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All Journal International Journal of Electrical and Computer Engineering ComEngApp : Computer Engineering and Applications Journal Teknika Jurnal Teliska Elektron Jurnal Ilmiah Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Sinergi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Techno Bahari Jurnal Rekayasa Elektro Sriwijaya International Journal of Research in Vocational Studies (IJRVOCAS) Sriwijaya Electrical and Computer Engineering (Selco) Journal
Pola Risma
Politeknik Negeri Sriwijaya
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Aplikasi CNN untuk Analisis Visual Pertumbuhan Tanaman Bitter Melon dalam Sistem Akuaponik Yurni Oktarina; Rapli Wijaya; Tresna Dewi; Pola Risma
Jurnal Rekayasa Elektro Sriwijaya Vol. 6 No. 2 (2025): 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/jres.v6i2.152

Abstract

Technological advances in modern agriculture face major challenges, such as limited land and climate change that affect crop productivity. One approach that is gaining popularity is the aquaponic system, which is a farming method that combines fish and plants in one controlled ecosystem. In this study, a Convolutional Neural Network (CNN) method with a transfer learning approach was used, using the ResNet50 model to classify the condition of bitter melon plants growing in an aquaponic system. The developed model aims to distinguish plants into two categories, namely Good Condition and Reject. Test results show that the model has a high level of accuracy in classifying plant conditions, with a precision of 92%, recall of 100%, and F1-score reaching 96% on training data. However, the model still faces challenges in generalizing to the test data, which indicates the possibility of overfitting. To improve the performance of the model, various optimization techniques such as data augmentation and model regulation were performed to enrich the dataset variation and improve the model's ability to recognize more diverse plant growth patterns. Although there are still obstacles in handling differences in lighting and image capture angles, this method makes a significant contribution to the development of a more efficient and accurate artificial intelligence-based monitoring system in aquaponics systems. This research can be further developed by creating a more lightweight and adaptive model, and testing its performance in various real conditions in the aquaponics environment. The implementation of this deep learning-based classification system is expected to support precision agriculture innovation and encourage the sustainability of technology-based food production.
Smart Aquaculture Vision: Deteksi dan Klasifikasi Ikan Otomatis Menggunakan YOLOv8 Riyo Irawan; Tresna Dewi; Pola Risma; Yurni Oktarina
Jurnal Rekayasa Elektro Sriwijaya Vol. 6 No. 2 (2025): 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/jres.v6i2.157

Abstract

Akuakultur modern menuntut sistem pemantauan ikan yang efisien dan akurat guna meningkatkan produktivitas dan keberlanjutan. Penelitian ini mengusulkan pemanfaatan algoritma deteksi objek berbasis deep learning, yaitu YOLOv8, untuk mendeteksi dan mengklasifikasikan tiga jenis ikan secara otomatis: Black Spotted Barb, Gourami, dan Mosquito Fish. Dataset yang digunakan berasal dari Kaggle dan terdiri atas 730 gambar yang telah dilabeli ulang menggunakan Roboflow. Proses pelatihan dilakukan di Google Colab dengan konfigurasi GPU, batch size 32, selama 100 epoch. Model dievaluasi menggunakan metrik presisi, recall, dan mAP. Hasil evaluasi menunjukkan performa yang sangat baik dengan nilai precision sebesar 0.978, recall sebesar 0.928, mAP50 sebesar 0.973, dan mAP50-90 sebesar 0.616. Temuan ini membuktikan bahwa YOLOv8 mampu memberikan deteksi objek yang akurat dan efisien, serta berpotensi untuk diterapkan dalam sistem pemantauan akuakultur berbasis visi komputer secara real-time.
Model Prediksi Deep Learning dengan Pendekatan Feedforward Neural Network Lukman Nul Hakim; Tresna Dewi; Pola Risma; Yurni Oktarina
Jurnal Rekayasa Elektro Sriwijaya Vol. 6 No. 2 (2025): 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/jres.v6i2.158

