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"Kematian yang disebabkan oleh kanker diperkirakan akan terus meningkat, terutama untuk kanker prostat. Penyakit ini adalah jenis kanker yang paling umum untuk pria di dunia. Jumlah kematian dapat dikurangi dengan deteksi dini menggunakan machine learning. Salah satunya adalah klasifikasi data kanker prostat. Data kanker yang digunakan memiliki berbagai fitur, tetapi tidak semua fitur adalah fitur penting. Dalam penelitian ini, kami menggunakan Support Vector Machine-Recursive Feature Elimination (SVM-RFE) dan One Dimensional Naïve Bayes Classifier (1-DBC) sebagai metode seleksi fitur. Dalam kedua metode itu akan mendapatkan peringkat untuk setiap fitur. Penggunaan kedua metode ini dalam klasifikasi data kanker prostat menghasilkan tingkat evaluasi yang tinggi. Kedua metode ini dapat menghasilkan tingkat akurasi 100%, precision 100%, dan recall 100% pada metode klasifikasi Random Forest. Dan menghasilkan tingkat akurasi 95%, precision 100%, dan recall 94,11% pada metode klasifikasi SVM. Dalam evaluasi tambahan, SVM-RFE memiliki running time lebih rendah dari 1-DBC.

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Death caused by cancer is expected to continue to increase, especially for prostate cancer. This disease is the most common type of cancer for men in the world. The number of deaths can be reduced by early detection using machine learning. One of them is the classification of prostate cancer data. Cancer data used has various features, but not all features are essential features. In this study, we use Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and One Dimensional Naïve Bayes Classifier (1-DBC) as a feature selection method. In both methods, it will get a rating for each feature. The use of these two methods in the classification of prostate cancer data produces a high level of evaluation. Both of these methods can produce 100% accuracy, 100% precision, and 100% recall in the Random Forest classification method. And it produces 95% accuracy, 100% precision, and 94.11% recall in the SVM classification method. In the additional evaluation, SVM-RFE has a running time lower than 1-DBC."

"[ABSTRAKVirus dengue terdiri atas 10 protein penyusun yang berbeda dan diklasifikasikanmenjadi empat serotipe utama (DEN 1 ? DEN 4). Penelitian ini dirancang untukmelakukan pengelompokan terhadap 30 sekuens protein virus dengue yangdiambil dari Virus Pathogen Database and Analysis Resource (ViPR)menggunakan metode Regularized Markov Clustering (R?MCL) dan untukmenganalisis hasilnya. Dengan menggunakan program Python 3.4, algoritmaR-MCL diimplementasikan dan menghasilkan 8 kelompok dengan pusatkelompok lebih dari satu di beberapa kelompok. Banyaknya pusat kelompokmenunjukkan tingkat kepadatan interaksi. Interaksi protein ? protein yangterhubung padat dalam jaringan cenderung membentuk kompleks protein yangberfungsi sebagai unit proses biologi tertentu. Hasil analisis menunjukkan hasilpengelompokan dengan R-MCL menghasilkan kelompok ? kelompokkekerabatan virus dengue berdasarkan peran yang sama dari protein penyusunnya,tanpa memperhatikan serotipenya.

