Judul: Optimalisasi kondisi fisiologis puyuh Jepang (Coturnix coturnix japonica) dengan suplementasi serbuk kunyit (Curcuma longa)
Abstrak: Quail have an economic value. Egg production decreased as the decline of the quail’s physiological conditions, either due to aging or stress. The purpose of this study was to optimize the physiological condition of quail with turmeric powder supplementation. The study was conducted in four stages: 1. Determining the physiological deterioration quail, 2. Getting the optimal dose of turmeric powder to improve quail physiological conditions, 3. Knowing the profile of reproductive hormones in an ovulation cycle, 4. Determining the effect of turmeric powder at different times and types of feed on Japanese quail. The decline in quail’s liver function occurs after the age of 7-8 months as evidenced by a decrease in body weight, percentage of liver weight / body weight, hepatocytes diameter, ovarium and reproductive tracts weight, as well as an increase in the levels of SGPT and SGOT serum.Turmeric powder at a dose of 54 mg / quail / day may improve liver function. Curcumin in the content of turmeric powder at 7.97% has a hepatoprotective effect, which was supported by the percentage increase in liver weight / body weight, DNA content of the liver, hepatocytes diameter, and decrease in SGPT and SGOT levels, triglycerides, and blood cholesterol. Content of phytoestrogens in turmeric powder at 6.73% was able to induce increased synthesis vitellogenin as yolk precursor, so vitellogenin levels in the blood rise. Most vitellogenin stored in hepatocytes was indicated by the number of vacuoles in the cytoplasm of hepatocytes. Vitellogenin mainly distributed to the ovarian follicle to the development hierarchy. The number of growing follicles may accelerate follicular maturation, as evidenced by the shortening of the cycle time of ovulation approximately 5 hours 35 minutes, and an increase in the number of eggs. The number of follicles was ovulated that can spur the growth of the glandular cells of the reproductive tract to secrete albumen, shell membranes, and eggshell. Turmeric powder is also able to increase the absorption of calcium, so it can improve the quality of the eggs. The number of growing follicles also contributes to improving egg quality which evidenced by the decrease in cholesterol and fat eggs. This is due to the growing number of follicles, fat, and cholesterol will be distributed to many follicles. Provision of turmeric powder was most effective when given before maturity sexual. Giving turmeric may extend quail’s productivity. Feeding the higher protein did not able to increase the productivity of quail.
Keyword: Japanese quail, liver function, turmeric powder, vitellogenin.

Judul: Penambahan Tepung dan Ekstrak Daun Jati (Tectona grandiss Linn. F.) Pada Puyuh coturnix coturnix japonica
Abstrak: Tectone leaf is plant perennial who had the chance very large for use as fodder. The availability and its abundant known was selected as one alternative plant a source of materials local feed. Generally of tecton trees used to be taken wood as major commodity logistics. Hence in the study is done the utilization of waste in the form of tecton leaf meal in diet laying quail. Active ingredients that was found in the leaves of flavonoid tecton namely. A compound phytochemistry is able to inhibit the growth of pathogenic bacteria in the digestive tract so that the absorption of nutrient could be optimal and no mortality on experiment. The supplementation tecton leaf can improve balance the necessary of feed nutrient quails and increase productivity of egg quail. This study aimed to analyze supplementation meal and extract tectone leaf in quails diet on antimicrobial activities such as bacteria pathogen E.coli and S.typhimurium on digestive tract so it could improve the quail performance and egg quality. This research used the laying Japanese quail (Coturnix coturnix japonica) aged 4-12 weeks as many as 180 bird given the supplementation tecton meal in diet and extract tecton leaf in drinking water. A method of research is experiment with design random complete (CRD) consist of six treatments and three replications. Treatments consist of : R0 = commercial rations, R1 = commercial rations + Vitachick, R2 = commercial rations + 6% meal tecton leaf, R3 = commercial rations +6% extract tecton leaf, R4= commercial rations+ 9% meal tecton leaf, R5 = commercial rations + 9% esxtract tecton leaf in drinking water. Parameters observed were feed consumption, water consumption, egg production, egg weight, egg mass, feed conversion, yolk weight and albumin weight, egg yolk colour score, eggshell weight, eggshell thickness, haugh unit, total of colonies bacteria, and analysis of Income Overfeed Cost. The analysis showed that meal tecton leaf proximate containing crude protein 11.01%, crude fat 3.80%, crude fiber 22.01%, and ash 8.11%. Active compound of flavonoid that was found in extract and meal tecton leaf is 0.241% and 0.643%. The result showed that the addition of meal and extract tecton leaf didn’t affect significantly (P>0.05 ) of feed consumption, water consumption, egg production and feed conversion. There was no mortality during this experiment. The addition of meal tecton leaf 6% and 9% could be increase a score of yolk colour to 8 with a yellow color. Supplementation extract tectone leaf 9% to reduce the total bacteria colonies E.coli and S. typhimurium. Flavonoids were able to inhibit the growth of pathogenic bacteria with the concentration of the medium. The conclusion of this research was supplementation 6% tecton leaf was able to increase the egg yolk color score while was able to reduce the total population of the colonies of pathogenic bacteria of the digestive tract quail can be given the addition of 9% extract tecton leaf. The content of flavonoid that serves as the meal contained in the antibacterial meal and extract tecton leaf is 0.643% and 0.24%.
