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"[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.]"

"Kanker serviks merupakan penyakit kanker yang menyerang daerah kelamin. Kanker serviks ini disebabkan oleh infeksi Human Papillomavirus HPV. Terdapat lebih dari 100 tipe HPV yang sudah teridentifikasi, namun tidak semua tipe memiliki tingkat keganasan yang sama. Clustering dapat dilakukan dengan metode partisi atau metode hirarki. Kedua metode tersebut dapat dikombinasikan dengan metode partisi pada level pertama dan hirarki pada level kedua. Metode ini dikenal dengan nama hybrid clustering. Fase partisi dapat dilakukan menggunakan PAM, K-means, atau Fuzzy c-means. Kami memilih PAM pada level pertama dan Divisive Analysis DIANA pada level kedua untuk memperoleh hasil cluster yang lebih spesifik. Kami memilih hasil clustering yang meminimalkan nilai IDB. Pada penelitian ini dilakukan clustering pada 1252 barisan DNA HPV yang diperoleh dari GenBank. Hasil dari hybrid clustering diperoleh 15 clusters, dan hasil clustering menunjukkan data dapat dikelompokkan berdasarkan genusnya.

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Cervical cancer is a type of cancer that attacks the genital areas which may cause death. Cervical cancer is caused by Human Papillomavirus HPV which attacks uterus cells More than 100 types of HPV have been identified, but not all types have the same malignancy. One way to determine which types of HPV high risk lead to cervical cancer is by clustering or grouping data. Clustering can be achieved through partition or hierarchical method. Both clustering methods can be combined by processing partition algorithm in the first level and hierarchical in the second level.

This method is known as a hybrid clustering. The partition phase can be done by using PAM, K means, or Fuzzy c means methods. We selected Divisive Analysis DIANA algorithm for the second level in order to get more accurate clustering. We choose the clustering results which minimize the DBI value. In this work, we conduct the clustering on 1252 HPV DNA sequences data from GenBank. Results of hybrid clustering obtained 15 clusters, and the result shows that the data can be identified by genus."

"[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.]"

"penelitian ini diusulkan implementasi 2D dan 3D Set Partitioning In Hierarchical Trees (SPIHT) coding pada kompresi data ECG multi-lead. Implementasi SPIHT mereduksi tiga jenis redundansi yang umumnya terdapat pada sinyal electrocardiogram (ECG), yaitu redundansi intra-beat, inter-beat, dan inter-lead. Kami juga mengusulkan tiga teknik optimisasi untuk meningkatkan kinerja kompresi lebih lanjut dengan mengelompokkan sinyal ECG yang berasal dari beberapa lead, menyusun kembali posisi ECG cycle pada 2D ECG array (beat reordering), dan menormalisasikan amplitudo dari 2D ECG array dengan residual calculation. Beat reordering menyusun posisi beat pada 2D ECG array berdasarkan kemiripannya dengan beat terdekat. Penyusunan ini mengurangi variasi antar beat-beat yang berdekatan sehingga 2D ECG array mengandung lebih sedikit komponen frekuensi tinggi. Residual calculation mengoptimalkan penggunaan ruang penyimpanan lebih lanjut dengan meminimalkan variasi amplitudo dari 2D ECG array. Hasil eksperimen terhadap sejumlah record pada St Petersburg INCART 12-lead Arrhythmia Database menunjukkan bahwa metode yang diusulkan menghasilkan distorsi rendah pada rasio kompresi 8 dan 16. Hasil eksperimen juga memperlihatkan bahwa pendekatan 3D SPIHT memiliki kinerja kompresi yang lebih baik dibanding 2D SPIHT. Untuk mengevaluasi kualitas sinyal hasil rekonstruksi pada permasalahan klasifikasi, pada penelitian ini kinerja dari metode kompresi sinyal ECG dianalisis dengan cara membandingkan sinyal asli dengan sinyal hasil rekonstruksi pada dua permasalahan; pertama, klasifikasi sleep stage berdasarkan sinyal ECG; kedua, klasifikasi arrhythmia. Hasil eksperimen menunjukkan bahwa akurasi dari klasifikasi sleep stage dan klasifikasi arrhythmia menggunakan sinyal hasil rekonstruksi sebanding dengan menggunakan sinyal input. Metode yang diusulkan dapat mempertahankan karakteristik sinyal pada kedua permasalahan klasifikasi tersebut.

