Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan dan Teknologi Indonesia
Umari, Zainal
Unknown Affiliation
Computer Science & IT Education Engineering

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

Found 3 Documents
Search

 1 
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.
Deteksi Anomali Sinyal Vibrasi pada Mesin Industri Menggunakan Autoencoder di PT. Pusri Palembang Umari, Zainal; Supardi, Julian
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 12 (2024): JPTI - Desember 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.553

Abstract

Analisa sinyal vibrasi merupakan metode penting untuk mendeteksi kondisi kesehatan mesin untuk mencegah kerusakan lebih lanjut dan mengurangi downtime. Pendekatan konvensional sering kali tidak efektif dalam menangkap pola kompleks pada sinyal vibrasi, sehingga dibutuhkan metode yang lebih canggih, seperti pembelajaran mesin. Penelitian ini bertujuan untuk mengembangkan model deteksi anomali pada sinyal vibrasi menggunakan autoencoder. Data vibrasi yang digunakan diperoleh dari tiga unit blower di PT. Pusri Palembang. Evaluasi dilakukan dengan membandingkan kemampuan model dalam mendeteksi anomali dan kondisi normal.  Hasil penelitian menunjukkan bahwa model autoencoder efektif mendeteksi anomali dengan akurasi tinggi serta keseimbangan optimal dalam mengidentifikasi kondisi mesin. Penelitian ini menawarkan solusi praktis bagi industri untuk meningkatkan efisiensi pemeliharaan dan keandalan peralatan.
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 Julian Supardi Ramadini, Suci