Analisa Probabilistik Algoritma Routing pada Jaringan Hypercube

Algoritma routing pada suatu jaringan interkoneksi adalah suatu mekanisme untuk menentukan rute yang harus dilalui oleh suatu packet yang berasal dari suatu node sumber ke node destinasi pada jaringan tersebut

Kombinasi Boolean Automata Buchi

Suatu word tak hingga w dikenal (recognized) oleh automata A jilu word tersebut merupakan label dari suatu lintasan sukses. Lintasan sukses dari suatu autornata adalah lintasan yang dimulai dari suatu state inisial dan melintasi state final tak hingga kali. Himpunan sernua word yang diterima oleh, dinotosikan dengan L(AI. Jil(a A, A'1 dan 42 masing-masing merupakan automata Buchi unambigu dan lengkap, dimana X=L(A}-, Xt=L(A)dan Xz = L(Azl . Jika digunakan metode balat yang terdapat di (Eilenberg,l974) , dalam pembentukan automatayang dapat mengenal himpunan XC, X.tlX2dan X1fiX2, automatayang terbentuk tersebut belum tentu merupakan automata unambigu dan lengkap. Maknlah ini akan membahas cara membentuk autotndta-automata Buchi unambigu dan lengkap, yang dapat menerima himpunan word Xc, XllJX2dan X1fiXz

The comparison study of kernel KC-means and support vector machines for classifying schizophrenia

Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent

Prediction schizophrenia using random forest

Schizophrenia is a mental illness with a very bad impact on sufferers, attacking the part of human brain that disables the ability to think clearly. In 2018, Rustam and Rampisela classified Schizophrenia by using Northwestern University Schizophrenia Data, based on 66 variables consisting of group, demographic, and questionnaires statistics, based on the scale for the assessment of negative symptoms (SANS), and scale for the assessment of positive symptoms (SAS), and then classifiers that used are SVM with Gaussian kernel and Twin SVM with linear and Gaussian kernel. Furthermore, this research is novel based on the use of random forest as a classifier, in order to predict Schizophrenia. The result obtained is reported in percentage of accuracy, both in training and testing of random forest, which was 100%. This classification, therefore, shows the best value in contrast with prior methods, even though only 40% of training data set was used. This is very important, especially in the cases of rare disease, including schizophrenia

Comparing random forest and support vector machines for breast cancer classification

There are more than 100 types of cancer around the world with different symptoms and difficulty in predicting itsappearance in a person due to its random and sudden attack method. However, the appearance of cancer is generally marked by the growth of some abnormal cell. Someone might be diagnosed early and quickly treated, but the cancerous cell most times hides in the body of its victim and reappear, only to kill its sufferer. One of the most common cancers is breast cancer. According to Ministry of Health, in 2018, breast cancer attacked 42 out of every 100.000 people in Indonesia with approximately 17 deaths. In addition, the Ministry recorded a yearly increase in cancer patients. Therefore, there is adequate need to be able to determine those affected by this disease. This study applied the Boruta feature selection to determine the most important features in making a machine learning model. Furthermore, the Random Forest (RF) and Support Vector Machines (SVM) were the machine learning model used, with highest accuracies of 90% and 95% respectively. From the results obtained, the SVM is a better model than random forest in terms of accuracy

Aplikasi Metode Fuzzy Kernel K-Medoids untuk Klasifikasi Kanker berdasarkan Konsentrasi Logam di dalam Darah

Classification technique has already been applied widely in the medical data. One of its applications is for classification of cancer. The accuracy of this technique highly depends on the type of data to be processed (whether the data are separable or non-separable) and the dissimilarity function used. To surmount those hindrances and to improve the accuracy of classification therefore a method named Fuzzy Kernel K-Medoids (FKKM). The method can be used for separable or non separable of data. Based on the research on the concentration data of Zn, Ba, Mg, Ca, Cu, and Se in blood in order to diagnose cancer, FKKM gives better result than the Support Vector Machines Method. This paper will discuss an application of the FKKM method on the concentration data of Zn, Ba, Mg, Ca, Cu, and Se in blood samples and compared with the Support Vector Machines Method for the diagnosis of cancer. Results showed that the FKKM method produced a better result than the Support Vector Machines Method.Teknik klasifikasi telah diaplikasikan secara luas didalam bidang medis. Salah satunya adalah untuk klasifikasi kanker. Akurasi teknik ini sangat tinggi tergantung pada tipe data yang diproses (apakah data dapat atau tidak dapat dipisahkan secara linear) dan fungsi disimiliritas yang digunakan. Untuk mengatasi kedua hambatan tersebut dan meningkatkan akurasi teknik klasifikasi dibentuk suatu metode yang dinamakan Fuzzy Kernel K-Medoids (FKKM). Metode ini dapat digunakan untuk data yang dapat dipisahkansecara linear maupun tidak. Berdasarkan hasil penelitian terhadap konsentrasi logam Zn, Ba, Mg, Ca, Cu, dan Se dalam darah, didalam mendiagnosis penyakit kanker, FKKM memberikan hasil yang lebih baik dibandingkan dengan metode Support Vector Machines

Implementasi Algoritma Enkripsi Citra Digital Menggunakan Skema Tranposisi Berbasis Fungsi Chaos

Algoritma enkripsi citra digital yang dikembangkan dalam paper ini ditujukansebagai alternatif dalam mengamankan informasi citra tersebut. Lenaha yangdilakukan adalah dengan menggunakan skema transposisi yang berbasis fungsichaos, yaitu fungsi Arnold\u27s cat map. Fungsi tersebut berfungsi sebagai bentuktransposisi atau pertukaran posisi dari informasi data aslinya. Akan ditetapkanskema transposisi tertentu untuk mengacak informasi asli sehingga sulit untukdibaca kembali oleh pihak ketiga. Selanjutnya dilakukan pengujian secarapraktis. Pengujian dilakukan untuk beragam data berupa citra digital denganberbagai ukuran. Hasil analisis pengujian secara praktis menunjukkan bahwaruang kunci yang dihasilkan sangat jauh lebih besar dan tingkat sensitivitasnyasangat jauh lebih kecil