The benefits of a computer network are to be able to exchange information and facilitate the work. Information sent from one network to another cannot be separated from the use of the router. Determination of network address can be done by routing method. Routing on many routers is easier and more efficient when using dynamic routing like EIGRP, OSPF, and RIP. Different route settings become a problem in data transmission. The purpose of this study is to explain the method of routing data transmission route routing. The routing redistribution method is chosen as one way to connect the routing method by combining the method using IPv6. Based on the results of the tests conducted on redistribution routing by looking at the length of time the data packet delivery with ICMP then obtained EIGRP redistribution routing to OSPF with an average value of 3.478 seconds and OSPF redistribution to RIP with an average of 3.486 seconds, and EIGRP redistribution to RIP with average 3,514 seconds. While the value of ICMP data transmission the longest by using OSPF redistribution to EIGRP with an average of 4.976 seconds, RIP redistribution to EIGRP with an average of 4.616 seconds and RIP redistribution to OSPF with an average of 4.462 seconds.

Edu-tourism is the one of the most popular sub-type of tourism these days,where many countries in the world use Edu-tourism as one of the main sources of income. However, the lack of promotion with such a good planning and good infrastructure makes Edu-tourism on farms less attractive. In this research we try to build a plan to develop an information systems architecture in the Pondok Rangon ranch area to analyze operational activities on the ranch, designing information system development architecture using TOGAF ADM, and make a system information enterprise architecture design model that can be used as one of the facility to optimize the development of the promotion of the educational tourism industry on livestock especially the Pondok Rangon area. This research is only limited to the preliminary phase up to the technology phase so that the next research is expected to meet all the TOGAF ADM phases.

Kanker payudara adalah kanker paling umum yang menyerang wanita di seluruh dunia. Machine Learning telah banyak digunakan untuk membantu dalam mendukung keputusan para ahli kesehatan dalam memprediksi penyakit kanker payudara . Algoritma K-NN digunakan pada penelitian ini untuk mengklasifikasi dataset kanker payudara Coimbra dan forward selection diimplementasikan untuk menghindari sensitivitas K-NN terhadap attribute yang tidak relevan dan berkorelasi, sehingga kinerja K-NN dapat lebih maksimal. Tujuan penelitian ini adalah untuk membandingkan kinerja dari kombinasi K-NN dan forward selection dengan algoritma machine learning lainnya, sekaligus meningkatkan kinerja K-NN dalam mengklasifikasi dataset kanker payudara. Hasil menunjukan akurasi tertinggi diperoleh dari kombinasi KNN+FS sebesar 91,43% dengan 5 atribut terpilih dari 9 atribut independen. Bahkan KNN+FS juga mengungguli algoritma machine learning lainnya, juga unggul jika dibandingkan penelitian terdahulu. Forward selection dan proporsi data yang tepat sangat mempengarui kinerja K-NN dan machine learning lainnya.

Color blindness is one of the decreasing diseases which is very difficult to determine its deterioration, whether a family member will suffer from color blindness or not, especially with the prediction of color blindness which has been using manual methods by calculating the inheritance formula. Genetic Algorithms have advantages over other traditional optimization algorithms. To implement a computerized method in predicting color blindness that can be used by many people, it is necessary to have a user-friendly operating system like the Android operating system. The test results show that the implementation of genetic algorithms in applications to predict color blindness produces more optimal predictions and their application to Android makes the application a user friendly application. Keywords: Heredity, Color Blindness, Genetic Algorithms, Android Application, Color Blindness Predictions

Kanker payudara merupakan masalah kesehatan yang serius, sehingga deteksi dini dari kanker payudara dapat berperan penting dalam perencanaan pengobatan. Pada penelitian ini Support Vector Machine dengan kernel (dot, polynomial, RBF) dan forward selection diterapkan. Perbandingan akurasi SVM tanpa forward selection dengan menggunakan forward selection menunjukkan selisih yang besar. Hasil penelitian menunjukkan SVM(RBF)+FS unggul dengan akurasi 85,38% dibandingkan dengan SVM(Polynomial & dot), selain itu SVM(RBF)+FS juga unggul dibandingkan algoritma machine learning lainnya dalam memprediksi dataset kanker payudara Coimbra.