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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

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.

Multi-Stage CNN: U-Net and Xcep-Dense of Glaucoma Detection in Retinal Images Desiani, Anita; Priyanta, Sigit; Ramayanti, Indri; Suprihatin, Bambang; Rio Halim, Muhammat; Geovani, Dite; Rayani, Ira
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.314

Glaucoma is a chronic neurological disease in the human eye where there is damage to the nerves which causes vision loss to blindness. Glaucoma can be detected by classifying retinal images. Several previous studies that classified glaucoma did not perform segmentation beforehand. Segmentation is needed to extract the features of the optic disc and optic cup from retinal images that are used to detect glaucoma. This study proposes two stages in the detection of glaucoma, namely the segmentation and classification stages. Segmentation is carried out using the U-Net architecture. Classification is done using a new architecture, namely Xcep-Dense. The Xcep-Dense architecture is a new architecture which is the result of a combination of the Xception and DenseNet architectures. At the segmentation stage, accuracy, recall, precision, and F1-score values are obtained above 90%. The Cohen’s kappa value has a value above 85% and loss below 20%. At the classification stage, accuracy and specification values were obtained above 85%, sensitivity and F1-score above 80%, and Cohen’s kappa above 70%. The predicted image obtained at the segmentation stage has a very similar appearance to the ground truth. Based on the results of the performance evaluation obtained, it shows that the method proposed in this study is feasible in detecting glaucoma.Glaucoma,

Combination of Image Enhancement and Double U-Net Architecture for Liver Segmentation in CT-Scan Images Fitri Brianna, Dwi; Indra Kesuma, Lucky; Geovani, Dite; Sari, Puspa
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.582

Liver cancer can be identified using CT-Scan liver image segmentation. Liver segmentation can be performed using CNN architecture like U-Net. However, the segmentation results using U-Net architecture are affected by image quality. Low image quality can affect the accuracy of segmentation results. This study proposes a combination of image enhancement and segmentation stages on CT-Scan liver images. Image enhancement is achieved by using a combination of CLAHE to enhance contrast and Bilateral Filter to reduce noise. The segmentation architecture proposed in this study is Double U-Net which is a development of U-Net architecture by adding a second U-Net block with the same structure as a single U-Net. The first U-Net is used to extract simple features, while the second U-Net is used to extract more complex features and enhance the segmentation results of the first U-Net. PSNR and SSIM measure the results of image enhancement. The PSNR is more than 40dB and the SSIM result is close to 1. These results show that the proposed image enhancement method can enhance the quality of original images. The segmentation results were measured by calculating accuracy, sensitivity, specificity, dice score, and IoU. The result of liver segmentation obtained 99% for accuracy, 98% for sensitivity, 99% for specificity, 98% for dice score, and 90% for IoU. This shows that liver segmentation using Double U-Net obtained good segmentation. Results of image enhancement and image segmentation show that the proposed method is very good for enhancing image quality and performing liver segmentation accurately.

Pemanfaatan teknologi smart talk: Media komunikasi berbasis artificial intelligence bagi siswa tunarungu SLBN Ogan Ilir Desiani, Anita; Suprihatin, Bambang; Gofar, Nuni; Ermatita, Ermatita; Amran, Ali; Geovani, Dite; Ayuputri, Niken
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 8, No 3 (2025): Agustus
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v8i3.2737

SLBN Ogan Ilir merupakan sekolah luar biasa di Desa Tanjung Pering, Indralaya Utara, Ogan Ilir, Sumatera Selatan, yang mendidik siswa berkebutuhan khusus, termasuk tuna rungu. Guru di SLBN Ogan Ilir menghadapi tantangan komunikasi karena harus menggunakan bahasa isyarat untuk berinteraksi dengan siswa tuna rungu. Guru yang tidak memiliki latar belakang yang sesuai dan memiliki keterbatasan dalam menggunakan bahasa isyarat. Keterbatasan ini menghambat komunikasi dan mengurangi efisiensi proses pembelajaran. Untuk mengatasi masalah tersebut, kegiatan pengabdian masyarakat ini memberikan pelatihan dan pendampingan penggunaan aplikasi Smart Talk. Smart Talk merupakan sebuah perangkat berbasis kecerdasan buatan yang dirancang untuk mendukung komunikasi antara guru dan siswa tuna rungu. Kegiatan ini terdiri dari tiga tahap: persiapan, penyampaian materi, serta praktik dan evaluasi. Hasil penelitian menunjukkan bahwa aplikasi ini meningkatkan keterampilan komunikasi guru dan memfasilitasi pembelajaran yang lebih efektif bagi siswa. Diharapkan aplikasi Smart Talk dapat diimplementasikan di SLB lainnya untuk meningkatkan kualitas pendidikan bagi siswa berkebutuhan khusus.

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

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

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

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.

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