Judul: Deteksi Empat Organisme Pengganggu Utama Tanaman  Padi Menggunakan Artificial Intelligence of Things (AIoT) Berbasis Citra Kamera  Digital
Abstrak: Infestasi hama dan infeksi patogen tanaman padi selalu menjadi masalah yang dihadapi oleh petani dari tahun ke tahun. Untuk mengatasi masalah tersebut, adanya suatu metode untuk memantau serangan hama dan penyakit tersebut yang lebih efektif dan efisien sangat diperlukan. Metode pemantauan yang selama ini dilaksanakan oleh petugas pengamat OPT masih belum memberikan hasil yang optimal. Perkembangan teknologi informasi dan digital dapat memberikan potensi alternatif dalam pemantauan hama dan penyakit yang lebih baik, salah satunya dengan artificial intelligence of things. Penelitian ini bertujuan mendapatkan metode identifikasi empat OPT utama tanaman padi yang lebih akurat melalui penggunaan machine learning. Citra serangga wereng batang cokelat dan gejala serangan penggerek batang padi, penyakit blas dan hawar daun diambil secara langsung dari lahan pertanaman padi di Kecamatan Compreng, Kabupaten Subang dengan menggunakan kamera DSLR dan handphone. Data yang digunakan sebanyak 3200 citra untuk membangun model dan 800 citra untuk pengujian model. Data citra ini diproses pengklasifikasiannya dengan model convolutional neural network (CNN) menggunakan bahasa pemrograman Python versi 3.10. Hasil penelitian menunjukkan bahwa penggunaan arsitektur CNN dapat mengklasifikasikan dengan benar keempat OPT utama dengan tingkat presisi yang sama besar dengan recall yaitu sebesar 81,88%. Hasil evaluasi model memberikan accuracy sebesar 90,94%., Pest infestation and pathogen infection in rice have always been problems faced by farmers have been worried about from year to year. To overcome these problems, there is a need for methods to monitor infestations to be more effective and efficient. The pests monitoring methods that has been implementing by pest observer officers has not yet given optimal results. The development of information and digital technology can provide an alternative for better pest and disease monitoring, one of which is the artificial intelligence of things. This study aims to obtain a more accurate identification method for the four main pests of rice plants through the use of machine learning. The image of the brown planthopper and the symptoms of the rice stem borer, blast disease, and leaf blight were taken directly from rice fields in Compreng District, Subang Regency using a DSLR camera and mobile phone. The data used are 3200 images to build the model and 800 images to test the model. This image data is classified using a convolutional neural network (CNN) model using the Python programming language version 3.10. The results of this study indicate that the use of CNN architecture can correctly classify the four major pests with the same level of precision as recall, which is 81,88%. The results of the model evaluation give an accuracy of 90,94%.