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ABSTRACTDengue virus consists 10 different constituent proteins and are classified into fourmajor serotypes (DEN 1 - DEN 4). This study was designed to perform clusteringagainst 30 protein sequences of dengue virus taken from Virus Pathogen Databaseand Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL)algorithm and tp analyze the result. By using Python program 3.4, R-MCLalgorithm produces 8 clusters with more than one centroid in several clusters. Thenumber of centroid shows the density level of interaction. The density ofinteractions protein - protein connected in a network tend to form a proteincomplex that serves as the unit of specific biological processes. The analyzingresult shows the R-MCL clustering produces clusters of dengue virus familybased on the similirity role of their constituent protein, regardless serotypes;Dengue virus consists 10 different constituent proteins and are classified into fourmajor serotypes (DEN 1 - DEN 4). This study was designed to perform clusteringagainst 30 protein sequences of dengue virus taken from Virus Pathogen Databaseand Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL)algorithm and tp analyze the result. By using Python program 3.4, R-MCLalgorithm produces 8 clusters with more than one centroid in several clusters. Thenumber of centroid shows the density level of interaction. The density ofinteractions protein - protein connected in a network tend to form a proteincomplex that serves as the unit of specific biological processes. The analyzingresult shows the R-MCL clustering produces clusters of dengue virus familybased on the similirity role of their constituent protein, regardless serotypes;Dengue virus consists 10 different constituent proteins and are classified into fourmajor serotypes (DEN 1 - DEN 4). This study was designed to perform clusteringagainst 30 protein sequences of dengue virus taken from Virus Pathogen Databaseand Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL)algorithm and tp analyze the result. By using Python program 3.4, R-MCLalgorithm produces 8 clusters with more than one centroid in several clusters. Thenumber of centroid shows the density level of interaction. The density ofinteractions protein - protein connected in a network tend to form a proteincomplex that serves as the unit of specific biological processes. The analyzingresult shows the R-MCL clustering produces clusters of dengue virus familybased on the similirity role of their constituent protein, regardless serotypes, Dengue virus consists 10 different constituent proteins and are classified into fourmajor serotypes (DEN 1 - DEN 4). This study was designed to perform clusteringagainst 30 protein sequences of dengue virus taken from Virus Pathogen Databaseand Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL)algorithm and tp analyze the result. By using Python program 3.4, R-MCLalgorithm produces 8 clusters with more than one centroid in several clusters. Thenumber of centroid shows the density level of interaction. The density ofinteractions protein - protein connected in a network tend to form a proteincomplex that serves as the unit of specific biological processes. The analyzingresult shows the R-MCL clustering produces clusters of dengue virus familybased on the similirity role of their constituent protein, regardless serotypes]"

"[ABSTRAKHierarchical clustering merupakan metode yang efektif dalam membentuk pohonfilogenetik dengan mengetahui matriks jarak antar barisan DNA. Salah satu carauntuk membuat matriks jarak yaitu dengan cara menggunakan metode -mer.Kelebihan dari metode -mer yaitu lebih efisien dalam segi waktu. Langkahlangkahdalam membuat matriks jarak dengan metode -mer dimulai denganmembentuk -mer sparse matrix dari masing barisan DNA. Selanjutnya,membentuk -mer singular value vector. Pada tahap akhir yaitu menghitung jarakantar vektor. Pada tesis ini akan dilakukan analisis terhadap barisan DNA MERSCoVdengan mengimplementasi Hierarchical clustering menggunakan -merssparse matrix sehingga dapat diketahui leluhur dari masing-masing barisan DNAMERS-CoV.

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ABSTRACTHierarchical clustering is an effective method in creating phylogenetic byknowing the distance matrix between DNA sequence. One of methods to make thedistance matrix use -mer method. -mer is more efficient than others. The stepsto make distance matrix using -mer method starts from creating -mer sparsematrix. Then, creating -mer singular value vector. The last steps is countingdistance each vectors. This thesis will analyze the sequence of DNA MERS-CoVby implementing Hierarchical clustering using k-mers sparse matrix so that willbe known the ancestor of each sequence of DNA MERS-CoV., Hierarchical clustering is an effective method in creating phylogenetic byknowing the distance matrix between DNA sequence. One of methods to make thedistance matrix use -mer method. -mer is more efficient than others. The stepsto make distance matrix using -mer method starts from creating -mer sparsematrix. Then, creating -mer singular value vector. The last steps is countingdistance each vectors. This thesis will analyze the sequence of DNA MERS-CoVby implementing Hierarchical clustering using k-mers sparse matrix so that willbe known the ancestor of each sequence of DNA MERS-CoV.]"

