Multi-Horizon Prediction Of Broiler Mortality With Decision Tree And SVM: A Case Study In Small-To-Medium Farms In Sukabumi - Zaenal Abidin, Budi Jejen; Wijaya, Bambang Somantri; Hayatus Sahla, Ghaida Sandie (Lembaga Layanan Pendidikan Tinggi Wilayah IV, 2025)
Abstrak Penelitian ini mengembangkan model machine learning untuk memprediksi mortalitas harian (jumlah kematian) ayam broiler 1–7 hari ke depan secara multi-horizon (H+1–H+7) menggunakan data 12 kandang di Sukabumi selama lima siklus produksi (Juli 2024–Juli 2025). Data dipraproses melalui imputasi nilai hilang (Random Forest), penanganan outlier (IQR dan winsorizing), normalisasi Z-score, serta seleksi fitur (Pearson dan ReliefF). Support Vector Regression (SVR) dan Decision Tree Regression (DTR) dibandingkan dengan hasil menunjukkan SVR unggul untuk prediksi jangka pendek (H+1–H+2; R² = 0,842–0,760), tetapi performanya menurun pada horizon yang lebih panjang. Sebaliknya, DTR lebih stabil pada horizon menengah–panjang (H+5–H+7; R² ≈ 0,683–0,696). Faktor dominan yang berkaitan dengan mortalitas adalah umur dan bobot rata-rata, serta kondisi kandang seperti ventilasi/kecepatan angin, kepadatan tebar, NH₃, dan suhu. Evaluasi dilakukan dengan repeated holdout 70/30 (10 repetisi) dan 5-fold cross-validation pada data latih, mendukung prototipe sebagai peringatan dini. Kata kunci: ayam broiler, decision tree regression, mortalitas, multi-horizon forecasting, support vector regression Abstract This study develops a machine-learning model to predict daily broiler mortality (death counts) 1–7 days ahead using a multi-horizon approach (H+1–H+7), based on data from 12 broiler houses in Sukabumi across five production cycles (July 2024–July 2025). Data were preprocessed using missing-value imputation (Random Forest), outlier handling (IQR and winsorizing), Z-score normalization, and feature selection (Pearson correlation and ReliefF). Support Vector Regression (SVR) and Decision Tree Regression (DTR) were compared. Results show that SVR outperformed DTR for short-term prediction (H+1–H+2; R² = 0.843–0.760), but its performance declined at longer horizons. In contrast, DTR was more stable for medium-to-long horizons (H+5–H+7; R² ≈ 0.683–0.696). Dominant factors associated with mortality included age and average body weight, as well as housing conditions such as ventilation/wind speed, stocking density, NH₃, and temperature. Evaluation used repeated 70/30 holdout (10 repetitions) and 5-fold cross-validation on the training data, supporting a prototype as an early warning tool. Keywords: broiler chicken, decision tree regression, mortality, multi-horizon forecasting, support vector regression