Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal Ilmu Pendidikan
Ramadini, Suci
Unknown Affiliation
Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Neuroscience Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Peran Kader pada Program Bina Keluarga Balita (BKB) di Kampung KB Bangau Putih Kelurahan Parupuk Tabing Kota Padang Ramadini, Suci; Sunarti, Vevi
JFACE: Journal of Family, Adult, and Early Childhood Education Vol 1 No 4 (2019): JFACE: Journal of Family, Adult, and Early Childhood Education
Publisher : Penerbit Aksara Rentaka Siar (ARS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This background reseaarch is the participation of mothers has motivated to follow the BKB’s activity. The aim of the research is to see how the instructor influence on Bina Keluarga Balita(BKB) at Bangau Putih region Padang city. The influence of the instructor on BKB activity as a facilitator, motivator, and educator. This type of research is quantitative research which population is BKB members that are 50 persons and 33 of them become samples. The technique of collecting data is using questioner and the technique analysis is percentage. The research finding are 1) the research shows the instructor influence on BKB program at Bangau Putih region Padang city is well. 2) The research shows the instructor influence as a motivator on BKB program at Bangau Putih region Padang city categorize well. 3) The research shows the instructor influence as an educator on BKB program at Bangau Putih region Padang city categorize well. suggestion 1) the research suggests the instructors of BKB program at Bangau Putih region Padang city to keep their role as facilitator motivator and educator in the good program. 2) for DP3AP2KB to keep increasing program policies that will create the good program. 3) for the next researcher hopefully can be discussed this study deeper with different variable.
CLUSTER ANALYSIS OF OBESITY RISK LEVELS USING K-MEANS AND DBSCAN METHODS Geovani, Dite; Umari, Zainal; Ramadini, Suci
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.481

Abstract

Obesity is defined as excessive fat accumulation and abnormal accumulation of adipose tissue in the human body that poses health risks. The causes of obesity are multifactorial and include environmental and individual factors. Several factors that cause obesity include genetic, behavioral and environmental factors. Obesity causes various problems in various fields, including health, employment, demographics, economics and family. The problem of obesity has a significant impact on public health. Therefore, understanding and predicting the level of obesity risk is important in efforts to prevent and treat obesity risk. Data on eating habits, physical activity, and other factors associated with obesity levels in certain populations can provide an important basis for understanding obesity risk. This research clusters the risk of obesity to find hidden patterns in the data. The stages in this research consist of pre-processing, clustering, and analysis. The clustering methods used are K-means and DBSCAN. In clustering using the K-means method with a parameter value of k , results are obtained with the same pattern as clustering using the DBSCAN method with a parameter value of epsilon and a minimum sample . In clustering using the K-means method with a parameter value of k , Four clusters were formed which had different patterns. The clustering results obtained in this research can be used as an effort to prevent and treat the risk of obesity.
Cluster Analysis of Obesity Risk Levels Using K-Means And DBScan Methods Geovani, Dite; Umari, Zainal; Ramadini, Suci
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Obesity is defined as excessive fat accumulation and abnormal accumulation of adipose tissue in the human body that poses health risks. The causes of obesity are multifactorial and include environmental and individual factors. Several factors that cause obesity include genetic, behavioral and environmental factors. Obesity causes various problems in various fields, including health, employment, demographics, economics and family. The problem of obesity has a significant impact on public health. Therefore, understanding and predicting the level of obesity risk is important in efforts to prevent and treat obesity risk. Data on eating habits, physical activity, and other factors associated with obesity levels in certain populations can provide an important basis for understanding obesity risk. This research clusters the risk of obesity to find hidden patterns in the data. The stages in this research consist of pre-processing, clustering, and analysis. The clustering methods used are K-means and DBSCAN. In clustering using the K-means method with a parameter value of k , results are obtained with the same pattern as clustering using the DBSCAN method with a parameter value of epsilon and a minimum sample . In clustering using the K-means method with a parameter value of k , Four clusters were formed which had different patterns. The clustering results obtained in this research can be used as an effort to prevent and treat the risk of obesity.
Co-Authors Geovani, Dite Umari, Zainal Vevi Sunarti