"[Salah satu tujuan dalam studi ekpresi gen (DNA/Protein) adalah menemukan subbagianyang penting secara biologis dan kelompok-kelompok dari gen-gen. Pengelompokan gen tersebut dapat dilakukan dengan metode hirarki maupun metode partisi. Kedua metode pengelompokan dapat dikombinasikan, dimanadilakukan fase partisi dan hirarki secara bergantian, metode ini dikenal dengan metode Hopach. Tahap partisi dapat dilakukan dengan metode PAM, SOM, atau K-Means. Proses partisi dilanjutkan dengan proses Ordered, baru kemudian dikoreksi dengan proses agglomorative, sehingga hasil pengelompokan menjadi lebih akurat. Dalam menentukan kelompok utama digunakan ukuran MSS (Median Split Silhouette). MSS mengukur homogenitas hasil pengelompokan,dimana hasil pengelompokan yang dipilih adalah yang meminimumkan MSS. Pada pengelompokan 136 barisan DNA Virus Ebola dari GeneBank. Prosesawalnya dilakukan pensejajaran global, dan dilanjutkan dengan perhitungan jarak genetik dengan menggunakan koreksi Jukes-Cantor. Pada penelitian ini didapat jarak genetik maksimum adalah 0.6153407 sedangkan jarak genetik minimum adalah 0. Selanjutnya matriks jarak genetik dapat dijadikan dasar untuk mengelompokkan barisan-barisan tersebut dengan menggunakan metode Hopach. Pada hasil pengelompokan Hopach-PAM, diperoleh kelompok utama sebanyak 10 kelompok dengan nilai MSS sebesar 0,8873843. Kelompok-kelompok virus ebola dapat diidentifikasikan berdasarkan subspesies dan tahun pertama kali mewabah.Proses pensejajaran global dan pengelompokan Hopach-PAM menggunakan bantuan program open source R.

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One goal in the study of gene expression (DNA/Protein) is finding biologically important subsets and clusters of genes. Clustering these genes can be achieved by hierarchical and partitioning methods. Both clustering methods can be combined, where partition and hierarchy phases can be executed alternately, this method is known as a Hopach method. The partitioning step can be done by the PAM, SOM, or K-Means clustering method. The partition process continued with the process of Ordered, then corrected with agglomorative process, so that the clustminering results become more accurate. The main clusters determine by using MSS

(Median Split Silhouette). MSS is used to measure homogeneity of the clustering result, in which the clustering is selected to minimize its MSS. The clustering procceses of 136 DNA sequences of Ebola virus, are started by performing a global alignment, and continued with the genetic distance calculations using

Jukes-Cantor correction. In this research we found the maximum genetic distance is 0.6153407, meanwhile the minimum genetic distance is 0. Furthermore, the genetic distance matrix can be used as a basis for clustering sequences in Hopach-PAM clustering method. Based on, the clustering results, we obtained 10 major clusters with MSS value of 0.8873843. Ebola virus clusters can be identified by subspecies and the first occoring year of their outbreak. We implemented the global alignment process and Hopach-PAM clustering algorithm using the open source program R.;One goal in the study of gene expression (DNA/Protein) is finding biologically important subsets and clusters of genes. Clustering these genes can be achieved by hierarchical and partitioning methods. Both clustering methods can be combined, where partition and hierarchy phases can be executed alternately, this method is known as a Hopach method. The partitioning step can be done by the PAM, SOM, K-Means clustering method. The partition process continued with the process

of Ordered, then corrected with agglomorative process, so that the clustmineringresults become more accurate. The main clusters determine by using MSS (Median Split Silhouette). MSS is used to measure homogeneity of the clustering result, in which the clustering is selected to minimize its MSS. The clustering procceses of 136 DNA sequences of Ebola virus, are started by performing a global alignment, and continued with the genetic distance calculations using Jukes-Cantor correction. In this research we found the maximum genetic distance is 0.6153407, meanwhile the minimum genetic distance is 0. Furthermore, the genetic distance matrix can be used as a basis for clustering sequences in Hopach-PAM clustering method. Based on, the clustering results, we obtained 10 major clusters with MSS value of 0.8873843. Ebola virus clusters can be identified by subspecies and the first occoring year of their outbreak. We implemented the global alignment process and Hopach-PAM clustering algorithm using the open

source program R., One goal in the study of gene expression (DNA/Protein) is finding biologically

important subsets and clusters of genes. Clustering these genes can be achieved by

hierarchical and partitioning methods. Both clustering methods can be combined,

where partition and hierarchy phases can be executed alternately, this method is

known as a Hopach method. The partitioning step can be done by the PAM, SOM,

or K-Means clustering method. The partition process continued with the process

of Ordered, then corrected with agglomorative process, so that the clustminering

results become more accurate. The main clusters determine by using MSS

(Median Split Silhouette). MSS is used to measure homogeneity of the clustering

result, in which the clustering is selected to minimize its MSS. The clustering

procceses of 136 DNA sequences of Ebola virus, are started by performing a

global alignment, and continued with the genetic distance calculations using

Jukes-Cantor correction. In this research we found the maximum genetic distance

is 0.6153407, meanwhile the minimum genetic distance is 0. Furthermore, the

genetic distance matrix can be used as a basis for clustering sequences in Hopach-

