Judul: Performa Kambing Peranakan Etawah Muda dan Produktivitas Induk Laktasi dengan Sistem Pemberian Pakan yang Berbeda di Lahan Pasca Galian Pasir
Abstrak: This study aimed to examine the effect of different feeding systems on post-mining of sand land performance of young Peranakan Etawah goat and lactation productivity of Etawah ewe The study used 12 goats (12,592 ± 1,326 kg) within 3-6 months of age. There were two factor of treatment. Treatment one was differences of eating time (W1 = 08.00 a.m to 04.00 p.m and W2 = 02.00 p.m to 04.00 p.m). Treatment two was type of feed (P1 = 100 g/day concentrate and forage, P2 = forage). Protein content of concentrate was 19,32% and forage affered ad libitum. The data were analyzed descriptevely for dry matter intake, feed eficiency and Income Over Feed Cost (IOFC) but the method for daily Body Weight Gain (BWG) used in this study was 2x2 factorial completely randomized design. The results showed that BWG of goats was significantly different among treatment (P<0,01). There was interaction betwent W & P trial. Daily BWG was significantly higher in trial W1 and P1 as well as IOFC with average daily BWG is 74,41 ± 11,89 and IOFC is Rp. 249.666,67. Subsequent study on the lactation productivity Etawah ewe of different feeding systems. This study used 15 goats within 10 goats in lactation two and 5 goats in lactation three. Treatment was differences type of feed (P1 = forage, P2 = forage and food industry waste). The data feed consumption were analyzed descriptevely. Data production and milk quality were analyzed using t test. Dry matter of milk was very significant higher (P<0,01) in ration added food industry waste than ration without food industry waste adition, while protein content and fat in milk was significant (P<0,05).
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Judul: Produktivitas Kambing Peranakan Etawah Jantan dan Betina dengan Pemberian Ransum Grower Komplit Tinggi Energi dan Protein
Abstrak: Etawah goats in growing phase require a complete and balance nutrition for growth and preparation for reproduction phase, especially energy and protein. This research aimed to compare the productivity of Etawah growing kids fed with high energy and proteins complete rations based on NRC (2006). This research used 4 male and 4 female goats at 4 months age with initial body weight of 11.38±3.15 kg and 12.13±2.70 kg, respectively the data were analyzed with TTest with 2 treatments (male and female) and 4 groups. Concentrate and forages ratio was 90% and 10% from total feed. Parameters in this research were dry matter intake, body weight gain, protein efficiency ratio, and feed conversion ratio. Statistical analysis showed that no significant results for dry matter intakes (g day- 1), but body weight gain (g day-1), feed conversion ratio and protein efficiency ratio of male goats was significantly (P<0.05) better than female group. These results indicated that consumption and body weight gain were in line with NRC (2006) recomendation so as to the implementation in Indonesia needs to be further investigated.
Keyword: Bogor Agricultural University (IPB), performance, high energy protein rations, feed conversion, etawah growing kids

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