Judul: Performa Bakalan Induk Domba Lokal Yang Diberi Ransum Dengan Level Energi Yang Berbeda.
Abstrak: Young female sheep require adequate energy to improve their performance and reproduction. However, in reality most farmers are not aware of the condition. This experiment was aimed at investigating the effect of different energy levels on the performance of young female sheep. Twelve post-weaning female sheep with average body weight of 9.67 ± 1.72 kg were used in this experiment and fed three rations with different energy levels for 89 days. The rations as treatments consisted of P1= ration with 65% TDN; P2= ration with 70% TDN; and P3= ration with 75% TDN. The experiment used completely randomized design, and data analysis were done with analysis of variance. Further analysis was conducted using orthogonal contrast. Results of the experiment show that different energy levels did not significantly affect consumption of TDN, crude protein, body weight gain, and feed efficiency, but it significantly (P < 0.05) influenced the Ca consumption. It is concluded that ration with 65% TDN is sufficient to support the performances of young female sheep.
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Judul: Penampilan Produksi dan Reproduksi Calon Induk Domba Lokal (Jonggol) yang Mendapat Ransum dengan Sumber Energi Berbeda.
Abstrak: Animal farming takes role in providing the animal protein, increasing the income of the farmers, job generating and preserving the nature resources. Sheep farming belongs to a common business for local farmers, although the productivity of Indonesian local sheep is very low which is drived by genetic and environment. The crucial factors are the low feed quality and the absence of production and reproduction targets. Flushing with high quality feed becomes a choice. This experiment was conducted to study the effect of different energy sources in ration on the performance of the local (Jonggol) young ewes. A total of 15 local (Jonggol) young ewes were used within the body weight average of 16.1±1.17 kg. All ewes were fed a ration with 40:60 for forage and concentrate, respectively. Nutrients composition was calculated according to iso-energy and iso-protein (66% TDN and 14% protein). Completely randomized design (CRD) was applied with 3 different treatments of energy source of feed in 5 replications. The energy sources used were maize (P1), tapioca by product (cassava) (P2) and mixture of maize and cassava (P3). The variables measured were the dry matter consumption before and after pregnancy, body weight gain before and after pregnancy, feed conversion, first estrus and successful mating, service per conception rate and number of pregnancies and fetus. The first estrus and successful mating, service per conception rate and number of pregnancy and fetus were descriptively analyzed. Other variables were subjected to analyses of variance (ANOVA) and Tukey to test the differences. The results showed that different energy feedstuff has no effect on the dry matter consumption before and after pregnancy, body weight gain before and after pregnancy, feed conversion, service per conception rate, and number of pregnancy and fetus. As conclusion, ration with different energy sources had not influence to the productive and reproductive performances of local (Jonggol) young ewes.
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Judul: Quantum Convolutional Neural Network (QCNN) for Classification Brain Tumor MRI Image
Abstrak: Penelitian ini bertujuan untuk mengembangkan model deteksi tumor otak menggunakan citra MRI dengan metode Quantum Convolutional Neural Network. Data yang digunakan adalah citra MRI otak yang terdiri dari citra tumor otak glioma, meningioma, hipofisis, dan negatif tumor yang dibagi kedalam data latih dan uji dengan rasio 80:20. Selanjutnya data latih dilakukan penyeimbangan dengan teknik oversampling. Dilakukan pengembangan model untuk mendeteksi tumor otak (biner), dan juga model untuk mengklasifikasikan jenis tumor otak (multiclass). Parameter yang digunakan dalam pengembangan model adalah jumlah ukuran filer = 2 dan kedalaman = 4, dengan qubit 4, dan dilatih dengan jumlah epoch sebanyak 10. Data akan diproses dengan metode Quantum Convolutional Neural Network yang terdiri dari lapisan quantum convolution, lapisan flatten, dan lapisan dense. Model multiclass mendapatkan akurasi 52%, setelah data latih diseimbangkan akurasi model meningkat menjadi 62%, sedangkan model biner mendapatkan akurasi 88%, setelah data latih diseimbangkan akurasi model meningkat menjadi 89%.
Keyword: Citra MRI, Klasifikasi Citra, QCNN, Tumor Otak