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All Journal JURNAL ILMIAH INFORMATIKA Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Qorry Aina Fitroh
Universitas Islam Negeri Sunan Kalijaga
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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PERAN ETHICAL HACKING DALAM MEMERANGI CYBERTHREATS Qorry Aina Fitroh; Bambang Sugiantoro
JURNAL ILMIAH INFORMATIKA Vol 11 No 01 (2023): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v11i01.6593

Abstract

The efforts to digitize and optimize various things in the modern world will certainly highlight issues related to cybersecurity such as data breaches, security breaches, and so on. Ethical hacking and its need in the future cannot be avoided. Ethical hacking technology is spreading in almost every aspect of life, especially the computer industry, which requires protection of important data and must be handled with the right technology. Ethical hacking aims to find vulnerabilities in security systems and discover potential data breaches. This contrasts with the common understanding of hacking, which is black hat hackers who damage systems with malicious intent and steal data and infect viruses. Ethical hacking is a way to combat and neutralize black hat hackers. Teaching ethical hacking is preparing professionals in the information security field with the tools and skills to combat and prevent cybersecurity threats. Teaching inexperienced people in information security with aggressive methods can be viewed as both beneficial and harmful. This is because the same methods are used by malicious hackers hence educating professionals in information security may be perceived as enhancing malicious hackers. Using the literature study method, this article discusses various issues related to ethical hacking.
Deep Transfer Learning untuk Meningkatkan Akurasi Klasifikasi pada Citra Dermoskopi Kanker Kulit Qorry Aina Fitroh; Shofwatul 'Uyun
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 2: Mei 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i2.6502

Abstract

Benign and malignant cancers are the most common skin cancer types. It is essential to know skin cancer symptoms with an early diagnosis to provide an appropriate treatment and reduce the mortality rate. Dermoscopic image is one of the diagnostic media that many researchers have developed. It provides more optimal results in computational-based diagnosis than visual detection. Deep learning and transfer learning are two models that have been used successfully in computational-based analysis, although optimization is still needed. In this study, transfer learning was used to separate dermoscopic images of skin cancer into two categories: benign and malignant. This study used 2,000 images to increase previous research’s accuracy conducted on the Kaggle public dataset containing 3,297 images. Two pretrained models, namely VGG-16 and residual network (ResNet)-50, were compared and used as feature extractors. Fine-tuning was conducted by adding a flatten layer, two dense layers with the ReLU activation function, and one dense layer with the Softmax activation function to classify images into two categories. Hyperparameter tuning on the optimizer, batch size, learning rate, and epoch were performed to get each model’s best performance parameter combination. Before hyperparameter tuning, the model was tested by resizing the input image using different sizes. The results of model testing on the VGG-16 model gave the best performance at an image size of 128 × 128 pixels with a combination of Adam parameters as an optimizer, batch size of 64, learning rate of 0.001, and epoch of 10 with an accuracy value of 91% and loss of 0.2712. The ResNet-50 model yielded better accuracy of 94% and a loss of 0.2198 using the optimizer parameter RMSprop, batch size of 64, learning rate of 0.0001, and epoch of 100. The results indicate that the proposed method provides good accuracies and can assist dermatologist in the early detection of skin cancer.
Co-Authors Bambang Sugiantoro Shofwatul ‘Uyun