Keyword: artificial intelligence, CNN model, digital image, pests detection

Judul: Deep Learning Model YOLO for Detecting the Success of Rice (Oryza sativa) Planting Based on Unmanned Aerial Vehicle Image
Abstrak: Deteksi keberhasilan tanam padi (Oryza sativa) pada fase awal budidaya perlu dilakukan secara ketat agar dapat dimanfaatkan untuk kegiatan penyulaman, pengendalian hama, pengendalian penyakit, prediksi kebutuhan pupuk, dan perlakuan pemeliharaan tanaman yang tepat. Citra tangkapan dari Unmanned Aerial Vehicle (UAV) sangat potensial digunakan untuk mengamati lahan padi yang luas dengan bantuan algoritma kecerdasan buatan. Penelitian ini bertujuan mengembangkan model deep learning untuk mendeteksi tegakan padi di sawah pada fase awal budidaya menggunakan citra RGB beresolusi tinggi hasil tangkapan UAV. Penelitian ini menggunakan deep learning dengan algoritma YOLOv5 yang terdiri dari tahapan: (1) pengumpulan dataset, (2) pelabelan dataset, (3) training dataset, (4) uji model hasil training, dan (5) implementasi model. Training deep learning terhadap 9.677 dataset dilakukan dengan menggunakan algoritma YOLOv5s, YOLOv5m, dan YOLOv5l. Akurasi dari model deep learning menggunakan algoritma YOLOv5s, YOLOv5m, dan YOLOv5l masing-masing sebesar 89,35%; 91,29%; dan 79,43%. Nilai presisi yang didapatkan sebesar 99,90%; 99,80%; dan 99,89%. Sedangkan nilai recall yang didapatkan sebesar 89,43%; 91,46%; dan 79,50%. Hasil tersebut menunjukkan model deep learning yang telah dikembangkan mampu mendeteksi tegakan padi dengan akurasi tinggi dan presisi. Model dapat digunakan untuk mendeteksi padi pada fase awal budidaya dengan user interface berbasis web yang diimplementasikan dengan bahasa pemrograman Pyhton pada komputer lokal., Detection the success of rice (Oryza sativa) planting in the early stages of cultivation are important for embroidery activities, pest control, disease control, prediction of fertilizer needs, and proper plant maintenance treatment. The captured image from the Unmanned Aerial Vehicle (UAV) coupled with artificial intelligence are potential to observe large scale of rice fields. The objective of this study is to develop a deep learning model to detect rice stands using high resolution RGB imagery captured by UAV in the early stages of paddy. Deep learning with the YOLOv5 algorithm was used to develop the model that consists of the following stages: (1) dataset collection, (2) dataset labeling, (3) dataset training, (4) model testing, and (5) model implementation. Deep learning was employed to train 9,677 datasets using YOLOv5s, YOLOv5m, and YOLOv5l algorithms. The accuracy of the deep learning model using the YOLOv5s, YOLOv5m, and YOLOv5l algorithms was 89.35%; 91.29%; and 79.43% respectively. The precision value obtained is 99.90%; 99.80%; and 99.89% respectively. While the recall value obtained was 89.43%; 91.46%; and 79.50% respectively. Based on these results indicate that the deep learning model was capable to detect rice stands with high accuracy and precision. The model can be used to detect rice in the early stages of cultivation with a web-based user interface implemented with the Python programming language on a local computer.
Keyword: deep learning, rice, stand detection, unmanned aerial vehicle, YOLOv5

Judul: Analisis Bifurkasi Fluxon pada Persambungan Josephson Bertipe Superkonduktor-Feromagnetik-Superkonduktor (S/F/S)
Abstrak: Dynamics for the Josephson junction of type S/F/S is different from ordinary Josephon junction, where on the junction is contained the addition of tribal sin 2φ which is the result of the second harmonic supercurrent on the Josephson junction of type S/F/S that is related to Shapiro Steps symptoms. Research has been done is analyze the fluxon bifurcation to determine the stability of the fluxon in a Josephson junction of type S/F/S. The analysis was done by using the approach of analytically dynamics system and assisted by numerical solution that uses the application ODE 45 on MATLAB. The result of the critical point and the bifurcation types which is obtained to show the normalized damping (β and ϳₑ) does not affect the change of fluxon stability at the limit of less than one. Parameter β only affects the type of critical point and bifurcation that occurs, if β = 0, for ϳₑ = 0 and ϳₑ = 0.4 the bifurcation occurs is a saddle-center, and if β = 0.5 the bifurcation is a saddle-focus. Parameter ϳₑ affects the amplitude on the generated fluxon oscillation, if ϳₑ = 0 the fluxon amplitude is very small, whereas if ϳₑ = 0.4 the amplitude is larger. In each case, the fluxon stability is affected by the first harmonic supercurrents which is different sign, those are when ϳ₁ < 0 the fluxon is always stable, whereas when ϳ₁ < 0 fluxon is unstable at first but it tends towards to the point of its stability.
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