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Journal : Faktor Exacta

Implementasi Graph Clustering Algorithm Modification Maximum Standard Deviation Reduction (MMSDR) dalam Clustering Provinsi di Indonesia Menurut Indikator Kesejahteraan Rakyat Nurfidah Dwitiyanti; Septian Wulandari; Noni Selvia
Faktor Exacta Vol 13, No 2 (2020)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v13i2.5863

Abstract

The population of Indonesia from year to year has increased. The increase in population must also be accompanied by increased economic growth in Indonesia. The increase in economic growth in Indonesia is marked by the reduction in the number of poor people in Indonesia. In addition, the increase in economic growth is reflected in the equitable distribution of public income in the country. Even though there are still many Indonesian people who are not yet prosperous in economic terms. To overcome, it is necessary to have clustering and characteristics of 34 provinces in Indonesia by implementing the Modification Maximum Standard Deviation Reduction (MMSDR) graph clustering algorithm. The data used are indicators of public welfare in 2017 obtained from the Central Statistics Agency. There are 9 indicators of community welfare used in this research. There are four stages in the MMSDR algorithm namely the "MST", "Subdivide", "Biggest Stepping" and "Create Clusters" processes. The results of this study can be seen from the distance between the nodes or between one province and another province produced 22 clusters. From the cluster results obtained using the MMSDR algorithm on welfare data, there are many clusters formed with cluster members formed at most two nodes (province). Keywords: MMSDR, Clustering, Welfare of People
Sifat Nilai Eigen Matriks Antiadjacency dari Graf Simetrik Noni selvia
Faktor Exacta Vol 10, No 2 (2017)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.301 KB) | DOI: 10.30998/faktorexacta.v10i2.1284

Abstract

Antiadjacency matrix is one of the ways to represent a directed graph . Let G be a directed graph with V(G)={v1, v2, . . ., vn} . The adjacency matrix of G is  a  matrix A=(aij)  of order n x n , with aij=1 if there is an edge from vi to vj , for i not equal j , otherwise aij  will equals 0. The matrix B= J - A is called the antiadjacency matrix of G, with J  is a matrix of order n x n   with all entries equal to 1. In this paper, it will show characteristic of eigenvalue of antiadjacency matrix of symmetric graph. Keywords : antiadjacency matrix, a symmetric graph, characteristic of eigenvalue
Penerapan Fuzzy C-Means Cluster dalam Pengelompokkan Provinsi Indonesia Menurut Indikator Kesejahteraan Rakyat Nurfidah Dwitiyanti; Noni Selvia; Finata Rastic Andrari
Faktor Exacta Vol 12, No 3 (2019)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v12i3.4526

Abstract

Metode fuzzy c means clustering adalah salah satu teknik pengelompokkan data dalam satu klaster ditentukan oleh pusat cluster yang akan menandai lokasi rata-rata untuk tiap klaster. Tujuan dari penelitian ini akan dibahas tentang penerapan metode fuzzy c means cluster dalam pengelompokkan provinsi Indonesia berdasarkan indikator kesejahteraan rakyat. Berdasarkan hasil analisis pengelompokkan fuzzy c means dengan 2 klaster diperoleh fungsi objektif yang konvergen pada iterasi ke-18 adalah sebesar 130,7085. Pada klaster 1 yang dikategorikan sebagai kelompok kurang sejahtera terdiri dari 18 propinsi dan klaster 2 adalah kelompok sejahtera, terdiri dari 16 propinsi.
Analisis Model Matematika dan Simulasi Pada Penyebaran Hepatitis Non HepA-E Akut di Indonesia Ristiawan, Rifki; Wahyudi, Farrell; Selvia, Noni
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.19670

Abstract