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In this study we proposed the implementation of 2D and 3D Set Partitioning In Hierarchical Trees (SPIHT) coding to a multi-lead ECG signal compression. The implementation of SPIHT coding decorrelates three types of redundancy that typically found on a multi-lead electrocardiogram (ECG) signal i.e. intra-beat, inter-beat, and inter-lead redundancies. We also proposed three optimization techniques to improve the compression performance further by grouping the ECG signal from precordial and limb leads, reordering the ECG cycles position in the 2D ECG array, and normalizing the amplitude of 2D ECG array by residual calculation. Beat reordering rearranged beat order in 2D ECG array based on the similarity between adjacent beats. This rearrangement reduces variances between adjacent beats so that the 2D ECG array contains less high frequency component. The residual calculation optimizes required storage usage further by minimizing amplitude variance of 2D ECG array.

Our experiments on selected records from St Petersburg INCART 12-lead Arrhythmia Database show that proposed method gives relatively low distortion at compression rate 8 and 16. The experimental results shows that 3D SPIHT approach gives better compression performance than 2D SPIHT. To evaluate the quality of reconstructed signal for classification task, we analyzed the performance of electrocardiogram (ECG) signal compression by comparing original and reconstructed signal on two problems. First, automatic sleep stage classification based on ECG signal; second, arrhythmia classification. Our experimental results showed that the accuracy of sleep stage classification and arrhythmia classification using reconstructed ECG signal from the proposed method is comparable to the original signal. The proposed method preserved signal characteristics for the automatic sleep stage and arrhythmia classification problems."

"Penerapan algoritma partisi k-means dalam metode HOPACH clustering dalam penelitian ini dilakukan untuk mengelompokkan barisan DNA virus ebola. Proses dimulai dengan mengumpulkan barisan DNA virus ebola yang diambil dari GenBank, kemudian dilakukan ekstraksi ciri menggunakan n-mers frequency. Hasil ekstraksi ciri barisan DNA tersebut dikumpulkan dalam sebuah matriks dan dilakukan normalisasi menggunakan normalisasi min-max dengan interval [0, 1] yang akan digunakan sebagai data masukan. Hasil pengelompokan barisan DNA virus ebola pada penelitian ini diperoleh 8 kelompok dengan nilai MSS (Mean Split Silhouette) minimum 0,50266. Proses clustering pada penelitian ini menggunakan program open source R.

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The implementation of k-means partitioning algorithm in HOPACH clustering method in this thesis is used to clustering DNA sequences of ebola viruses. The clustering process is started with collecting DNA sequences of ebola viruses that are taken from GenBank, then performing the extraction of DNA sequences using n-mers frequency. The extraction results are collected as a matrix and normalized using the min-max normalization with interval [0, 1] which will be used as an input data. As the results, we obtained 8 clusters with minimum MSS (Mean Split Silhouette) 0,50266. The clustering process in this thesis is using the open source program R."

