Daftar isi:
- Perhitungan gas rumah kaca asal ternak
- Bioaktif peptida maggot (Hermetia illucens)
- Artificial neural network to predict crude protein and crude fiber
- Nonlinear model on calcium and phosphor requirements
- Optimasi ekstraksi larva Hermetia illucens
Kumpulan tugas selama mengikuti perkuliahan pascasarjana di Institut Pertanina Bogor, baik dalam bentuk presentasi dan artikel.
Perhitungan gas rumah kaca asal ternak
Perhitungan gas rumah kaca yang dihasilkan dari sektor peternakan di Jawa Timur.
Bioaktif peptida maggot (Hermetia illucens)
Masyarakat Uni Eropa melarang penggunaan AIP karena mampu menimbulakan resistensi dan residu pada produk hasil ternak (Leeson dan Summer 2009). Peraturan Pemerintah nomor 14 tahun 2017. pasal 15 dan 16 tentang pelarang penggunaan AIP pada pakan semenjak Januari 2018.
Artificial neural network to predict crude protein and crude fiber
The aim of this research was to build and compare multilinear regression, artificial neural network and deep learning model to predict crude protein and fibre content from physical properties of feedstuffs. The 91 data were obtained from https://repository.ipb.ac.id using keywords (e.g. sifat fisik and pakan). To reduce the dimensional of the data had been transformed. The independent variables consist of X1: specific gravity, X2: bulk density, X3: compacted bulk density and X4: angle of repose. The dependent variable was Y1: crude protein and Y2: crude fibre. Multilinear regression (MLR), artificial neural network (ANN) and deep learning (DLr) model built by R programing language 3.5.3 using library R-base, neuralnet and keras. Mean square error (MSE) used to evaluate the model, lower MSE within two models means better than others. Mean square error of crude protein MLR model was higher than ANN model, that were 0.0143 and 0.0093. Besides that, MSE of crude fibre MLR model was lower than ANN model, 0.0605 and 0.0722. Furthermore, the model built using DLr can improve ANN model, that has MSE 0.0249 and 0.0449, respectively, for crude protein and crude fibre deep learning model. The deep learning model generally can perform better to predict crude protein and fibre from physical properties than multilinear regression and artificial neural network.
Nonlinear model on calcium and phosphor requirements
Telaah pemodelan kebutuhan nutrien pada ternak khusunya ayam petelur bermanfaat dalam memprediksi kebutuhan dari ternak dan mampu menggambarkan status fisiologis ternak. Penghimpunana data rataan kebutuhan fosfor tersedia per hari dari enam strain ayam ras petelur umur 1-90 minggu. Komparasi model nonlinear (Logistik, Gompertz, dan Weiibulle) yang tepat untuk menggambarkan kebutuhan fosfor tersedia pada ayam ras petelur.