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All Journal Jurnal Teknik Informatika (JUTIF) DECODE: Jurnal Pendidikan Teknologi Informasi Mohuyula : Jurnal Pengabdian Kepada Masyarakat
Habie, Khairul Fathan
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Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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Peningkatan Cyber Security Awareness Melalui Pelatihan Edukatif Kepada Siswa Sekolah Menengah Kejuruan Habie, Khairul Fathan; Putro, Aldibangun Pidekso; Yuliansyah, Herman; Riadi, Imam
Mohuyula : Jurnal Pengabdian Kepada Masyarakat Vol 3, No 2 (2024): Desember
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/mohuyula.3.2.46-53.2024

Abstract

Perkembangan teknologi informasi dan komunikasi telah mengalami pertumbuhan yang sangat pesat. Era digital membawa banyak kemudahan dan keuntungan, mulai dari akses informasi yang cepat, komunikasi yang efisien, hingga kemudahan layanan online yang mempermudah kehidupan sehari-hari. Sejalan dengan hal tersebut era digital juga menghadirkan tantangan baru dalam hal keamanan informasi atau cyber security. Keamanan informasi menjadi semakin krusial dengan meningkatnya penggunaan teknologi digital, membuat individu, organisasi, dan institusi rentan terhadap ancaman siber seperti peretasan, pencurian data, malware, dan serangan phishing.  Oleh karena itu, diperlukan pelatihan dengan tujuan untuk meningkatkan kesadaran dan pemahaman siswa SMK 1 Sedayu, tentang pentingnya kesadaran dan pemahaman cyber security.  Kegiatan program pemberdayaan umat (PRODAMAT) meliputi tahap persiapan, pelaksanaan, dan evaluasi. Kegiatan PRODAMAT dilakukan dengan metode ceramah dan diskusi tanya jawab. Peningkatan pemahaman siswa diukur dengan rangkaian pre-test dan post-test. Hasil yang diperoleh dari program pemberdayaan umat menunjukkan bahwa adanya peningkatan pemahaman pada indikator kesadaran “Sangat Paham” meningkat dari 31% menjadi 52%.
Effect of Learning Rate on VGG19 Model Architecture for Human Skin Disease Classification Habie, Khairul Fathan; Murinto, Murinto; Sunardi, Sunardi; Khusna, Arfiani Nur
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.576

Abstract

The skin is the largest external organ that serves to protect human internal organs and is very sensitive to various diseases, so early detection is very important to reduce the risk and increase the chance of recovery. This study aims to classify skin disease types using CNN algorithm with VGG19 architecture and learning rate adjustment to get a more optimal model, using a dataset from Kaggle consisting of 3,295 images with six classes, including several types of skin diseases and one healthy skin class. The preprocessing process includes dividing the data into training and testing sets, resizing the images to fit the VGG19 architecture, and normalization to scale the pixel values from 0-255 to a range of 0-1. The results show that using a learning rate of 0.00003 produces the best performance with 97.29% accuracy, 97.36% precision, 97.29% recall, and 97.30% F1-score. These findings confirm that the CNN algorithm with VGG19 architecture can classify skin disease types well.
Impact of Optimizer Selection on MobileNetV1 Performance for Skin Disease Detection Using Digital Images Habie, Khairul Fathan; Murinto, Murinto; Sunardi, Sunardi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4685

Abstract

Automatic detection of skin diseases using digital images is a growing field in the application of deep learning in the medical world, especially to help the early diagnosis process. One of the most widely used models is MobileNetV1 because it is lightweight and efficient in image processing. However, the performance of the model is greatly affected by the training configuration, including the type of optimizer used. This study aims to compare the effectiveness of six types of optimizers, namely SGD, RMSprop, Adam, Adadelta, Adagrad, Adamax, and Nadam in training MobileNetV1 models for human skin disease image classification. The model was trained on annotated skin image dataset with predetermined training parameters: batch size 32, learning rate of 0.0001, and 10 epochs. Performance evaluation was performed using accuracy metrics. The results obtained demonstrate that RMSprop performs best, with 99.10% accuracy, 99.14% precision, 99.10% recall, and a 99.10% F1-score. Adadelta showed the lowest performance consistently, with only 22.22% accuracy, 20.34% precision, 22.22% recall, and 18.42% F1-score. This finding confirms that the type of optimizer affects the effectiveness of model training, especially in medical image classification tasks. This research provides empirical insights that are useful in selecting the optimal optimizer for MobileNetV1 model implementation in the healthcare domain.
Co-Authors Arfiani Nur Khusna Herman Yuliansyah, Herman Imam Riadi Murinto Murinto Putro, Aldibangun Pidekso Sunardi Sunardi