PAM clustering method. Based on, the clustering results, we obtained 10 major

clusters with MSS value of 0.8873843. Ebola virus clusters can be identified by

subspecies and the first occoring year of their outbreak. We implemented the

global alignment process and Hopach-PAM clustering algorithm using the open

source program R.]"

"Salah satu metode clustering yang banyak digunakan karena unggul dari sisi kestabilannya adalah metode Self Organizing Map. Pada tesis ini dibahas penggunaan metode SOM pada DNA Human Papillomavirus (HPV) yang menjadi penyebab utama penyakit kanker serviks, yaitu penyakit kanker yang menempati urutan pertama di negara berkembang. DNA HPV yang digunakan adalah sebanyak 18 buah yang diambil berdasarkan complete genome terbaru. Dengan menggunakan program berbasis opensource R, proses clustering berhasil mengelompokkan 18 tipe HPV ke dalam dua buah cluster berbeda, yang terdiri dari 2 tipe HPV di cluster pertama sementara 16 tipe HPV lainnya di cluster ke dua. Hasil analisis 18 tipe HPV adalah berdasarkan tingkat keganasannya, atau tingkat kesulitan dalam penyembuhannya. Dua di antara tipe HPV yang berada di cluster pertama tergolong jenis HPV jinak, sementara 16 tipe HPV yang berada di cluster ke dua tergolong jenis HPV ganas.

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One of the most widely used clustering method, since it has advantage on its robustness is Self Organizing Map (SOM) method. This thesis discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is a main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The Analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV."

"[ABSTRAKBerdasarkan data World Health Organization (WHO) diketahui prevalensi herpesdi negara-negara berkembang lebih tinggi dibandingkan dengan di negara maju.Virus herpes dapat ditemukan dimana saja dan salah satu ciri penting adalahkemampuannya yang dapat menimbulkan infeksi akut dan kronik pada waktuwaktutertentu. Akibat infeksi tersebut memungkinkan terjadi komplikasi yanglebih berat. Virus herpes terdiri atas genome DNA tertutup inti yang mengandungprotein dan dibungkus oleh glikoprotein. Dengan mempelajari ekspresi gen(sekuen DNA/protein) dan didukung oleh kemajuan di bidang bioinformatika,dapat ditemukan sub-sub bagian penting dan kelompok gen. Virus-virus ini dapatdikelompokkan dengan menganalisa sekuens protein dari virus herpes denganmenggunakan algoritma Tribe Markov Clustering (Tribe-MCL). Tribe-MCLmerupakan metode clustering efisien berdasarkan teori rantai Markov chain,untuk mengelompokkan barisan keluarga protein. Data sekuens protein virusherpes diperoleh di GenBank yang dapat diakses pada situs National Center forBiotechnology Information (NCBI), kemudian disejajarkan menggunakanprogram BLASTp. Hasil pengelompokan sekuen protein virus herpesmenggunakan algoritma Tribe-MCL dengan program R diperoleh enamkelompok . Semua kelompok menunjukkan jenis protein yang sama, dalam halini jenis protein yang digunakan adalah glikoprotein B, M, dan H pada delapanjenis virus herpes yang terjangkit pada manusia.

