Authors : Sindung Hadwi Widi Sasono; Helmy Iskandar; Sri Kusumastuti

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/34uerwms

Scribd : https://tinyurl.com/43hz5yy7

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1889

Abstract : Technological advancements impact various sectors of life, including animal husbandry, which is an important part of the education curriculum. Innovations are needed to optimize livestock management, especially ruminant farming, which is crucial for producing meat and milk. SMKN 1 Bawen, with its study program focusing on ruminant farming (cows, sheep, and goats), still uses conventional recording methods, leading to inefficiencies and limited information for potential buyers. To address this, a QR Code-based livestock management and identification system was developed and integrated into a website and Android application using agile methods. Black box testing confirmed that all features function well. Stress testing on the website with 2 Mbps bandwidth showed 98% successful requests and 2% failed requests, with a data transmission speed of 52,816 bits per second and a reception speed of 268,624 bits per second. At 50 Mbps bandwidth, the system had a 100% success rate, with a data sending speed of 44,304 bits per second and a receiving speed of 343,272 bits per second, demonstrating good and stable performance across different bandwidths. The Android application, tested on a POCO X3 with 6 GB RAM and 128 GB internal storage, showed a highest CPU usage of 39%, memory usage of 326 MB, and network speeds of 10.3 KB/s for data received and 535.9 KB/s for data sent. These results indicate that the application runs optimally and is suitable for use.

Keywords : Web, Android, QR Code, Livestock Management, Ruminant Farming

References :

1. C. Trilaksana, E. Akbartama, A. Muttaqin, and O. Setyawati, “Internet of Things-based Cow Body Weight Recording System,” J. EECCIS (Electrics, Electron. Commun. Control. Informatics, Syst., vol. 17, no. 1, pp. 8–12, 2023, doi: 10.21776/jeeccis.v17i1.1632.
2. J. G. Rajendran, M. Alagarsamy, V. Seva, P. M. Dinesh, B. Rajangam, and K. Suriyan, “IoT based tracking cattle healthmonitoring system using wireless sensors,” Bull. Electr. Eng. Informatics, vol. 12, no. 5, pp. 3086–3094, 2023, doi: 10.11591/eei.v12i5.4610.
3. K. Subandi, H. Hermawan, and A. S. Aryani, “Value Chain Analysis Indonesian Animal Husbandry Industry,” J. Appl. Sci. Adv. Technol., vol. 2, no. 1, pp. 21–28, 2019, [Online].Available:  https://jurnal.umj.ac.id/    index.php/JASAT/article/view/4688
4. R. S. Pressman, Software Quality Engineering: A Practitioner’s Approach, 7th ed., vol. 9781118592. The McGraw-Hill Companies, Inc, 2014. doi: 10.1002/9781118830208.
5. U. Riaz, M. Idris, M. Ahmed, F. Ali, and L. Yang, “Infrared Thermography as a Potential Non-Invasive Tool for Estrus Detection in Cattle and Buffaloes,” Animals, vol. 13, no. 8, 2023, doi: 10.3390/ani13081425.
6. V. M. T. Aleluia, V. N. G. J. Soares, J. M. L. P. Caldeira, and P. D. Gaspar, “Livestock Monitoring Prototype Implementation and Validation,” Rev. Inform. Teor. e Apl., vol. 30, no. 1, pp. 53–65, 2023, doi: 10.22456/2175-2745.127207.
7. W. Tang, A. Biglari, R. Ebarb, T. Pickett, S. Smallidge, and M. Ward, “A smart sensing system of water quality and intake monitoring for livestock and wild animals,” Sensors, vol. 21, no. 8, pp. 7–10, 2021, doi: 10.3390/s21082885
8. Y. Usman, “Pemberian Pakan Serat Sisa Tanaman Pertanian ( Jerami Kacang Tanah , Jerami Jagung , Pucuk Tebu ) Terhadap Evolusi pH , N-NH 3 dan VFA Di,” J. Agripet, vol. 13, no. 2, hal. 53–58, 2013.