Abstract

Penelitian ini bertujuan untuk menerapkan model deep learning, khususnya Feedforward Neural Network (FNN), dalam meramalkan nilai irradiance berdasarkan data waktu. Solar irradiance sangat penting dalam pengembangan sistem energi terbarukan seperti panel surya untuk meningkatkan efisiensi sistem tenaga surya dan mengoptimalkan perencanaan sumber daya energi. Penggunaan model ini diharapkan dapat memberikan prediksi yang lebih akurat dan handal, sehingga mendukung pengambilan keputusan dalam pengelolaan energi terbarukan secara berkelanjutan. Untuk meningkatkan akurasi prediksi, penelitian ini menerapkan teknik preprocessing data yang mencakup penghapusan nilai hilang dan normalisasi menggunakan MinMaxScaler guna meningkatkan stabilitas pelatihan model. Model FNN yang diusulkan terdiri dari beberapa lapisan tersembunyi dengan aktivasi non-linear untuk menangkap pola kompleks dalam data, serta lapisan output untuk menghasilkan prediksi akhir. Pelatihan model dilakukan menggunakan algoritma optimasi seperti Adam, dengan fungsi aktivasi ReLU untuk meningkatkan konvergensi. Evaluasi model dilakukan menggunakan metrik RMSE, MSE, MAE, dan R-squared (R²) sebagai indikator utama keakuratan model dalam peramalan irradiance. Hasil evaluasi menunjukkan bahwa model ini mampu memberikan prediksi yang akurat terhadap pola irradiance, dengan nilai RMSE dan MAE yang rendah serta R² mendekati satu, menandakan kinerja yang sangat baik dalam menangkap dinamika data.
APLIKASI SENSOR PZEM UNTUK MENDETEKSI KEMAMPUAN KAPASITAS BATERAI PADA PANEL SURYA DAN SISTEM KENDALI MOTOR STEPPER SEBAGAI PENGGERAK OTOMATIS PEMBOLAK-BALIK PANGGANGAN PADA PERANCANGAN SISTEM PENGASAPAN IKAN SALAI OTOMATIS risma, pola; Dira, Arbi Pratam; Muslimin, Selamat
JURNAL TELISKA Vol 16 No I (2023): TELISKA Maret 2023
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.8134935

Abstract

Pengasapan ikan salai yang dilakukan manusia masih dalam bentuk tradisional menggunakan asap dari arang dan membolak-balik ikan secara manual sehingga kematangan ikan tidak merata dan juga tidak higienis. Untuk mengatasi masalah tersebut diperlukan perancangan pengasapan ikan Salai otomatis. Pada perancangan ini menggunakan motor stepper sebagai pembolak-balik ikan agar kematangan ikan merata dan juga sensor pzem untuk mendeteksi kemampuan baterai dan kecepatan motor stepper. Alat sistem pengasapan ikan salai otomatis terdiri dari panel surya, solar charge control, dan baterai sebagai sumber daya listrik untuk arduino mega yang berperan sebagai mikrokontroler, LM2596 sebagai converter derect current (DC) to derect current (DC), sensor DHT22 sebagai pendeteksi suhu dan kelembapan, lcd berguna untuk menampilkan sistem, fan exhaust dan motor stepper sebagai output dari sistem pengasapan ikan otomatis. Alat ini mampu mempercepat proses pengasapan ikan salai dan juga menjaga tingkat kematangan ikan, sehingga ikan yang dihasilkan higienis.
IMPLEMENTASI SISTEM KENDALI POMPA OTOMATIS MENGGUNAKAN FUZZY LOGIC SUGENO PADA PENYIRAMAN TANAMAN BAWANG MERAH DI KELOMPOK WANITA TANI KEMUNING Baiki, Amaldi; Lutfi, Iskandar; Risma, Pola
JURNAL TELISKA Vol 18 No I (2025): TELISKA Maret 2025
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15052688

Abstract

Onion plants require consistent watering to provide nutrients. Watering that is inconsistent and done manually is often inefficient, requires a lot of labor, and can inhibit growth and reduce crop yields. This automatic shallot plant watering system uses the sugeno fuzzy logic method because the sugeno fuzzy logic method is very accurate in making decisions. there are three main conditions that determine the watering of shallot plants based on measurements of soil moisture and temperature. In the first condition, soil moisture is recorded at 34% with a temperature of 29°C, so the water pump is activated for 30 seconds to maintain optimal soil moisture. In the second condition, soil moisture increased sharply to 341% with a temperature of 34°C, so the water pump was activated for 48 seconds to reduce excess moisture. In the third condition, the soil moisture reached 639% with a temperature of 33°C, and the water pump was activated for 20 seconds to overcome the excessively wet soil conditions. Through the use of Sugeno's Fuzzy Logic method, the system is able to respond to changing environmental conditions quickly and efficiently, ensuring that shallot plants receive the appropriate watering for optimal growth.
Piezoelectric Output Analysis Oktarina, Yurni; Nur Aina Okta Ferrisa; Pola Risma
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 2 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i2.10

