Judul: Estimation of infestation level of diamondback moth and cabbage cluster caterpillar based on images
Abstrak: Diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), and cabbage cluster caterpillar, Crocidolomia pavonana (F.) (Lepidoptera: Pyralidae), are the two most important insect pests of cabbage in Indonesia. Research was conducted with the objectives to assess level of infestsation of the pests based on digital images. Infestation levels were classified into five categories: healthy/negligible, low, moderate, severe and very severe. The data used in this research were images of cabbage that were healthy and attacked by Plutella xylostella and/or Crocidolomia pavonana. Images of cabbages with crop and without crop were 476 and 24 images respectively. The images were divided in two sets: training set and test set with percentage are 80% and 20% respectively. Ten-fold cross-validation was used to find the best model for classifier. Estimation of infestation level initially was made on individual plants. Cabbages with crop and without crop were classified with probabilistic neural network (PNN) based on four Haralick features, namely: contrast, correlation, dissimilarity, and homogeneity. Gray level co-occurrence matrix (GLCM) was used to extract texture features. The research showed that the average accuracy from PNN as a classifier for classifying cabbage with crop and without crop was 92.4%. Cabbage with crop was segmented, and after that the crop area was detected with randomized hough transform (RHT). The detected crop then were grouped into three categories: good, good enough and not good. Our research showed that estimation of crop area with RHT had accuracy of 89.13 %. Image enhancement was then applied to the crop area through shadow removal and smoothing. The level of damage was estimated based one proportion of holes on the crop area by applying erosion operations and thresholding with Otsu. Estimation of level of damage for cabbage with crop had accuracy of 76.1%. Estimation of level of damage for cabbage with crop and without crop had accuracy of 75%. Having obtained the level of damage for each plant, the final stage of this research was to assess level of caterpillar infestation in cabbage fields. Error arising from this estimation was 3.25%. Results of this research could be used by pest observers or extension agents in making pest management decisions.
Keyword: Crocidolomia pavonana, gray level co-occurrence matrix, Plutella xylostella, probabilistic neural network, randomized hough transform

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: Formulasi Hamiltonian untuk Menggambarkan Gerak Gelombang Soliter Dimensi Tiga di Permukaan Laut
Abstrak: Gelombang permukaan adalah gelombang yang terjadi perbedaan rapat massa antara air dan udara. Salah satu gelombang permukaan yang ditinjau adalah gelombang soliter. Formulasi gelombang soliter dilakukan dengan menggunakan formulasi Hamiltonian.
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