"[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]"

"ABSTRAKMenganalisis populasi bakteri Streptococcus adalah penting karena spesies ini dapat menyebabkan karies gigi, periodental (plak), halitosis (bau mulut) dan masih banyak lagi masalah yang dapat ditimbulkan. Dalam tesis ini akan dibahas hubungan kekerabatan antara bakteri Streptococcus pada air liur dengan menggunakan pohon filogenetik dari metode agglomerative clustering. Dimulai dengan adanya barisan DNA bakteri Streptococcus yang diambil dari pangkalan data gen (GenBank) yang akan disejajarkan, proses pensejajaran yang dilakukan menggunakan Algoritma Needleman-Wuncsh untuk pensejajaran global. Hasil pensejajaran tersebut berupa skor optimal yang merupakan jarak antara dua barisan DNA bakteri Streptococcus. Skor-skor optimal dikumpulkan dalam satu matriks kemudian membuat pohon filogenetik dengan metode agglomerative clustering yang terdiri atas teknik single linkage,complete linkage dan average linkage. Pada setiap teknik, banyaknya kelompok sama dengan banyaknya individu spesies. Spesies yang paling mirip dikelompokkan sampai akhirnya kemiripan berkurang maka terbentuk kelompok tunggal. Hasil dari pengelompokan berupa pohon filogenetik dan cabang-cabang yang bergabung merupakan tingkatan jarak yang terbentuk. Semakin kecil jarak, maka semakin besar kemiripan spesies serta mengimplementasikannya dengan menggunakan perangkat lunak berbasis open source (Oktave).

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ABSTRACTAnalyzing population of Streptococcus bacteria is important because these spesies can cause dental caries, periodontal, halitosis (bad breath) and more problems.This paper will discuss the phylogenetically relation between the bacterium Streptococcus in saliva using a phylogenetic tree of agglomerative clustering methods. Starting with the bacterium Streptococcus DNA sequence obtained from the GenBank to be aligned, the alignment is performed using the Neddleman-Wuncsh Algorithm for global alignment. The alignment results in the optimal score or the distance between DNA sequence of the bacterium Streptococcus one another. Optimal scores collected in a single matrix. Agglomerative clustering technique consisting of single linkage, complete linkage and average linkage. In this technique the number of group sequal to the number of individual species. The most similar species is grouped until the similarity decreases and then formed a single group. Results of grouping is a phylogenetic tree and branches that join an established level of distance, that the smaller distance the more the similarity of the larger spesies implementation is using the Octave, an open source program."

"ABSTRAKVirus ebola EBOV merupakan virus patogen yang dapat menyebabkan kematian pada manusia dan makhluk primata. Data WHO pada Maret 2016 menunjukkan bahwa sebanyak 11,323 kasus dinyatakan meninggal dari 28,646 kasus yang dilaporkan. Kasus ini tidak hanya terjadi di Afrika, tetapi juga sudah terjadi di Itali, Spanyol, Amerika dan Inggris. Penting untuk menemukan kandidat obat yang mampu menghambat penyebaran virus ini. Salah satu protein yang dapat jadikan sebagai target untuk menginhibisi EBOV adalah viral protein 35 VP35 . Protein ini akan menghambat transkripsi dari interferon, sehingga produksi interferon akan menurun. Pada penelitian ini, digunakan pangkalan data Universal Natural Product Database UNPD sebagai inhibitor. Ada beberapa metode komputasi yang digunakan untuk menemukan kandidat obat dari senyawa UNPD, yaitu metode Protein-Ligand Interaction Fingerprint PLIF , titik farmakofor, simulasi penambatan dan dinamika molekul serta uji farmakologi senyawa. Hasil dari berbagai proses ini menyimpulkan bahwa ligan UNPD161456 merupakan senyawa terbaik yang dapat dijadikan kandidat obat untuk menginhibisi VP35.

