Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Amin Septianingsih
Dinas Kependudukan dan Pencatatan Sipil Kabupaten Tangerang
Decision Sciences, Operations Research & Management Education Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
ANALISIS K-MEANS CLUSTERING PADA PEMETAAN PROVINSI INDONESIA BERDASARKAN INDIKATOR RUMAH LAYAK HUNI Amin Septianingsih
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 3 No. 1 (2022): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v3i1.116

Abstract

Liveable houses, based on national and global have various indicators criteria, they are proper access to sanitation, adequate living space, building resilience, drinking water access, and living security. In addition, the government also pays attention to the housing ownership status variable. This research aims to map Indonesia provinces based on liveable houses indicator that consists of 9 variables, they are access to adequate housing, decent drinking water, electric lighting, decent sanitation, houses area <7.2 m2, the widest land floor, the widest bamboo wall, top of the widest fibers palm, house for rent/contract. This study uses K-Means Clustering analysis and coefficient silhouette width validation method to determine the level of cluster validation. The analysis results are 4 clusters, which cluster 1 consists of 10 members, cluster 2 consists of 19 members, cluster 3 consists of 3 members, and cluster 4 consists of 2 members. Cluster 1 is the lowest percentage for houses size variable with <7,2 m2, the widest fibers roof and ownership of rent/contract houses compared to other clusters. Cluster 2 has the highest percentage of liveable access variable and descent drinking water. Cluster 3 has the lowest percentage of liveable access, descent drinking water, electric lighting, and decent sanitation, while the highest percentage of houses size variable is <7,2 m2, The Widest Land Floor and Wall. Cluster 4 has the highest percentage of other variables of electric lighting variable, descent sanitation, the widest fibers roof, and ownership of temporary rent/contract houses. Meanwhile, has the lowest percentage of the widest land floor and bamboo wall, compared to other clusters
PEMETAAN KABUPATEN KOTA DI PROVINSI JAWA TIMUR BERDASARKAN TINGKAT KASUS PENYAKIT MENGGUNAKAN PENDEKATAN AGGLOMERATIF HIERARCHICAL CLUSTERING Amin Septianingsih
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 3 No. 2 (2022): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v3i2.139

Abstract

Health is a human right that is one of the indicators of welfare that must felt by all people following the ideals of the Indonesian people to realize a high degree of health. The high level of public health is one of the base capital for realization of the implementation of national development. One indicator of policy target evaluation in the health sector is the availability of adequate public health facilities and services. In addition, the level of the number of people suffering from disease complaints needs to be the attention of the government and the community to become the material for evaluating policy targets in the health sector. Based on data on the percentage of the population who have health complaints in 2020 obtained from the BPS of the Republic of Indonesia, East Java Province is ranked 7th, which is 32.8%. While the data in 2021 East Java Province is ranked 8th at 28.55%. This figure is quite high compared to other provinces so the East Java Provincial government needs to pay attention to reducing cases of disease complaints. Therefore, this study aims to analyze and map regencies and cities in East Java Province based on the distribution of the characteristics of the number of disease cases in each regency and city using the Agglomerative Hierarchical Clustering method. Based on the value of the cophenetic correlation coefficient, the average linkage method is the best method with the optimal number of clusters formed 4 clusters based on the cluster validation test using the connectivity validity index test, Dunn index, and silhouette. The results of this study can be used as a reference for the government of East Java Province in taking policies to improve national development planning in the health sector to increase the high level of public health
Co-Authors