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All Journal JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Computer Networks, Architecture and High Performance Computing Yayasan Cita Cendikiawan Al Khwarizmi Journal Of Science Education And Management Business The Indonesian Journal of Computer Science
Aldi, Febri
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Journal : The Indonesian Journal of Computer Science

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Skin Cancer Segmentation On Dermoscopy Images Using Fuzzy C-Means Algorithm Aldi, Febri; Sumijan
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3797

Abstract

Millions of people around the world suffer from skin cancer, a common and sometimes fatal disease. Dermoscopy has become an effective diagnostic technique for skin cancer. Precise segmentation is essential for skin cancer diagnosis. Segmentation allows more precise analysis of dermoscopic images by defining the boundaries of the lesion and separating it from surrounding healthy tissue. Dermoscopy images served as a source of research data, and Fuzzy C-Means (FCM) segmentation techniques were used. FCM is a promising method and has received a lot of attention lately. FCM is able to distinguish the various components within the lesion and effectively separate the lesion from the surrounding area. As a result, the distribution of membership degree values of each pixel in the image for each cluster represents the segmentation results obtained through FCM. The FCM technique for segmenting dermoscopic images is expected to significantly improve the precision and effectiveness of skin cancer diagnosis.
Identifikasi Kanker Darah pada Gambar Apusan Darah Perifer (PBS) Menggunakan Ekstraksi Fitur HSV Nozomi, Irohito; Aldi, Febri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4177

Abstract

Blood cancer is a category of diseases that have an impact on the development and operation of blood cells. Due to the complexity and diversity of these diseases, proper diagnosis is required before starting treatment. Medical imaging techniques have undergone significant advances in recent years, especially in peripheral blood smear (PBS) image processing. The aim of this study was to uncover how important the extraction of PBS image features is for the diagnosis of blood cancer. Feature extraction is essential to detect anomalies in blood cells in terms of blood cancer detection. The method used is feature extraction based on hue and saturation values (HSV) and uses Machine Support Vector Machine (SVM) machine learning algorithms in classifying malignant and benign PBS images. PBS image data used in this study was 100 images, consisting of 50 malignant PBS images, and 50 benign PBS images. Through the application of HSV feature extraction techniques and PBS image analysis, SVM algorithms can uncover latent indicators of blood cancer and facilitate timely and precise diagnosis. With the SVM technique, classification accuracy can be achieved by 92%. These results demonstrate the potential effectiveness of this feature extraction method. Extraction of HSV features may alter the diagnosis of blood cancer with additional research and application in clinical settings.
Co-Authors Ahmad Junaidi Hafizh, M. Muhammad, Hammar Faris Novita, Triana nozomi, irohito Putra, Yeviki Maisyah Sentosa, Rio Bayu Soeheri, Soeheri Sumijan Surmayanti, Surmayanti Tajuddin, Muhammad Wadisman, Cendra Yuhandri Yuhandri, Yuhandri Yulasmi