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ABSTRACTEbola virus is a pathogen virus that can be pathogenic in the human and non human primate. Based on data from WHO in March 2016 there are more than 11,323 deadly cases from 28,646 reported cases. This virus has been separated to another country, such as Italy, Spain, and USA. This is a challenge for scientists to find a drug that can inhibit this virus. Viral protein VP35 is the important target from this virus that can hamper the transcription of interferon, and the production of interferon will be decreased. In this research, Universal Natural Product Database UNPD is used as inhibitor. There are several computational methods that can be used to find drug candidate from UNPD. The methods are Protein Ligand Interaction Fingerprint PLIF , pharmacophore feature, molecular docking, molecular dynamics and pharmacological properties. In the end, a ligand with the code UNPD161456 is the best drug candidate that can inhibit VP35. "

"Berdasarkan standar prevalensi stunting yang ditetapkan oleh WHO, yaitu sebesar 20%,tingkat prevalensi stunting di Indonesia masih cukup tinggi. Oleh sebab itu, pada tahun2018 pemerintah menetapkan 100 kabupaten prioritas penurunan angka stunting.Penentuan 100 kabupaten tersebut hanya didasarkan pada kriteria jumlah dan prevalensibalita stunting yang dibobot dengan tingkat kemiskinan provinsi (desa-kota). Akibatnya,akan tidak efektif apabila pemerintah memberikan alokasi APBN, APBD, dan perhatianyang merata pada 100 daerah prioritas tanpa melihat kondisi pada masing-masingkabupaten untuk indikator yang lain. Dengan demikian, diperlukan analisispengelompokan 100 kabupaten prioritas intervensi stunting pada tahun 2018 berdasarkanpada indikator-indikator yang telah ditetapkan oleh Tim Nasional PercepatanPenanggulangan Kemiskinan untuk melihat kondisi keparahan stunting. Analisispengelompokan ini diharapkan dapat dijadikan acuan bagi pemerintah dalam penentuankelompok kabupaten prioritas dan diharapkan pemerintah dapat mengambil kebijakanyang tepat sesuai dengan kondisi masing-masing kelompok. Banyaknya observasi yangdigunakan adalah 100 kabupaten prioritas intervensi stunting tahun 2018 dengan terdapatdelapan variabel numerik dan enam variabel kategorik. Adapun metode yang digunakanadalah metode Partitioning Around Medoids (PAM) dengan menggunakan Gowerdistance yang mampu menangani pengelompokan pada tipe data campuran. Hasil daripenelitian ini menunjukkan bahwa terbentuk lima kelompok kabupaten yang memilikikarakteristik masing-masing. Diperoleh bahwa Cluster 5 memiliki kondisi yang relatifpaling buruk di antara cluster lainnya untuk setiap indikator, sehingga sebaiknya menjadikelompok kabupaten prioritas pertama dalam penanganan kasus stunting. Cluster yangmenjadi prioritas kedua adalah cluster 4, prioritas ketiga adalah cluster 2, dan prioritaskeempat adalah cluster 3. Cluster 1 memiliki kondisi yang relatif paling baik di antaracluster lainnya, sehingga menjadi prioritas terakhir. Kabupaten-kabupaten yang berasaldari Provinsi Papua dan Provinsi NTT secara garis besar merupakan kabupatenkabupatenyang memiliki kondisi keparahan stunting yang buruk, dengan mayoritasmerupakan anggota cluster 2, cluster 4, dan cluster 5. Secara umum untuk lebihmeningkatkan upaya penurunan angka stunting pada 100 kabupaten prioritas, pemerintahperlu mengoptimalkan upaya penurunan angka kemiskinan, meningkatkan proporsipenduduk dengan perilaku BAB di jamban, meningkatkan akses masyarakat terhadap airbersih dan akses masyarakat terhadap sanitasi yang baik, meningkatkan jumlah posyanduper desa, dan meningkatkan ketersediaan jumlah dokter pada masing-masing kabupaten

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Based on the stunting prevalence standard set by WHO, which is 20%, the stunting

prevalence rate in Indonesia is still quite high. Therefore, in 2018 the government set 100

priority districts to reduce stunting rates. The determination of the 100 regencies only

based on the criteria of the number and prevalence of stunted children weighted by the

provincial (rural-urban) poverty rate. As a result, it will be ineffective if the government

allocates the state budget, regional budget, and equal attention to 100 priority areas

without looking at each district’s conditions for other indicators. Therefore, an analysis

of the 100 priority districts for stunting intervention needed in 2018 based on indicators

established by the National Team for the Acceleration of Poverty Reduction to see the

condition of the severity of stunting. This grouping analysis expected to use as a reference

for the government in determining priority district groups and expected the government

to take appropriate policies by each group’s conditions. The number of observations used

was 100 priority districts for stunting intervention in 2018 with eight numerical variables

and six categorical variables. The method used is the Partitioning Around Medoids (PAM)

method using a Gower distance that believed to handle grouping on mixed data types.

