Judul: Deteksi Perilaku Makan Dan Keagresifan Pada Ayam Pedaging  Menggunakan Arsitektur Deteksi Objek You Only Look Once  (YOLOv4)
Abstrak: Permintaan pasar terhadap komoditas ayam pedaging yang tinggi menuntut peternak ayam untuk meningkatkan performa produksi. Biaya produksi dan kesejahteraan unggas menjadi aspek penting dalam daya saing industri. Untuk mengoptimalkan aspek-aspek tersebut, perilaku ayam seperti makan dan keagresifan perlu diperhatikan secara kontinu. Namun hal ini tidak praktis dilakukan sepenuhnya dengan tenaga manusia. Penerapan Precision Livestock Farming dengan Deep Learning dapat membantu memberikan keputusan secara kontinu, real-time, dan terotomasi. Pada penelitian ini diterapkan arsitektur deteksi objek You Only Look Once Version 4 (YOLOv4) untuk mendeteksi perilaku ayam makan dan keagresifan. Data yang digunakan yaitu 1.128 bounding box makan dan 881 bounding box keagresifan. Nilai mAP pelatihan model menghasilkan 73,95 % - 99,74 %, namun nilai mAP pengujian model menghasilkan 16,92 % – 32,38 %. Hal ini menunjukan bahwa pelatihan model deteksi objek masih overfitting. Pelatihan model selanjutnya perlu dilakukan pembangunan dataset lebih lanjut yang lebih relevan dan representatif serta mengurangi data yang redundan.
Keyword: broiler chicken, object detection, feeding behaviour, aggressive behaviour, YOLOv4

Judul: Implementasi Model Yolov4 untuk Pemantauan Perilaku Agresif Ayam Broiler Berbasis Website
Abstrak: Produktivitas daging ayam merupakan hal yang penting dalam menjaga pasokan daging ayam sebagai komoditas penting dalam negeri. Pemantauan terhadap perilaku agresif ayam merupakan salah satu upaya menjaga kesejahteraan ayam. Penelitian untuk mengamati perilaku ayam telah dilakukan dengan bantuan deep learning yaitu YOLOV4 yang menghasilkan model deteksi perilaku agresif ayam. Penelitian ini bertujuan mengimplementasikan model tersebut dalam bentuk website dengan menggunakan framework Flask dengan library OpenCV. Pengguna dapat mengunggah video yang ingin dilakukan deteksi perilaku agresif lalu sistem akan melakukan deteksi dengan YOLOv4 dan menghasilkan video yang telah ditandai. Pengujian implementasi pada web dilakukan dengan memasukkan beberapa jenis video yang memiliki resolusi dan durasi berbeda. Hasil uji menunjukkan bahwa deteksi video berhasil dilakukan dan ditampilkan keterangan tambahan berupa average confidence dan durasi waktu proses. Dapat disimpulkan bahwa, semakin besar resolusi dan durasi video masukan maka semakin tinggi nilai confidence yang didapat dan semakin lama proses deteksinya., The chicken meat productivity is important to maintain the chicken meat supply as an important domestic commodity. The monitoring of the chicken’s aggressive behavior is an effort to maintain their welfare. Many research to observe chicken behavior has been carried out with the help of deep learning, namely YOLOv4 which produces a model to detect the chicken’s aggressive behavior. This study aims to implement the model in the form of a website. It was created using the Flask framework with the OpenCV library. A user can upload video that he wants to detect aggressive behavior, then the system will detect them with YOLOv4 and produce video that have been tagged. Implementation testing on the web is carried out by entering several types of videos that have different resolutions and durations. The test results show that video detection was successful and additional information is displayed in the form of average confidence and processing time duration. In conclusion, the larger the resolution and duration of the input video, the higher the confidence value obtained and the longer the detection process.
Keyword: Aggressive Behavior, Broiler Chicken, Object Detection, Flask, YOLOv4

Judul: Thermal Characteristics and Dynamics of Tropical Cyclone over Western North Pacific Ocean: A Case Study of Typhoon Songda (Chedeng) 2011
Abstrak: The frequent occasion of Tropical Cyclone (TC) over Western North Pacific (WNP) Ocean lies from 10˚N to 26˚N and 121˚E to 170˚E. Sources of TC development come from warm ocean temperature where latent heat is released during condensation processes and subsequently concentrated in the boundary layer. Relative humidity at 850-700 hPa is responsible to produce the most intense storm. Meanwhile, weak vertical wind shear between 850-200 hPa is one of the dynamical factors that lead to the development of TC. Pressure level of 850-200 hPa is chosen to minimize the thermodynamic effect in the lower troposphere. Typhoon Songda (Chedeng in the Philippines) or TSC lasted from May 18th to 30th, 2011 and strengthened into powerful Super Typhoon stage between May 24th and 27th. Long curving track and the exhibition of all sequences of TC life cycle, TSC is then considered as an ideal case of TC. The objective of the study with case of TSC is to investigate the role of thermal characteristics and dynamics on TC intensity. The research is carried out by using Advanced Research of Weather Research and Forecasting model (WRF ARW model) version 3.3. Two domains are employed in the model. The first domain situate from 02˚N to 40˚N and 118˚E to 149˚E and cover spatial resolution () of 33.1 km. The second domain situate from 09˚N to 27˚N and 119˚E to 132˚E with of 11 km. Timestep of the model () is 120 seconds. The model is supported by the following physical parameterization, i.e., Yonsei University Scheme (YSU) in the boundary layer, Kain-Fritsch scheme (KF) in the cumulus, and WRF Single-Moment 3-class scheme in the microphysics. Initial meteorological condition prior to the formation of the cyclone is generated using WRF ARW standard-release model. Sensitivity of the model output is obtained from a selected single-parameter change scenarios, namely, 10 % to 20 % decrease and increase of wind shear initial data at 850-200 hPa, and by 10 % to 20 % decrease of relative humidity initial data at 850-700 hPa. The result showed that the negative correlations between wind shear and intensity are found in wind shear scenarios. Wind shears of 10 ms-1 which lead to strengthening of the wind intensity in the early period of Typhoon development are found in wind shear scenarios. The effect of wind shear changes on wind intensity is statistically significant in the 20 % wind shear scenarios. During mature stage, weak wind intensity occurred when wind shear increase as given in the WRF standard-release and the 20 % decreasing scenario. Different situation is found in the 20 % increasing scenario where wind shear tend to weak and lead to strong wind intensity at this stage. Meanwhile, the reduction of moisture supply at 850-700 hPa resulting from 20 % decreasing relative humidity play a major role in the weakening of wind intensity and reduces mechanical energy of the cycle process as much as 300 J kg-1.
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