Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Dedikasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Lailis Syafa'ah
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Science and Technology for Community: Improving Web Programming Skills using Laravel Framework Agus Eko Minarno; Lailis Syafa'ah; Dwi Rahayu
Jurnal Dedikasi Vol. 18 No. 1 (2021): May
Publisher : Direktorat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/dedikasi.v18i1.15590

Abstract

The essence of technology is to help human work become easier, cost effective, save time more efficiently and on target. The use of technology that is studded with innovation provides real changes in every activity, including in the world of education. Even in some countries such as Australia and Hong Kong, technology has been introduced through basic programming since the age of 3 years. In Indonesia in 2016 the Minister of Communication and Information, Rudiantara, has discussed programming so that it is included in the elementary school curriculum. The introduction of technology at the elementary school level aims to prepare young people who are ready to face technology in the industrial era 4.0. This service activity is carried out in order to improve web programming capabilities using the Laravel framework. The hope is that by providing the ability to program using the Laravel framework, it can support academic education and increase employment opportunities with more capabilities.
Skin Lesion Image Classification using Convolutional Neural Network Lailis Syafa'ah; Ilham Hanami; Inda Rusdia Sofiani; Mohammad Chasrun
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i4.1353

Abstract

Classification of skin cancer is an important task to detect skin cancer and help with the treatment of skin cancer according to its type. There are many techniques in imaging used to classify skin cancer, one of the superior deep learning (DL) algorithms for classification is the Convolutional Neural Network (CNN). One type of skin cancer is dangerous is melanoma. In this study, CNN is proposed to help classify this type of skin cancer. The dataset consists of 15103 images of skin cancer pigments with 7 different types of skin cancer. These three tests proved malignant skin lesions can be classified with higher accuracy than non-melanocytic skin lesions which is 90% and performance evaluation shows melanocytic and non-melanocytic skin lesions detected with the highest accuracy. The tests conducted in this study grouped several types of skin diseases namely the first tests conducted using a group of melanocytic and non-melanocytic skin disease, second testing using groups of melanoma and melanocytic nevus diseases, and the final testing using malignant and benign. The proposed CNN model achieved significant performance with a best accuracy of 94% on the classification of melanoma and melanocytic nevus.
Co-Authors Agus Eko Minarno Dwi Rahayu Ilham Hanami Inda Rusdia Sofiani Mohammad Chasrun Wildan Suharso Yannu Indra Kusuma