Diversity of milk traits in 25 goat breeds

Goats are livestock that is mostly raised by small farmers in Indonesia because they are easier to raise. Apart from having the potential to become meat, several breeds of which are kept as milk-purpose. Milk traits of each breed differ from one another. Therefore, this study tried to observe genetic differences of 25 goat breeds with statistical approach. Information about milk traits from 25 goat breeds is obtained from published journal. Multidimensional preference analysis and average linkage cluster analysis were performed using SAS 9.4 to determine the differences in goat breeds from three traits, namely milk yield, fat content, and protein content. Multidimensional preference analysis was able to see the advantages of breeds from the three observed traits. Goat breeds with superior milk yields are Saanen, Camosciata delle Alpi, and Charmoisée. Sarda Primitiva, Sarda, Etawah Grade have high fat content in milk. In addition, Arsi-Bale and Somali have high protein content. Average linkage cluster analysis is able to observe the genetic relationship of goat breeds based on three traits. According to average linkage cluster analysis, we found four clusters for goat breeds in this study. With existing statistical approaches, we can evaluate genetic diversity in milk traits.

CLUSTERING OF PROVINCE IN INDONESIA BASED ON AQUACULTURE PRODUCTIVITY USING AVERAGE LINKAGE METHOD

Fisheries are one of the agricultural sub-sectors that play an important role in contributing to income figures for the state and the region because most of Indonesia's territory is water so that the fisheries sector is a sub-sector that is feasible to be developed in this country, one of which is through aquaculture. One of the efforts that can increase and maintain productivity in the aquaculture sector is to classify provinces that produce aquaculture production into groups based on the similarity of characteristics possessed by each province in Indonesia. In this study, clustering was carried out using cluster analysis using the average linkage method and based on the analysis results obtained showed that cluster 1 consists of 25 provinces, cluster 2 consists of 5 provinces, cluster 3 consists of 2 provinces, cluster 4 consists of 1 province, and cluster 5 consists of 1 province with a standard deviation value within a cluster of 11,729 and a standard deviation between clusters of 118,745.

Occupational Disease as the Bane of Workers’ Lives: A Study of Its Incidence in Slovakia. Part 2

The main objective of this article is to monitor the development of the number of occupational diseases related to selected physical factors in the working environment (noise, vibration and dust). Each region of Slovakia has its own specific social and economic conditions. Due to the existence of a strong correlation between the several regional variables observed, principal component analysis (PCA) was used to determine the new variables. Cluster analysis was used to group regions with similar characteristics. A dendrogram was created using the average linkage method, which illustrated the similarity of the regions studied. The value of the cophenetic correlation coefficient (CC = 0.90) confirms the validity of the average linkage method. The result of the cluster analysis is the grouping of the eight regions into five homogenic groups (clusters). An analysis of the data shows that Slovakia’s regional differences significantly influence the incidence of occupational diseases in individual regions. It is shown that, in Slovakia, the development of the number of occupational diseases has seen a favourable trend in the long term.

Comparison of Single Linkage, Complete Linkage, and Average Linkage Methods on Community Welfare Analysis in Cities and Regencies in East Java

Community welfare is one of the important points for a region and is also the essence of national development. The welfare of the people in Indonesia is fairly unequal, especially in East Java. To be able to map an area to the welfare of its people in East Java, one way that can be used is to use clustering. The hierarchical clustering method is one of the clustering methods for grouping data. In hierarchical clustering, single linkage, complete linkage, and average linkage methods are suitable methods for grouping data, which will compare the best method to use. The results of the calculation show that the average linkage method with three clusters is the best calculation with a silhouette index value of 0.6054, with the 1st cluster there are 23 regions, namely the city/district with the highest community welfare, the 2nd cluster there are 11 regions, namely cities/districts with moderate social welfare, and in the third cluster there are 4 regions, namely cities/districts with the lowest community welfare.

Analisa Hasil Pengelompokan Wilayah Kejadian Non-Kebakaran Menggunakan Agglomerative Hierachical Clustering di Semarang

