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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Otomasi Kontrol dan Instrumentasi
Mursito Budi, Eko
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Estimating broiler heat stress using computer vision and machine learning Anggoro Agung, Muhammad Iqbal; Mursito Budi, Eko; Indar Mandasari, Miranti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2922-2934

Abstract

To optimize and enhance the efficiency of broiler chicken farming, it is essential to maintain the chicken’s welfare, as heat stress can decrease growth efficiency. The temperature-humidity index (THI) is a key indicator used to determine if chickens are experiencing heat stress. Precision livestock farming (PLF) based on computer vision is one method that can assist farmers in continuously and automatically monitoring the condition of their chickens. This research developed a computer vision-based PLF system to observe chickens with CP 707 strain in a commercial farm using the Mask region-based convolutional neural network (Mask R-CNN) method and object tracking algorithms to analyze features such as the cluster index, unrest index, and the distance traveled by broilers. The results indicated that all features tend to inversely correlate with the THI value, with the cluster index showing the most noticeable tendency. Additionally, it was found that external factors, such as the presence of farmers around the observation area, can affect the chickens' behavior, although the cluster index feature is relatively resilient to disturbances if the operator is not captured by the camera. It was concluded that there is a relationship between the features and the THI value; however, these features are not yet sufficient to distinguish the condition of chickens under high and low THI conditions in real-time.
Evaluasi Algoritme Bresenham dan Digital Differential Analyzer (DDA) untuk Pengontrolan Koordinasi Gerakan Dua Motor Stepper pada Robot untuk Simulator Penusukan Jarum Medis Devina, Helma; Jessica, Jessica; Susanti, Hesty; Suprijanto, Suprijanto; Mursito Budi, Eko
Jurnal Otomasi Kontrol dan Instrumentasi Vol 11 No 2 (2019): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2019.11.2.1

Abstract

Citra USG merupakan salah satu kebutuhan pendukung yang krusial dalam menuntun penusukan jarum medis ke dalam tubuh pasien. Konsistensi citra visibilitas jarum tersebut ditentukan oleh dua faktor, yaitu teknis pencitraan yang digunakan (spesifikasi transduser USG dan karakteristik akustik jarum) dan posisi jarum (kedalaman dan sudut penusukan) terhadap muka gelombang bidang yang dipancarkan pada bidang pandang tertentu. Sebelum diaplikasikan ke tubuh pasien, diperlukan suatu informasi berupa peta visibilitas jarum yang dicitrakan USG dengan mensimulasikan teknik penusukan secara in-plane menggunakan sistem robot 3 DOF. Hal penting dari robot 3 DOF yang digunakan adalah koordinasi gerakan dua motor stepper yang menjamin gerakan penusukan jarum berada pada trajektori berupa garis lurus untuk berbagai kombinasi kedalaman dan sudut penusukan. Oleh karena itu, pada makalah ini akan dilakukan evaluasi algoritme Bresenham dan Digital Differential Analyzer (DDA) dalam mengatur pemberian jumlah pulsa masukan pada kedua motor stepper sehingga diperoleh gerakan yang diinginkan. Pengujian dilakukan dengan menggunakan jarum spinal 22G pada gerakan penusukan dengan rentang sudut penusukan 08 - 908 dengan perubahan sudut 22,58 dan kedalaman penusukan 10 "“ 50 mm dengan perubahan kedalaman 10 mm. Berdasarkan nilai RMSE, standar deviasi, linearitas, dan R2, algoritme Bresenham lebih baik dibandingkan dengan algoritme DDA. 
Co-Authors Anggoro Agung, Muhammad Iqbal Devina, Helma Hesty Susanti Indar Mandasari, Miranti Jessica Jessica Suprijanto Suprijanto