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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Teliska Bulletin of Electrical Engineering and Informatics Sinergi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Rekayasa Elektro Sriwijaya International Journal of Research in Vocational Studies (IJRVOCAS) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Sriwijaya Electrical and Computer Engineering (Selco) Journal
Yurni Oktarina
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Neural Network-Based Image Processing for Tomato Harvesting Robot Oktarina, Yurni; Sukwadi, Ronald; Wahju, Marsellinus Bachtiar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 3, August 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i3.1723

Abstract

Agriculture is one of the areas that can benefit from robotics technology, as it faces issues such as a shortage of human labor and access to less arid terrain. Harvesting is an important step in agriculture since workers are required to work around the clock. The red ripe tomatoes should go to the nearest market, while the greenest should go to the farthest market. Harvesting robots can benefit from Neural Network-based image processing to ensure robust detection. The vision system should assist the mobility system in moving precisely and at the appropriate speed. The design and implementation of a harvesting robot are described in this study. The efficiency of the proposed strategy is tested by picking red-ripened tomatoes while leaving the yellowish ones out of the experimental test bed. The experiment results demonstrate that the effectiveness of the proposed method in harvesting the right tomatoes is 80%.
Neural Network Controller Application on a Visual based Object Tracking and Following Robot Risma, Pola; Dewi, Tresna; Oktarina, Yurni; Wijanarko, Yudi
Computer Engineering and Applications Journal Vol 8 No 1 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.088 KB) | DOI: 10.18495/comengapp.v8i1.280

Abstract

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.
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.
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

Deteksi Ikan Menggunakan MEtode Faster R-CNN Oktarina, Yurni; Yolanda Eka Pratiwi; Dewi, Tresna Dewi
Journal of Applied Smart Electrical Network and Systems Vol 5 No 2 (2024): Vol 5 No 2 (2024): Vol 5 No 2
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jasens.v5i2.1132

Abstract

Automatic fish detection in video is a challenging task in the field of computer vision, which can be addressed using deep learning methods. This study proposes the use of the Faster Region-based Convolutional Neural Network (Faster R-CNN) to detect two types of fish, namely Manfish and Lemonfish, in video data. The dataset was constructed by extracting frames from video and processing them using the Roboflow platform. The model was trained and tested using pre-split training and testing sets. The training process was conducted over 40 epochs using the Adam optimization algorithm to improve detection accuracy. Model evaluation was carried out using several metrics, including Precision, Recall, mean Average Precision (mAP), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). The results show that the model achieved a precision of 94% and an accuracy of 87% for the Lemonfish class, and a precision of 95% and an accuracy of 89% for the Manfish class. These findings indicate that the model is capable of accurately detecting fish, delivering high detection performance, and effectively recognizing objects in video frames.
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.
Co-Authors Ahmad Fudholi Alkausar, Muhammad Fajri Amperawan Amperawan Anggraini, Citra Auliya, Annisa Azhar, M. Sayid Bambang, Muhammad Refo Bhakti Yudho Suprapto Citra Anggraini Dadi Setiadi Daniesar, Muhammad Nouval Destri Zumar Sastiani Dewi, Tresna Dewi, Tresna Dewi Dicky Astra Yudha Edo Triyandi Evelina Ginting Hendra Marta Yudha Husni, Nyayu Latifah Juwita, Aulia Ratna Lukman Nul Hakim M. Nawawi Mardiyati, Elsa Nurul Mayastri Devana 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 Rinaldi Rinaldi Riyo Irawan Ronald Sukwadi Rusdianasari Rusdianasari Sakuraba, Takahiro Sastiani, Destri Zumar SELAMET MUSLIMIN Siproni Umar Siti Afiyah Qatrunnada Solly Aryza 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