Judul: Optimasi Arsitektur You Only Look Once Version 7 (YOLOv7) untuk Deteksi Perilaku Agresif pada Ayam Pedaging
Abstrak: Saat ini, Computer Vision telah banyak diterapkan di sektor peternakan untuk membantu pemantauan hewan ternak. Perilaku agresif merupakan salah satu permasalahan umum pada unggas berkoloni seperti ayam pedaging yang dapat berpengaruh terhadap efisiensi produktivitas dan kesejahteraan ternak. Penelitian sebelumnya menggunakan YOLOv4 berhasil membangun model deteksi perilaku agresif pada ayam pedaging namun performa deteksi model masih cukup rendah. Penelitian ini berfokus pada peningkatan presisi model dalam mendeteksi perilaku agresif pada ayam pedaging dengan mengimplementasikan arsitektur deteksi objek You Only Look Once versi ketujuh (YOLOv7) serta menerapkan penyesuaian label untuk meningkatkan kualitas data. Hasil pelatihan menggunakan arsitektur YOLOv7 menghasilkan nilai mAP (Mean Average Precision) sebesar 24,6% meningkatkan performa model YOLOv4 sebelumnya sebesar 17,1% dalam deteksi perilaku agresif. Selanjutnya, model YOLOv7 dengan penyesuaian label pada data mendapatkan nilai mAP yang lebih baik yaitu sebesar 53,7% yang menunjukkan bahwa penyesuaian label berpengaruh terhadap kinerja model., Currently, Computer Vision has been widely applied in the livestock sector to help monitor livestock. Aggressive behavior is one of the common problems in colonial poultry such as broilers which can affect productivity efficiency and livestock welfare. Previous studies using YOLOv4 have succeeded in building a model for detecting aggressive behavior in broilers, but the detection performance of the model is still quite low. This research focuses on increasing the precision of the model in detecting aggressive behavior in broilers by implementing the seventh version of the You Only Look Once object detection architecture (YOLOv7) and applying label adjustments to improve data quality. The results of training using the YOLOv7 architecture yielded a mAP (Mean Average Precision) value of 24.6%, increasing the performance of the previous YOLOv4 model by 17,1% in the detection of aggressive behavior. Furthermore, the YOLOv7 model with label adjustments on the data obtained a better mAP value of 53.7% which indicated that label adjustments had an effect on model performance.
Keyword: broiler chicken, object detection, aggressive behavior, YOLOv7

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: Merremia dennstedt ex endlicher (Convolvulaceae) in Sumatra.
Abstrak: Merremia is a small genus in the family Convolvulaceae. Merremia is a perennial or annual herb or woody liana with stems creeping or twinning on trees or rocks, put out milky white sap when injured and some species with tubers. Merremia is often confused with genus Ipomoea, due to they have resemblance in funnel shaped or campanulate corolla and straight anther. Phenetically, some members of Merremia have yellow flower whereas Ipomoea has white, purple or purplish white. Merremia is also confused with Operculina due both have non-spinulose pollen and dehiscent capsule. Nevertheless, Operculina characterized by circumciselli capsule and Merremia characterized by valve capsule. The important character in Merremia is the spread of the hairs on corolla that can be easily observed at the mature flower bud. The genus Merremia is found in the tropical regions of both hemispheres. Merremia wildly distributed throughout the tropical regions of Asia, Africa, Australia, North and South America and approximately there were 80 species of Merremia in the world. A Total of 175 sheets (164 collection numbers) which are deposited in Herbarium Bogoriense, 97 sheets (95 collection numbers) which are deposited Andalas University Herbarium (ANDA) and 9 sheets (4 collection numbers) which are deposited in BIOTROP Herbarium (BIOT) were studied. It composed of all specimens of Merremia in Sumatra. Additional specimens were collected by the author 28 sheets (15 collection numbers) from North Sumatra, West Sumatra and Lampung, which are stored in BO and BIOT. The digital data that consist of type specimens and distributions of species from JSTOR Plant Science and Convolvulaceae Unlimited were also used here. All the hebarium specimens were studied based on their morphological characters following de Vogel (1987) and Rifai (1976). The terminology of morphological characters followed Lawrence (1955), Veldkamp (1987), Harris and Harris (1994), Hickey and King (2005) and Stearn (1983). Information on the habitat, ecology, distribution, vernacular names and uses were noted from the specimens label and literatures. Taxonomic studies in Sumatra were conducted. There are eight species, one subspecies, one varietas and two forma from nine previously species (M. cissoides, M. dissecta, M. emarginata, M. hirta, M. peltata, M. quinquefolia, M. tridentata, M. tuberosa and M. vitifolia), two subspecies (M. umbellata ssp. orientalis and M. tridentata ssp. hastata), one varietas (M. boisiana var. sumatrana) and two forma (M. hederacea f. pubescens and M. hederacea f. barbata) can be recognized. One species (M. tridentata) and subspecies (M. tridentata ssp. hastata) are excluded because they have been proposed to different new taxa Xenostegia by Austin and Staples. The existence of M. emarginata was noted as new record in Sumatra and M. cissoides was noted as new record in Indonesia. A phylogenetic analysis using PAUP*4.Ob10 was undertaken based on morphological characters, using Evolvulus nummularius and Porana volubis as the out group. The phylogenetic analysis used 35 morphological characters was resulted consistency index (CI) = 0.60, homoplasy index (HI) = 0.40, retention index (RI) = 0.56 and Rescaled consistency index (RC) = 0.34. Phylogenetic tree used morphological characters divided taxa into two groups. The separation support M. boisiana var. sumatrana, M. cissoides, M.
Keyword: Convolvulaceae, Merremia, morphology, phylogenetic, Sumatra