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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) JNANALOKA Journal of English Education and Linguistics Journal Corner of Education, Linguistics, and Literature Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Prosiding Seminar Nasional Teknologi Informasi dan Bisnis JR-ELT (Journal of Research in English Language Teaching)
Anjar Setiawan
Universitas Pamulang PSDKU Serang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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CATTLE BODY WEIGHT PREDICTION USING REGRESSION MACHINE LEARNING Anjar Setiawan; Utami, Ema; Ariatmanto, Dhani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1521

Abstract

Increasing efficiency and productivity in the cattle farming industry can have a significant economic impact. Cow health and productivity problems directly impact the quality of the meat and milk produced. In the cattle farming industry, it can help predict cow weight oriented to beef and milk quality. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. This research aims to predict cow weight by increasing the results of smaller MAE values. The methods used are linear Regressor (LR), Random Forest Regressor (RFR), Support Vector Regressor (SVR), K-Neighbors Regressor (KNR), Multi-layer Perceptron Regressor (MLPR), Gradient Boosting Regressor (GBR), Light Gradient boosting (LGB), and extreme gradient boosting regressor (XGBR). Producing cattle weight predictions using the SVR method produces the best values, namely mean absolute error (MAE) of 0.09 kg, mean absolute perception error (MAPE) of 0.02%, root mean square error (RMSE) of 0.08 kg, and R-square of 0.97 compared to with other algorithm methods and the results of statistical correlation analysis showed several significant relationships between morphometric variables and live weight.
Co-Authors Ahmad Habibi Maftukhan Amrizal Amrizal Angga Pramadjaya Arifah, Tanalina Atina, Vihi Auliyah, Ulul Azmiati Az Zahra, Olivia Basit, Muhammad Abdul DHANI ARIATMANTO Dimas Permana Dyas Rasyid Ema Utami Evan Musa Pratama Fikri Maulana Iqbal Hamuda, Hayadi Hartanti, Dwi Muhamad Abdul Aziz Muhammad Sidiq Noviyanto Noviyanto, Noviyanto Testiana Deni Wijayatiningsih Zoe Zia Sidan