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: Rice Variety Identification System Based on Drone Image  to Support Seed Certification Process
Abstrak: Proses sertifikasi benih di Indonesia dihadapkan dengan tantangan semakin berkurangnya petugas Pengawas Benih Tanaman (PBT) dan beban kerja yang meningkat dalam hal pengawasan benih bersertifikat. Pemanfaatan teknologi menjadi suatu hal yang penting dalam meningkatkan efisiensi pada pemeriksaan lapangan, salah satu pemeriksaan tersebut adalah pemeriksaan kebenaran varietas tanaman padi yang disertifikasi. Penelitian ini dilakukan untuk mengembangkan sistem identifikasi varietas tanaman padi pada pemeriksaan lapangan sertifikasi benih berbasis citra drone dengan menerapkan algoritme Convolutional Neural Network (CNN). Penelitian ini dilakukan dengan mengidentifikasi dua varietas padi yang berbeda berdasarkan karakter agronomisnya, kemudian membangun model CNN untuk mengotomatisasi identifikasi varietas tersebut berbasis citra drone. Hasil analisis data pada karakter agronomis yang diamati menjustifikasi bahwa kedua varietas yang digunakan berbeda, baik pada karakter tinggi tanaman, jumlah anakan, warna daun, jumlah anakan produktif, panjang malai, dan jumlah gabah per malai. Penelitian ini menghasilkan tiga jenis model CNN yang mampu mengidentifikasi secara akurat varietas IPB 3S dan Inpari 32 dengan tingkat akurasi antara 99,52% sampai 100%., The seed certification process in Indonesia is faced with the challenge of a decreasing number of seed inspectors and an increasing workload in supervising certified seeds. The utilization of technology becomes an important thing in increasing the efficiency of field inspections, one of these inspections is checking the correctness of certified rice varieties. This research was conducted to develop a rice varietal identification system for field inspection of drone image-based seed certification by applying the Convolutional Neural Network (CNN) algorithm. This research was conducted by identifying two varieties different rice varieties based on their agronomic characters, then building a CNN model to automate the identification of these varieties based on drone imagery. The results of data analysis on the observed agronomic characters justified that the two varieties used were different, in terms of plant height, number of tillers, leaf color, number of productive tillers, panicle length, and number of grains per panicle. This study produced three types of CNN models that could accurately identify the varieties of IPB 3S and Inpari 32 with an accuracy rate of 99.52% to 100%.
Keyword: CNN, deep learning, technology, unmanned aerial vehicle

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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