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All Journal ComEngApp : Computer Engineering and Applications Journal KACANEGARA Jurnal Pengabdian pada Masyarakat Journal of Electronics, Electromedical Engineering, and Medical Informatics
Geovani, Dite
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : ComEngApp : Computer Engineering and Applications Journal

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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.
The Combination of Black Hat Transform and U-Net in Image Enhancement and Blood Vessel Segmentation in Retinal Images Darmo, Cahyo Pambudi; Kesuma, Lucky Indra; Geovani, Dite
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Diabetic Retinopathy (DR) is a disorder of the eye caused by damage to blood vessels in the retina. Damage to the retinal blood vessels can be analyzed by segmenting the blood vessels on the image. This study proposes a combination of image enhancement and blood vessel segmentation in retinal images. Retinal image enhancement is carried out using the black hat transform method to obtain a detailed view of blood vessels in retinal images. Segmentation of blood vessels in retinal images is carried out using the U-Net architecture. The results of image enhancement are measured using MSE and PSNR. This study has an MSE value below 0.05 and a PSNR above 90dB. The MSE and PSNR values obtained show that the black hat transform method is very good at image enhancement. Segmentation has an accuracy value above 0.95 and a sensitivity value above 0.85. In addition, the specificity value and f1-score are above 0.8. This shows that the proposed stages of image enhancement and blood vessel segmentation are able to accurately recognize blood vessel features in retinal images.
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 Ali Amran Anita Desiani Ayuputri, Niken Bambang Suprihatin Darmo, Cahyo Pambudi Ermatita - Fitri Brianna, Dwi Kesuma, Lucky Indra NUNI GOFAR Puspa Sari, Puspa Ramadini, Suci Ramayanti, Indri Rayani, Ira Rio Halim, Muhammat Sigit Priyanta Umari, Zainal