Analisis Prediksi Produksi Telur Menggunakan Algoritma Decision Tree Regression
DOI:
https://doi.org/10.26418/justin.v14i1.92550Keywords:
Prediksi, Regresi, Telur,Abstract
Produksi telur merupakan salah satu sektor penting dalam industri peternakan yang memiliki dampak signifikan terhadap ketahanan pangan. Namun, prediksi produksi telur yang akurat masih menjadi tantangan, mengingat banyaknya faktor yang mempengaruhi hasil produksi. Kesalahan prediksi, sekecil apapun, dapat berdampak langsung pada manajemen inventaris, fluktuasi harga, dan efisiensi rantai pasok. Penelitian ini bertujuan untuk mengembangkan model prediksi produksi telur menggunakan algoritma Decision Tree Regression. Kontribusi unik dari model ini adalah kemampuannya sebagai 'white-box' yang tidak hanya memberikan akurasi prediksi, tetapi juga wawasan berupa aturan keputusan yang dapat diinterpretasi (interpretable) oleh peternak, dengan memanfaatkan data terkait faktor-faktor yang mempengaruhi produksi, seperti jumlah ayam, usia ayam, suhu, kelembaban, pakan, dan waktu. Dalam penelitian ini, data yang digunakan berjumlah 250 data poin yang mencakup variabel-variabel yang relevan. Model yang dibangun menunjukkan hasil yang cukup baik dengan nilai R² sebesar 0.88, yang mengindikasikan bahwa model mampu menjelaskan sekitar 88% variansi dalam data. Meskipun nilai Mean Squared Error (MSE) yang dihasilkan sebesar 2427.33, menunjukkan adanya beberapa kesalahan prediksi, model ini secara keseluruhan memberikan hasil yang akurat dalam memprediksi produksi telur. Perbandingan antara nilai aktual dan prediksi menunjukkan bahwa sebagian besar prediksi cukup mendekati nilai aktual. Hasil dari penelitian ini menunjukkan bahwa algoritma Decision Tree Regression dapat menjadi alat yang efektif untuk memprediksi produksi telur, memberikan manfaat dalam pengambilan keputusan di sektor peternakan. Akurasi model ini krusial untuk membantu peternak mengoptimalkan jadwal produksi dan alokasi sumber daya. Meskipun model ini memberikan hasil yang memadai, penelitian lebih lanjut perlu dilakukan dengan tuning hyperparameter, penggunaan algoritma lain, serta peningkatan jumlah data untuk memperbaiki akurasi model. Penelitian ini diharapkan dapat memberikan kontribusi untuk peningkatan efisiensi dalam industri peternakan telur.References
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