Abstract

This study explores the utilization of mechanical energy generated from human footsteps as an alternative energy source through energy harvesting technology using piezoelectric materials. The designed system takes the form of a ceramic tile floor composed of four tiles arranged longitudinally, with each tile containing 30 piezoelectric elements, each 35 mm in diameter. The configuration consists of six piezoelectric units connected in series and five rows arranged in parallel, resulting in a total of 120 piezoelectric units in the entire system. The voltage, current, and power output depend on variations in body weight (60–94 kg), foot size, and the anatomical shape of the user's foot, which affect how many piezoelectric elements receive sufficient pressure during each step. The generated electrical energy is stored in a 12 Volt, 12 Ah battery for subsequent power use. Experimental results show that the system can produce varying amounts of energy depending on user physical parameters, indicating its potential for small-scale implementation in renewable energy applications within urban environments.
YOLOv9-Assisted Vision System for Health Assessment in Poultry Using Deep Neural Networks Risma, Pola; Prasetyo, Tegar; Muhammad Amri , Yahya
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2414

Abstract

Poultry farming represents one of the fastest growing sectors in global food production, yet disease outbreaks, high mortality, and labor shortages continue to threaten its sustainability. Conventional health monitoring methods based on visual inspection are time-consuming, subjective, and inadequate for early anomaly detection. In response, computer vision and deep learning have emerged as transformative tools for livestock management. While prior implementations of the YOLO object detection family, such as YOLOv5 and YOLOv8, have achieved notable success, their performance often deteriorates in dense flocks, low-light conditions, and occlusion-prone environments. This study introduces a YOLOv9-assisted vision framework tailored for poultry health assessment in commercial farm settings. The system integrates smart cameras with edge computing to enable real-time detection of behavioral and physiological anomalies without dependence on high-bandwidth or cloud-based resources. A dataset of 903 annotated poultry images, categorized into healthy and sick classes, was employed for model development. The trained model achieved 88.7% precision, 97% recall, an F1-score of 0.82, and a mAP@0.5 of 0.88, demonstrating robustness under variable illumination, bird occlusion, and high-density environments. Comparative evaluation confirmed that YOLOv9 provides a superior balance of accuracy, generalization, and computational efficiency relative to YOLOv8–YOLOv11, supporting practical deployment on edge devices. Limitations include the binary scope of health classification and reliance on a single dataset. Future directions involve extending the framework to multi-class disease recognition, cross-dataset validation, behavior-based temporal modeling, and multimodal fusion, advancing predictive analytics and welfare-oriented poultry farming.
Implementasi Deep Learning Dalam Prediksi Real-Time Iradian Surya Liwijaya, Angga; Risma, Pola; Oktarina, Yurni; Dewi, Tresna
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.
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.
Co-Authors Adella Rialita Ahmad Fudholi Amperawan Amperawan Andika Wijaya, Andika Anggraini, Citra Arbi Nugraha Ariski, Ariski Sapni Putra Assyifa Mourlina Faraquinsha Baiki, Amaldi Bambang, Muhammad Refo Bimo, Muhammad Citra Anggraini Citra Anggraini Dadi Setiadi Dewi Permata Sari Dicky Astra Yudha Dira, Arbi Pratam Edo Triyandi Evelina Ginting Fairuz Attalah Hendra Marta Yudha Hibrizi, Dzaky Rafif Husni, Nyayu Latifah Islami, Miftahu Khoiri Liwijaya, Angga Lukman Nul Hakim Lutfi, Iskandar M. Taufik Roseno Mardianto, Yudhi Masayu Anisah Mayastri Devana Muhammad Amri Yahya Muhammad Bagaskara Muhammad Dede Yusuf Muhammad Nawawi Muhammad Ridho Kenawas Muhammad Roriz Muhammad Taufik Roseno Mulya, Zarqa Muslikhin Muslimin, Selamat Nur Aina Okta Ferrisa Nur Mutiara Syahrian Oktarina, Yurni Oktarina, Yurni Putri Repina Kesuma R. D. Kusumanto Rahman, Raihan Aldiaz Rapli Wijaya Ray, Ammar Dion RD Kusumanto RD Kusumanto RD Kusumanto Renny Maulidda, Renny Riyo Irawan Roseno, M. Taufik Rusdianasari Rusdianasari Sakuraba, Takahiro Sari Kartini SELAMET MUSLIMIN Septriani, Dela Siti Afiyah Qatrunnada Siti Nurmaini Solly Aryza Sonny Syahputra Syahrian, Nur Mutiara Tampubolon, Debora Tegar Arifin Prasetyo Tresna Dewi Tresna Dewi Tresna Dewi Tresna Dewi Tresna Dewi Try Rahmany Fajriah Wijanarko, Yudi Yudi Wijanarko Yudi Wijanarko, Yudi Yurni Oktarina Yurni Oktarina Yurni Oktarina Yurni Oktarina Yurni Oktarina Yurni Oktarina Zarqa Mulya