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Journal : Jurnal Rekayasa Elektro Sriwijaya

 1 
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.
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