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All Journal The Indonesian Journal of Computer Science Jurnal Pengabdian Dian Mandala Journal of New Trends in Sciences Journal of Engineering, Electrical and Informatics
Mohamad Sofie
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Public Health

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Journal : Journal of Engineering, Electrical and Informatics

 1 
Classification of Skin Cancer Diseases Using KNN, CNN and SVM Methods Mohamad Sofie; Mohammad Rofi’i; Bayu Wahyudi
Journal of Engineering, Electrical and Informatics Vol. 5 No. 2 (2025): June: Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v5i2.3844

Abstract

According to the WHO, about 2 to 3 million non-melanoma.non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally every year, making up one out of every three cancers.globally each year, and account for one in every three cancers diagnosed.diagnosed. In Indonesia, skin cancer is listed as the cancer with the third highestincidence after uterine cervical and ovarian cancer, and breast cancer.Skin cancer can be detected with dermoscopy. Dermoscopy is a non-invasive diagnostic technique using optical magnification that allows visualization of morphologicHowever, this cannot be done optimally because it still relies on manual analysis so it cannot classify skin cancer types on larger datasets with potential errors and low accuracy. To accurately determine the type of skin cancer,a better classification method is needed. The purpose of this research is to determine the accuracy of skin cancer calcification using Convolutional Neural Network (CNN), support vector machine (SVM), K-nearest neighbor (KNN) models. The datasheet used amounted to 2,239 containing skin cancer images with class division 114 actinic keratosis, 376 basal cell carcinoma, 95 dermatofibroma, 438 melanoma, 357 nevus, 462 pigmented benign, 77 seborrheic keratosis, 181 squamos cell, 139 vascular lesion. The results showed that the convolutional neural network (CNN) algorithm model obtained a sensitivity of 92.59%, specificity of 99%, precision of 93%, F1-Score of 93.01%, and accuracy of 98.35%. For the KNN algorithm model, 57.77% sensitivity, 94.53% specificity, 64.25% precision, 55.99% F1-Score, and 90.45% accuracy were obtained. And for the SVM algorithm model, 61% sensitivity, 94.81% specificity, 70.23% precision, 61.26% F1-Score, and 91.17% accuracy were obtained.
Arduino Uno Based Audiometer Design Mohamad Sofie; Muhammad Rizky Aditya Firdaus; Bayu Wahyudi
Journal of Engineering, Electrical and Informatics Vol. 5 No. 2 (2025): June: Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v5i2.4828

Abstract

The ability to detect hearing impairment early is an important aspect of preventive efforts in the field of ear and hearing health. An audiometer is a device used to measure a person's hearing threshold by presenting sound stimuli at various frequencies and intensities. This research aims to design and build a simple digital audiometer that can be used as a means of early hearing screening at primary healthcare facilities. The developed audiometer system uses a microcontroller as the control center, equipped with a user interface based on an LCD screen and buttons for adjusting frequency and sound intensity. Sound output is channeled through headphones and calibrated within the frequency range of 125 Hz to 10,000 Hz with intensity levels from 0 dB to 10 - 100 dB. The value obtained from the measurements after making improvements on TP 1 (Input adapter) showed an error of 0.03% TP 2 (Nextion LCD input) at 5.12 V which is still within tolerance. TP 3 (Arduino Input) at 11.64 V which is still within tolerance. TP 4 (Input IC LM2956) at 11.66 V which is still within tolerance. The function of this audiometer tool was tested using a digital multimeter. The highest error value is at a frequency of 500 Hz, which is 0.152%. This is partly due to the tolerance values of the components used. Based on the data collection using a sound level meter, the furthest difference in sound intensity values at the point of 40 dB was found to be 3.46 dB. This is due to the influence of noise in the surrounding measurement area.
Co-Authors Affandy, Muhammad Eric Bayu Wahyudi Mohammad Rofi’i Muhammad Rizky Aditya Firdaus Muhammad Rofi’i Rusliadi Rusliadi