The results of this study indicate that five district groups formed that have their respective

characteristics. It found that cluster 5 had the relatively worst condition among the other

clusters for each indicator, so it should be the priority group in handling stunting cases.

The second priority cluster is cluster 4, the third priority is cluster 2, and the fourth priority

is cluster 3. Cluster 1 has the relatively best condition among other clusters, so it becomes

the last priority. Districts originating from Papua Province and East Nusa Tenggara

Province are generally districts that have reduced stunting severity, with the majority

being members of cluster 2, cluster 4, and cluster 5. In general, to further increase efforts

to reduce stunting rates at 100 priority districts, the government needs to optimize efforts

to reduce poverty, increase the proportion of the population with defecation behavior in

latrines, increase community access to clean water and community access to proper

sanitation, increase the number of posyandu per village, and increase the availability of

doctors in each district"

"Salah satu cara untuk mengetahui fungsi dari ekpresi gen (DNA/Protein) adalah dengan analisis kelompok (Clustering). Metode pengelompokan HOPACH mengkombinasikan agglomerative dan partisi. Partisi yang dapat digunakan antara lain PAM, SOM, dan K-Means yang termasuk dalam hard clustering. Dalam beberapa kasus karena beberapa hal pengelompokkan objek dengan hard clustering menjadi kurang tepat. Karena itu kemudian muncul teori himpunan fuzzy (kabur, tidak pasti) yang mendasari berkembangnya metode fuzzy clustering. Salah satu metode fuzzy clustering adalah metode Fuzzy c-means (FCM) yang merupakan perkembangan dari k-means. Hasil dari penerapan algoritma partisi fuzzy c-means dalam metode pengelompokan HOPACH adalah algortima pengelompokan dengan langkah-langkah: ekstraksi ciri dengan n-mers frecuency, normalisasi, partisi dengan FCM, menentukan kelompok terbaik dengan mencari nilai MSS minimum, ordering, dan collapsing. Hal ini dilakukan berulang kali sampai kriteria berhenti terpenuhi. Penerapan algoritma ini dilakukan dengan program R. Pada penerapan algoritma partisi dalam metode HOPACH clustering, langkah normalisasi tidak perlu dilakukan, karena FCM sendiri sudah mengatasi masalah adanya outliers. Kekurangan dari penerapan ini adalah running time program yang cukup lama untuk nilai batas toleransi yang kecil.

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One of the way to know the function of gene expression by clustering analysis. HOPACH clustering is combine thea agglomerative and partition method. The partition are PAM, SOM, and K-means which is part of hard clustering. In some cases because of the placement object in to a cluster with hard clustering can cause an error. So that is the reason why fuzzy set theory occurs and became the foundation of fuzzy clustering. One of the fuzzy clustering methods is Fuzzy C-means (FCM) which is developed from K-means.

The result from the implementation of FCM partitioning algorithm in HOPACH clustering method is the clustering algorithm which the steps are: characteristic extraction, normalization, partition using FCM, choosing the best cluster with the minimum MSS, ordering and collapsing. The process need done by iteration until the stopping criteria has reached. The implementation of this algorithm is use R program. In the implementation of FCM partitioning algorithm in HOPACH clustering method, normalization process can be deleted, because the FCM already sole the outliers problem. This disadvantage of this implementation is the running time program need quite along time for the small tolerance limits."