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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) eProceedings of Engineering
Fakhrezi, Alfian
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Enhancing Soybean Fertilization Optimization with Prioritized Experience Replay and Noisy Networks in Deep Q-Networks Fakhrezi, Alfian; Budiman, Gelar; Perdana, Doan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30690

Abstract

This study focuses on the optimization of reinforcement learning in the Deep Q Network algorithm. This is achieved using the prioritized experience replay algorithm and Noisy Network optimization. The main goal is to optimize fertilization so that it can adapt to its environment and avoid over-fertilization. This study uses the prioritized experience replay algorithm and Noisy Network optimization to create an agent in RL that is able to explore and exploit optimally so that it can improve the precision of fertilization in soybeans. This methodology includes several steps, including data preparation, creating an environment that matches real-world conditions, and validating changes in soil nutrient conditions.  The RL model was trained with PER and NN, with performance evaluated using cumulative reward, convergence speed, action distribution, and Mean Squared Error (MSE). The main results of the study show that DQN-PER NN achieves the highest cumulative reward, approaching 600,000 in 1000 episodes, outperforming standard DQN, A2C, and PPO. It also converges faster at episode 230, indicating superior adaptability. In addition, the results of this study indicate that the model that has been created is able to recommend a dose of SP36 fertilizer of 150 kg/ha, urea fertilizer of 100 kg/ha, and KCL fertilizer of 125 kg/ha. Compared with the A2C and PPO methods, the dose of urea fertilizer is reduced by 14%, KCL fertilizer is reduced by 33%, while for SP36 the difference is 23%. In Conclusion this model effectively distributes actions based on environmental conditions, which supports sustainable agriculture. In conclusion, the integration of PER and NN into DQN significantly improves exploration and decision making, and optimizes soybean fertilization. This model not only improves harvest efficiency but also encourages sustainable agricultural practices.
Rancang Bangun Sistem Monitoring Unsur Hara, Kelembaban, PH Tanah Dan Suhu Udara Berbasis Iot Menggunakanmikrokontroler ESP32 Fakhrezi, Alfian; Saputra, Randy Erfa; Hasibuan, Faisal Candrasyah
eProceedings of Engineering Vol. 10 No. 1 (2023): Februari 2023
Publisher : eProceedings of Engineering

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Abstrak—Tanaman Stroberi merupakan tanaman subtropis yang dijumpai pertama kali di chilli. Daerah Ciwidey, Kabupaten Bandung merupkan wilayah yang sangat cocok untuk pembudidayaan Stroberi. Setiap tanaman membutuhkan tanah sebagai media tanam. Didalam tanah terdapat unsur hara yang sangat penting untuk pertumbuhan suatu tanaman. Tanah yang kekurangan unsur hara dapat mengakibatkan tanaman Stroberi, umumnya semua tanaman menjadi tidak subur, daunnya kering, kualitasnya buahnya menurun dan bahkan bisa menyebabkan gagal panen. Meninjau hal-hal diatas, maka dibuat suatu perangkat untuk sistem monitoring yang dapat mengukur kadar unsur hara Nitrogen, Posfor, Kalium, suhu udara, kelembaban udara, pH tanah dan Kelembaban tanah. Perangkat yang di buat menggunakan mikrokontroler ESP32. Data yang telah diproses oleh ESP32 akan dikirimkan kedalam firebase realtime database menggunakan jaringan internet. Setelah data terkirim ke firebase data tersebut akan ditampilkan di LCD16x2 dan aplikasi mobile. Perangkat yang telah dibuat didapatkan bahwa mampu mendeteksi kandungan unsur hara NPK, pH tanah, Kelembaban tanah, suhu udara dan kelembaban udara yang berjalan dengan baik dengan . Akurasi untuk sensor NPK unsur N sebesar 98 %, unsur P sebesar 98% dan unsur K yaitu 93%, sedangkan untuk sensor pH mendapatkan akurasi pembacaan sebesar 99.06% terhadap kalibrator. Sensor soil moisture mendapatkan akurasi 97% dan sensor DHT11 untuk suhu mengahasilkan akurasi 98%. Pada aplikasi mobile juga telah dapat menampilkan data hasil pengukuran dari perangkat hardware. Kata kunci — ESP32, kelembaban tanah, NPK, pH, suhu udara, sistem monitoring
Co-Authors Doan Perdana Faisal Candrasyah Hasibuan Gelar Budiman Randy Erfa Saputra