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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.
Simulation Design of Artificial Intelligence Controlled Goods Transport Robot Oktarina, Yurni; Sastiani, Destri Zumar; Dewi, Tresna; Kusumanto, RD
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 2 (2022)
Publisher : Universitas Sriwijaya

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

Abstract

Technological advances enable scientists and researchers to develop more automated systems for life's convenience. Transportation is among those conveniences needed in daily activities, including warehouses. The easy-to-build and straightforward transport robot are desired to ease human workers' working conditions. The application of artificial intelligence (AI), Fuzzy Logic Controller, and Neural Network ensures the robot is able to finish assigned tasks better and faster. This paper shows the concept design of an AI-controlled good transport robot applied in the warehouse. The design is made as fast and straightforward forward possible, and the feasibility of the proposed method is proven by simulation in Scilab FLT and Neuroph.
Aplikasi Sensor Ultrasonik Dalam Pembacaan Level Air Pada Sistem Pertanian Aquaponic Daniesar, Muhammad Nouval; Dewi, Tresna; Oktarina, Yurni
JURNAL TELISKA - JURNAL TEKNIK ELEKTRO POLITEKNIK NEGERI SRIWIJAYA 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.8031245

Abstract

SYNERGY OF GREEN ENERGY AND SMART TECHNOLOGY: APPLICATION OF RECURRENT NEURAL NETWORKS IN SOLAR-POWERED AGRICULTURE Maulidina, Elfira; Dewi, Tresna; Kusumanto, Raden
International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) Vol. 3 No. 2 (2025): IJMEAS - May
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijmeas.v3i2.406

Abstract

In an effort to improve energy efficiency and sustainability in the agricultural sector, smart technology has been integrated into the greenhouse system. The research utilizes the Recurrent Neural Network (RNN) algorithm to forecast values of irradiance on a time principal. The RNN algorithm is chosen for its ability to handle time-series data and predict patterns based on historical data. By using the RNN algorithm, the system can predict real-time needs and then use this information to optimally distribute power from solar power plants. Additionally, this system is equipped with Internet of Things (IoT)-based monitoring capabilities, allowing remote monitoring and control of the research object. Connected IoT sensors collect real-time environmental data and send it to the data server for analysis. The data is also used to update the model of RNN, making supply prediction more accurate over time. The implementation results show increased energy efficiency and reduced operational costs in Green House management. By leveraging AI and IoT technology, model evaluation is conducted using RMSE, MSE, MAE, and R-squared (R²) metrics as important indicators of model accuracy. The evaluation results indicate that this model can provide accurate predictions of irradiance patterns, with low RMSE and MAE values and R² approaching one, signifying excellent implementation in capturing data dynamics.
Internet of Things Based Temperature and pH Stabilization Control System in The Pome Biodigester Fermentation Process at PLTBg Mustofa; Dewi, Tresna; Bow, Yohandri
International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) Vol. 3 No. 3 (2025): IJMEAS - September
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijmeas.v3i3.433

Abstract

The Government of Indonesia is striving to reduce dependence on fossil fuels by increasing the use of renewable energy (RE), with a target of achieving 23% by 2025. Biogas Power Plants (PLTBg) are one of the solutions that utilize palm oil mill effluent (POME) to produce biogas through anaerobic fermentation. The currently operating systems face problems and challenges in monitoring and operating mesophilic digesters, particularly covered lagoon types, which are managed conventionally, resulting in frequent drops in temperature and pH levels. To address these issues, automation was implemented in the temperature and pH control systems, with the expectation of increasing biogas production. This study adopted an approach that involved recording temperature and pH data, analyzing their upper and lower thresholds, and developing a laboratory-scale model that simulates industrial conditions. This model was equipped with a temperature and pH control system, along with monitoring and control coding. In the laboratory-scale POME digester prototype using actual POME liquid as the test medium, it was demonstrated that temperature and pH could be effectively monitored and controlled by automatically regulating the POME pump motors. The study results show that temperature control was maintained within the range of 38–41 °C, and pH control within the range of 6.5–8. The temperature drop from 40 °C to 35 °C occurred over 274 minutes. The temperature control response time was 5.6 seconds. The pH decrease rate due to the addition of fresh POME was 2.04, with a pH control response time of 8.5 seconds.
Co-Authors A Rahman Ahmad Fudholi Alkausar, Muhammad Fajri Amalia, Kania Yusriani Amperawan Amperawan Amperawan Amperawan, Amperawan Angga Prasetia Anggraini, Citra Arissetyadhi, Iwan Auliya, Annisa Azhar, M. Sayid Bambang Tutuko Bambang, Muhammad Refo Clinton, Billi Dadi Setiadi Daniesar, Muhammad Nouval Dicky Astra Yudha Dinata, Yogi Edo Triyandi Evelina Ginting Fatahul Arifin, Fatahul Fradina Septiarini Hendra Marta Yudha Hibrizi, Dzaky Rafif Husni, Nyayu Latifah INDRAYANI INDRAYANI Indriyani Indriyani Junaedi, Ketut Juwita, Aulia Ratna Kemala Dewi Kusumanto, Raden Lukman Nul Hakim M. Muhajir Mardianto, Yudhi Mardiyati, Elsa Nurul Maulidina, Elfira Mayastri Devana Muhammad Dede Yusuf Muhammad Insan Kamil, Muhammad Insan Muhammad Nawawi Muhammad Ridho Kenawas Muhammad Roriz Muhammad Taufik Roseno Mulya, Zarqa Muslikhin Mustofa Mustofa Neta Larasati Noer, Mohammad Nawawi Nur Mutiara Syahrian Oktarina, Yurni Oktarina, Yurni Pola Risma Putri Repina Kesuma Rapli Wijaya RD Kusumanto RD Kusumanto Rinaldi Rinaldi Riyo Irawan Robiansyah Ronald Sukwadi Roseno, M. Taufik Rusdianasari Rusdianasari Rusdianasari Rusdianasari Rusdianasari Sakuraba, Takahiro Sastiani, Destri Zumar SELAMET MUSLIMIN Siproni Siproni Siproni Umar Siti Afiyah Qatrunnada Siti Nurmaini Solly Aryza Sri Rezki Artini Syahrian, Nur Mutiara Tampubolon, Debora Utami, Retyo Wizi Nafa Velia Yuliza Wahju, Marsellinus Bachtiar Wijanarko, Yudi Wijaya Pratama, Agung Yohandri Bow Yudha Wira Pratama Yudi Wijanarko Yudi Wijanarko, Yudi Yurika Islamiati Yurni Oktarina Yurni Oktarina Yurni Oktarina Yusi, Muhammad Syahirman Zarqa Mulya