Keyword: antibacterial, flavonoid, productivity, quail, Egg, tecton leaf

Judul: Model Deteksi Botnet pada Trafik Terenkripsi dalam Jaringan Software-Defined Network (SDN) menggunakan Deep Neural Network (DNN).
Abstrak: Konfigurasi perangkat jaringan baru yang dilakukan terpusat dan tanpa bergantung kepada vendor perangkat merupakan salah satu konsep yang ditawarkan jaringan Software-Defined Network (SDN). Konsep SDN sangat efektif diterapkan pada jaringan yang memiliki perangkat sangat banyak atau jaringan dengan infrastruktur besar dan luas. Kehadiran jaringan SDN membuat pengelola jaringan perlahan-lahan mulai meninggalkan jaringan konvensional. Perbedaan jaringan SDN dengan jaringan konvensional terletak pada arsitektur jaringannya. Arsitektur jaringan SDN membagi perangkat jaringannya kedalam tiga bagian utama yaitu data plane, control plane dan application plane, sedangkan didalam arsitektur jaringan konvensioanl, tiga bagian utama dalam jaringan SDN digabung menjadi satu dalam sebuah perangkat jaringan seperti router dan switch, yang dalam arsitektur jaringan SDN dikenal sebagai data plane. Di dalam jaringan SDN, aktivitas seperti konfigurasi perangkat jaringan baru, controling dan monitoring jaringan dilakukan terpusat pada bagian yang dikenal dengan control plane. Bagian control plane juga mengatur keluar masuk semua aliran lalu lintas dalam jaringan SDN. Fungsi dan peran control plane dalam jaringan SDN sangatlah penting. Untuk itu, perlu adanya sebuah sistem keamanan yang kuat untuk melindungi control plane dalam jaringan SDN dari serangan. Jika tidak, ketika terjadi serangan pada bagian control plane dalam jaringan SDN maka, bisa dipastikan semua sumberdaya jaringan dengan cepat bisa diambil alih dan dikuasasi seluruhnya oleh penyerang. Untuk mengatasi hal tersebut, jaringan SDN menyediakan sistem keamanan yang dikenal dengan protocol OpenFlow. Protokol keamanan tersebut berfungsi mengarahkan setiap aliran lalu lintas paket yang lewat sesuai tujuannya. Protocol OpenFlow dalam jaringan SDN menghubungkan bagian control palane dan data plane, sehingga setiap aliran yang dilewatkan protocol OpenFlow sesuai tujuannya diatur pada bagian control plane. Beberapa penelitian terdahulu menjelaskan bahwa sistem keamanan protocol OpenFlow belum mampu melakukan identifikasi terhadap trafik terenkripsi dengan baik. Protocol TLS/HTTP merupakan salah satu trafik terenkripsi yang mampu melewati sistem keamanan protocol OpenFlow. Hal tersebut merupakan masalah yang perlu diperbahrui oleh jaringan SDN. Sebab, jika ada serangan menggunakan trafik terenkripsi dan berhasil menyerang maka, akibatnya penyerang akan dengan cepat dan mudah menguasai jaringan SDN. Dewasa ini banyak sekali serangan dalam jaringan yang memanfaatkan trafik terenkripsi untuk menyerang sistem keamanan dari sebuah jaringan. Botnet adalah salah satu serangan dalam dunia jaringan yang menggunakan trafik terenkripsi sebagai celah untuk melancarkan serangannya. Serangan botnet merupakan kumpulan aplikasi jahat seperti DDoS, fast-flux, click fraud, port scan, dan spam. Kumpulan aplikasi tersebut didesain oleh pembuatnya (botmaster / botheader) untuk menyerang jaringan, dengan tujuan melemahkan performa sebuah jaringan atau mengambil informasi penting (userid dan password) dalam jaringan secara illegal. Kurang lebih 16% sampai 25% komputer yang terhubung dengan internet terinfeksi botnet. Hal ini tentunya sangat mengkhawatirkan bagi pengelola jaringan, sehingga penelitian ini bertujuan membangun sebuah model sistem kamanan yang baik, untuk membantu protocol OpenFlow melakukan pengamanan terhadap lalu lintas trafik dalam jaringan SDN. Model yang dibangun diharapkan mampu melakukan deteksi terhadap serangan dalam jaringan SDN, terutama pada serangan botnet yang menggunakan trafik terenkripsi. Model keamanan yang dibangun pada penelitian ini, menggunakan pendekatan Deep Neural Network (DNN) dan function stochastic Adptive momentum (Adam) untuk optimasi model. DNN merupakan metode Machine Learning (ML) yang dikenal dengan Deep Learning (DL) dan merupakan pengembangan dari Neural Network (NN). Penelitian ini memanfaatkan salah satu framework
Keyword: Botnet, Deep Learning, Deep Neural Network, Protokol enkripsi, Software-Defined Network