Judul: Pembangunan Sistem Pemantauan Jarak Jauh Otomatis untuk Perilaku Ayam menggunakan Kamera Inframerah
Abstrak: Penelitian ini bertujuan untuk membangun sistem pemantauan otomatis jarak jauh menggunakan Raspberry Pi (RPi). Google Drive (GDrive) API digunakan sebagai metode transmisi data dan GDrive sebagai metode penyimpanan data. Sebuah livestream dari kandang ayam dapat diakses melalui halaman web dengan memanfaatkan ngrok sebagai tunneling software sehingga livestream dapat diakses secara publik menggunakan peramban web. Tiga resolusi video berukuran 640x480, 800x600, 1024x768 dengan masing-masing dua variasi framerate 15 dan 30 digunakan sebagai skenario pengujian perekaman video berdurasi lima menit sebanyak 12 kali untuk masing-masing resolusi dan framerate-nya. Skenario 640x480 dan 800x600 memiliki perbedaan peningkatan ukuran file sebesar 90% untuk 15 fps dan 85% untuk 30 fps, sedangkan peningkatan dari 800x600 ke 1024x768 relatif sangat tinggi yaitu sebesar 220~244% dan akan memakan banyak memori penyimpanan untuk perekaman jangka panjang., The purpose of this research is to develop an automatic remote monitoring system using Raspberry Pi (RPi), the RPi device is connected to Google Drive (GDrive) via GDrive API as the data transmission method for storing the recordings of chicken coop. A livestream of the chicken coop is accessible through a simple http web page utilizing ngrok as a tunneling software so that the livestream can be publicly accessed from anywhere using a web browser. The result of this research is an automatic remote monitoring system that can ease the process of remote monitoring broiler chickens and reduce production costs as well as recordings of the chickens that can be used for further research related to poultry behavior patterns. Three video resolutions of 640x480, 800x600, 1024x768 with 15 and 30 as the framerates each were used as test scenarios of 12 times five minutes recordings for each resolutions and framerates. The 640x480 and 800x600 scenarios showed an increasing difference of 90% for 15 fps and 85% for 30 fps, whereas the increase from 800x600 to 1024x768 showed a relatively high increase of 220~244% which will take a lot of memory space for long term monitoring.
Keyword: automatic remote monitoring system, API, chicken, google drive, raspberry pi

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: Karakteristik Kimia Kayu Reaksi Ulin (Eusideroxylon zwageri T. et B.)
Abstrak: Reaction wood which formed in hardwood generally has a lower klason lignin content, higher acid soluble lignin content, and higher syringil unit than opposite wood. Ulin wood (E. zwageri) contains high klason lignin and low acid soluble lignin contents which closed to lignin characteristic of softwood. This experimental aimed to study the difference of chemical characteristic of reaction wood (klason lignin content, acid soluble lignin content, and lignin monomer composition) and polysaccharides content which formed in E. zwageri stem. Visually, the reaction wood part was formed in upper side of leaning stem. Klason lignin content in reaction wood part was lower (32.71%) than opposite wood part (33.74%). However, the lowest lignin content was found in the side wood part (28.15%). Lignin of E. zwageri was composed by syringyl and guaiacyl monomer with average of syringil to guaisyl ratio 0,2. Acid soluble lignin content in E. zwageri was very low (0.69%) that positively correlated with the proportion of syringil unit in lignin. Polysaccharide content of reaction wood part (63.66%) was higher than opposite wood part (62.88%), but the highest content polysaccharides presented in the side wood part (64.19%).
Keyword: Polisaccharide, syringil-guaiasyl ratio, lignin, E. zwageri, reaction wood