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ABSTRACTBased on World Health Organization (WHO) data, the prevalence of herpes indeveloping countries is higher than in developed countries. The herpes virus canbe found anywhere and one of the important characteristics is its ability to causeacute and chronic infection at certain times. Due to infections enables moresevere complications occur. The herpes virus is composed of DNA containingprotein and wrapped by glycoproteins. By studying the expression of genes(sequences of DNA / protein) and is supported by advances in bioinformatics, canbe found an important sub-sections and groups of genes. These viruses can beclassified by analyzing the sequence of the protein-sequence of the herpes virususing algorithm Tribe Markov Clustering (Tribe-MCL). Tribe-MCL is an efficientclustering method based on the theory of Markov chains, to classify sequences ofprotein families. Herpes virus protein sequence data obtained in GenBank whichcan be accessed on the website National Center for Biotechnology Information(NCBI), then aligned using BLASTp program. The results of clustering proteinsequences herpes virus using algorithms (Tribe-MCL) with a program of Robtained six cluster. All clusters showed the same type of protein, in this case thetype of protein used is a glycoprotein B, F, and H in eight types of herpes virusthat infected humans, Based on World Health Organization (WHO) data, the prevalence of herpes indeveloping countries is higher than in developed countries. The herpes virus canbe found anywhere and one of the important characteristics is its ability to causeacute and chronic infection at certain times. Due to infections enables moresevere complications occur. The herpes virus is composed of DNA containingprotein and wrapped by glycoproteins. By studying the expression of genes(sequences of DNA / protein) and is supported by advances in bioinformatics, canbe found an important sub-sections and groups of genes. These viruses can beclassified by analyzing the sequence of the protein-sequence of the herpes virususing algorithm Tribe Markov Clustering (Tribe-MCL). Tribe-MCL is an efficientclustering method based on the theory of Markov chains, to classify sequences ofprotein families. Herpes virus protein sequence data obtained in GenBank whichcan be accessed on the website National Center for Biotechnology Information(NCBI), then aligned using BLASTp program. The results of clustering proteinsequences herpes virus using algorithms (Tribe-MCL) with a program of Robtained six cluster. All clusters showed the same type of protein, in this case thetype of protein used is a glycoprotein B, F, and H in eight types of herpes virusthat infected humans]"

"Penelitian ini bertujuan untuk mencari korelasi yang kuat antar gen dan kondisi dari data ekspresi gen penyakit Diabetes Melitus (DM) pada sampel obesitas dan sampel kurus dengan menggunakan metode three phase biclustering. Tahap pertama pada metode ini adalah dengan menggunakan matriks dekomposisi Singular Value Decomposition (SVD) yang mentransformasikan data menjadi dua matriks berbasis gen dan kondisi. Selanjutnya pada tahap kedua menggunakan metode partisi Partition Around Medoids (PAM) pada dua matriks gen dan kondisi menggunakan jarak Euclidean sehingga jika digabung akan membentuk bicluster yang pada tahap tiga akan dievaluasi dengan menggunakan modifikasi lift algorithm berbasiskan korelasi Pearson yang cocok untuk mendeteksi bicluster model additive-multiplicative. Hasil dari implementasi algoritma yang digunakan pada dataset microarray dinamakan δ-corbicluster yang memiliki korelasi yang tinggi antar gen dan sampel. Implementasi dari tahap pertama dan kedua (SVDPAM) pada dataset DM dengan 1331 gen terseleksi menghasilkan 8 bicluster. Sedangkan hasil tahap ketiga yaitu modifikasi algoritma lift pada kedelapan bicluster ini menghasilkan 3 δ-corbicluster dengan masing-masing nilai korelasi yang tinggi yaitu 0,097, 0,095, 0,085, sehingga metode yang diusulkan dan hasil analisisnya pada gen dan sampel penyakit DM memiliki potensi besar ke depannya dalam penelitan pada bidang medis.

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The purpose of this research is to find strong correlation among genes and conditions of Diabetes Melitus genes expression data which samples are obese and lean people using three phase biclustering. First step is to use matrix decomposition Singular Value Decomposition (SVD) to decompose matrix gene expression data into two global based gene and condition matrices. Second step is to use partition method Partition Around Medoid (PAM) to cluster gene and condition based matrices using Euclidean distance, forming several biclusters which further evaluated using modified lift algorithm based on Pearson correlation which is very appropriate method to detect additive-multiplicative bicluster type. The resulting bicluster of the proposed algorithm having strong correlation among genes and samples to microarray dataset are called δ-corbicluster. Implementation of the first and second step (SVD-PAM) to dataset DM with 1331 selected genes produces 8 biclusters. For the third step using modified lift algorithm to these 8 biclusters produces 3 δ-corbiclusters having strong correlation values: 0,097, 0,0095, 0,085, so that the proposed method and the result of analysis to genes and samples of DM have high potential in future medical researches."