Abstrak−Klasterisasi merupakan metode pengelompokan dari data yang sudah diketahui label kelasnya untuk menemukan klaster baru dari hasil observasi. Dalam klasterisasi banyak metode yaitu metode terpusat, hirarki, kepadatan dan berbasis kisi, namun dalam penelitian yang dilakukan ini dipilih metode berbasis hirarki. Metode hirarki ini bekerja melakukan pengelompokan objek dengan membentuk hirarki klaster namun bukan berarti selalu digambarkan dengan hirarki dalam organsasi. Dipilihnya Agglomerative Hierarchical Clustering dimana merupakan jenis dari bawah ke atas atau biasa disebut (bottom-up) dalam metode ini objek yang akan diuji dianggap sebagai objek tunggal sebagai klaster dan lalu dilakukan iterasi untuk menemukan klaster-klaster yang lebih besar. Data yang akan digunakan adalah data non-kebakaran pada Dinas Pemadam Kebakaran Kota Semarang ynng mana akan dilakukan pengelompokan wilayah penanganan non-kebakaran. Dinas Pemadam Kebakaran melakukan penanganan bukan hanya kebakaran saja namun ada banyak hal yang sebenarnya dapat ditangani oleh petugas pemadam kebakaran, kejadian non-kebakaran ada beberapa seperti evakuasi reptil, evakuasi kucing, penyelamatan korban kecelakaan dan lain sebagainya. Dari data non-kebakaran dari 16 kecamatan di Kota Semarang pada tahun 2019 akan dilakukan uji menggunakan tiga algoritma yaitu Single Lingkage, Average Linkage dan Complete Linkage . Adapun dari algoritma Single Linkage dilakukan prosedur pemusatan dari jarak terkecil antar objek data, algoritma Average Linkage dilakukan prosedur dari jarak rata-rata objek data, sedangkan jika algoritma Complete Linkage dilakukan prosedur pemusatan dari jarak yang terbesar. Implementasi dan visualiasi dari data uji coba yang dilakukan di penilitian ini menggunakan tools WEKA 3.8.4, Wakaito Environment Analysis for Knowledge atau yang biasa dikenal dengan WEKA ini merupakan software yang menggunakan bahasa pemrograman java. Dari dataset 380 data diambil sampel 100 data untuk diuji mengunakan WEKA menggunakan metode perhtungan jarak Manhattan Distance dengan 3 cluster. Hasil dari data uji coba dapat divisualisasikan dengan visualisasi dendogram pada fitur visualize tree dan jika dilakukan visualisasi dalam bentuk grafik dapat dilakukan menggunakan fitur visualize clusters assignment.

Pengelompokkan Tingkat Kriminalitas di Indonesia Menggunakan Algoritma Average Linkage

Crime needs to be analyzed and grouped so that the act does not cause harm either ecologically or psychologically. The statistical method that can be used to classify crime is the Average Linkage Algorithm. The study aims to group and analyze the characteristics of criminal cases in Indonesia. From the results of the analysis, 3 clusters were formed based on the average of each cluster. Cluster 1 consists of Aceh, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep. Bangka Belitung, Kep. Riau, West Java, Central Java, DI Yogyakarta, East Java, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, Maluku, North Maluku and Papua. Cluster 2 consists of North Sumatra while Cluster 3 consists of Metro Jaya. The grouping results are the basis of the government, apparatus, and the community in implementing the handling of criminal acts that occur in each cluster area so that prevention can minimize the losses caused by these crimes.

Morphology of pollen in Ferula genus (Apiaceae)

In this study, the pollen morphology of all Ferula species distributed throughout the country of Turkey was studied with light and scanning electron microscopy for the first time. The aim is to identify the pollen morphological characteristics of 23 Ferula species. The pollen is radially symmetrical, isopolar and tricolporate in all examined species. Pollen grains are prolate and perprolate with the polar axis ranging from 22.28 to 40.47 µm and the equatorial axis from 13.70 to 18.73 µm. Their polar shapes are triangular, triangular to subtriangular and circular to subcircular. Several types of exine ornamentations have been observed on pollen through the use of scanning electron microscopy. The dendrogram constructed by using Average Linkage of the examined data revealed two main groups. It was determined that some pollen characteristics are more useful for classification than others. In particular, P, E, the ratio of P/E (pollen shape) and ornamentation in the polar and equatorial views are the most valuable variables for discrimination the Ferula species.

Analisis Klaster untuk Pengelompokkan Kabupaten/Kota di Provinsi Maluku Berdasarkan Indikator Pendidikan dengan Menggunakan Metode Ward

Analisis klaster merupakan salah satu metode analisis yang bertujuan untuk mengklasterkan objek-objek berdasarkan kemiripan karakteristik yang dimilikinya. Metode yang digunakan pada analisis klaster yaitu metode hierarki. Secara umum metode hierarki dibagi menjadi 5 yaitu : single linkage, complete linkage, average linkage, Ward dan centroid dengan ukuran jarak euclidean. Tujuan dari penelitian ini untuk mengetahui hasil pengklasteran kabupaten/kota di Provinsi Maluku berdasarkan Indikator Pendidikan dengan menggunakan metode Ward. Hasil penelitian pengklasteran kabupaten/kota sebanyak 3 klaster, klaster pertama terdiri dari 2 kabupaten/kota yaitu Maluku Tengah dan Kota Ambon dengan tingkat pendidikan yang tinggi. Klaster kedua terdiri dari 4 kabupaten yaitu Kabupaten Seram Bagian Barat, Kabupaten Buru, Kabupaten Seram Bagian Timur dan Kabupaten Kepulauan Tanimbar dengan tingkat pendidikan rendah. Klaster ketiga terdiri dari 4 kabupaten/kota yaitu Kabupaten Maluku Tenggara, Kabupaten Maluku Barat Daya, Kabupaten Kepulauan Aru, Kabupaten Buru Selatan dan Kota Tual dengan tingkat pendidikan sedang.

Analisis kluster Kabupaten dan Kota di Provinsi Jambi berdasarkan indikator pembangunan

The purpose of this study is to classify Regencies and Cities in Jambi Province based on HDI, economic growth, percentage of population growth, percentage of poor population, percentage of open unemployment rate and Gini ratio. To answer these objectives the hierarchical method used is Average Linkage (Between Groups). The of analysis were obtained, two clusters could be formed, where group 1 consisted of 10 Regencies and Cities, group 2 consisted of 1 Regencies.