Pengelompokan Kabupaten/Kota di Pulau Jawa Berdasarkan Kondisi Sosial Ekonomi Sebelum dan Setelah Memasuki Pandemi COVID-19

Prospek ekonomi Indonesia melemah secara signifikan dengan adanya pandemi COVID-19. Struktur perekonomian Indonesia secara spasial pada tahun 2020 didominasi oleh kelompok provinsi di Pulau Jawa yang memberikan kontribusi terhadap PDB sebesar 58,75 persen. Pandemi COVID-19 memiliki dampak sosial ekonomi terbesar di wilayah yang padat penduduk dikarenakan pembatasan sosial, termasuk Pulau Jawa sebagai pusat perekonomian Indonesia. Hal ini dapat mengindikasikan apabila perekonomian Pulau Jawa terkontraksi, maka perekonomian Indonesia ikut terkontraksi. Penelitian ini bertujuan untuk menganalisis kondisi sosial ekonomi di Pulau Jawa sebelum dan setelah memasuki pandemi COVID-19 dan mengelompokkan kabupaten/kota di Pulau Jawa berdasarkan kondisi sosial ekonomi melalui variabel Pertumbuhan Ekonomi, Tingkat Pengangguran Terbuka, jumlah angkatan kerja, jumlah sektor unggulan, pengeluaran per kapita, dan persentase penduduk miskin, serta membandingkannya ketika sebelum dan setelah memasuki pandemi COVID-19 dengan menggunakan metode Cluster Ensemble. Hasil yang didapatkan menunjukkan bahwa secara rata-rata terjadi penurunan pertumbuhan ekonomi dan jumlah sektor unggulan, serta peningkatan TPT, jumlah angkatan kerja, penduduk miskin, dan pengeluaran per kapita di Pulau Jawa setelah memasuki pandemi COVID-19. Dari 3 klaster yang terbentuk pada tahun 2019 dan 5 klaster pada tahun 2020, didapat bahwa secara umum pengelompokan tiap klaster tidak jauh berbeda, namun terdapat beberapa perbedaan karakteristik dan anggota klaster.

A link based cluster ensemble

tugas pengantar ilmu ekonomi

Penggerombolan Daerah 3T di Indonesia Berdasarkan Rasio Tenaga Kesehatan dengan Metode Penggerombolan Berhierarki dan Cluster Ensemble

Health is a major factor in community development. Inequality on health is most felt by people living in disadvantaged, outermost, and leading areas (3T) because of the difficulty of access to transportation and communication. Effective efforts are needed to achieve the optimal distribution of health services, one of which is by clustering 3T areas based on the ratio of health workers to see which areas are experiencing shortage of health workers and know the adequacy of the number of health workers spread in 3T areas. The object used in this research is 27 provinces 3T region in Indonesia and the applied statistical method is various hierarchical methods and Cluster Ensemble. Based on the results of this study, 3T area is divided into four clusters. The first cluster consists of 22 provinces and has good characteristics because all categories of the variables are in the medium category. The second and the third cluster consists of two provinces. The characteristics of the second cluster are good enough. The characteristics of the third cluster are not been good enough because there is one variable in the low category. The fourth cluster consists of one province and has characteristics that are not been good enough because there are several categories of the variables are in the low category.

Classification with incomplete training information using a cluster ensemble and low-rank matrix decomposition

The paper is devoted to solve the pattern recognition problem with incomplete learning data. The solution method, which combines similarity graph with Laplacian Regularization and collective clustering is proposed. The low-rank decomposition of co-association matrix for cluster ensemble is used, which allows to speed up the computations and keep memory. Experimental results on test tasks and on real hyperspectral image demonstrate the effectiveness of proposed method, including with noisy data.

Sc-GPE: A Graph Partitioning-Based Cluster Ensemble Method for Single-Cell

Clustering is an efficient way to analyze single-cell RNA sequencing data. It is commonly used to identify cell types, which can help in understanding cell differentiation processes. However, different clustering results can be obtained from different single-cell clustering methods, sometimes including conflicting conclusions, and biologists will often fail to get the right clustering results and interpret the biological significance. The cluster ensemble strategy can be an effective solution for the problem. As the graph partitioning-based clustering methods are good at clustering single-cell, we developed Sc-GPE, a novel cluster ensemble method combining five single-cell graph partitioning-based clustering methods. The five methods are SNN-cliq, PhenoGraph, SC3, SSNN-Louvain, and MPGS-Louvain. In Sc-GPE, a consensus matrix is constructed based on the five clustering solutions by calculating the probability that the cell pairs are divided into the same cluster. It solved the problem in the hypergraph-based ensemble approach, including the different cluster labels that were assigned in the individual clustering method, and it was difficult to find the corresponding cluster labels across all methods. Then, to distinguish the different importance of each method in a clustering ensemble, a weighted consensus matrix was constructed by designing an importance score strategy. Finally, hierarchical clustering was performed on the weighted consensus matrix to cluster cells. To evaluate the performance, we compared Sc-GPE with the individual clustering methods and the state-of-the-art SAME-clustering on 12 single-cell RNA-seq datasets. The results show that Sc-GPE obtained the best average performance, and achieved the highest NMI and ARI value in five datasets.