"ABSTRAKHuman Immunodeficiency Virus (virus HIV) adalah virus penyebab penyakit Acquired Immunodeficiency Syndrome (AIDS). Virus HIV merupakan retro virus yang menginfeksi sel-sel sistem kekebalan tubuh manusia. Virus HIV dalam proses menginfeksi tubuh manusia tidak dapat lepas dari terjadinya interaksi antara protein HIV dan protein manusia. Untuk menganalisis jaringan interaksi antar protein dapat menggunakan barisan asam amino penyusun dari suatu protein. Proses analisis dapat dilakukan dengan metode konvensional maupun komputasi. Keunggulan menggunakan metode komputasi yaitu dapat menghemat waktu maupun biaya. Metode rotation forest merupakan sebuah metode ensemble untuk pemprediksian. Dalam pemprediksian interakasi protein menggunakan barisan asam amino rotation forest akan dikombinasikan dengan fitur ekstraksi dalam penelitian ini menggunakna PseudoSMR. Data untuk membuat model prediksi berupa interaksi protein beserta barisan asam amino. Data tersebut dapat diperoleh dari NCBI (National Centre for Biotechnology Information). Fitur ekstraksi menggunakan PseudoSMR akan merubah barisan asam amio berupa barisan alphabet menjadi sebuah vektor ciri. Di dalam fitur ekstraksi PsedoSMR terdapat variabel yang membuat matriks hasil dari fitur ekstraksi PseudoSMR mempunyai ukuran yang bereda. Matriks hasil ektraksi ciri akan dijadikan sebagain data training dan data testing dalam pembuatan model rotation forest, sehingga total data set yang digunakan ada sebanyak 6 data set. Hasil prediksi rotation forest untuk RF(PCA) yang paling bagus pada data dan untuk RF(IPCA) yang paling bagus pada data . Nilai evaluasi performa pada kisaran 0.759436 sampai 0.793178 untuk RF(IPCA) sedangkan untuk RF(PCA) pada kisaran 0.774837 sampai 0.812225. Nilai evaluasi tertinggi didapat pada saat variabel dimana merupakan panjang kolom. Semua perhitungan dan proses membuat model prediksi menggunakan software R.

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ABSTRACTHuman Immunodeficiency Virus (HIV virus) is a virus that causes the disease Acquired Immunodeficiency Syndrome (AIDS) which is a retro virus that infects cells of the human immune system. In a process of infecting the human body, HIV virus can not be separated from the interaction between proteins HIV and human. To analyze the interaction tissue between proteins, we can use sequence of amino acids from a protein compound. The process of analysis can be done by conventional and computational methods. The advantages of using computational methods that can save time and cost efficiently. Rotation forest is an ensemble method for the classification to be combined with the PseudoSMR feature extraction. To make predictive model, data is needed in the form of protein interaction along with amino acid sequence that can be obtained from NCBI (National Center for Biotechnology Information). Feature extraction of the amio acid sequence will be transformed into a feature vector using PseudoSMR. The result of PsedoSMR by using paramter lg = {2,3,5,6,8,10}, which will be used as training data and test data in rotation forest, so there are 6 datasets. The best result for RF(PCA) occur when lg = 5 and for RF(IPCA) occur when lg = 8. The value of performance evaluation in the range 0.759436 to 0.793178 for RF(IPCA) while for RF(PCA) in the range 0.774837 to 0.812225. The highest evaluation value is obtained when the variable K = p / 3 where p is the length of the column. All calculations and prediction process using software R."

"ABSTRAKPada penelitian ini, dibahas mengenai konstruksi dan analisis terhadap sebuah model matematika penggunaan vaksinasi dengan kelas umur pada pencegahan penyakit tuberkulosis. Model tersebut dikonstruksi berdasarkan model SEIR dengan sistem persamaan diferensial biasa berdimensi sepuluh. Setiap populasi diklasifikasi berdasarkan kelompok usia anak-anak

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ABSTRACTIn this article, a new mathematical model for the transmission dynamics of tuberculosis TB with the intervention of vaccination in age structured susceptible population is designed and analyzed. The model is constructed as an SEIR based system of ten dimensional ordinary differential equation. Each population is then further classified according to its age class children."