhammad Ridho Kenawas Muhammad Taufik Roseno Mulya, Zarqa Muslikhin Nawawi, M. Neta Larasati Noer, Mohammad Nawawi Nur Aina Okta Ferrisa Pola Risma Pratama, Yudha Wira Putri Repina Kesuma Rahman, Raihan Aldiaz Rapli Wijaya RD Kusumanto RD Kusumanto Rendi Dwi Yanto Renny Maulidda, Renny Rinaldi Rinaldi Riyo Irawan Ronald Sukwadi Rusdianasari Rusdianasari Sakuraba, Takahiro Sastiani, Destri Zumar SELAMET MUSLIMIN Siproni Umar Siti Afiyah Qatrunnada Sri Rezki Artini Sri Rezki Artini Tresna Dewi Tresna Dewi Tresna Dewi Tulak, Windy Gafrila Wahju, Marsellinus Bachtiar Windy Gafrila Tulak Yolanda Eka Pratiwi Yudha Wira Pratama Yudi Wijanarko, Yudi Zainuddin Nawawi Zarqa Mulya