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All Journal SISFOTENIKA Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Informasi dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Jurnal Sistem Informasi SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Jurnal Pengabdian Masyarakat Bangsa Jurnal Pariwisata, Bisnis Digital dan Manajemen Journal Medical Informatics Technology International Journal of Computer Technology and Science
Daniati Uki Eka Saputri
Universitas Nusa Mandiri
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Journal Medical Informatics Technology

 1 
Optimization of Melanoma Skin Cancer Detection with the Convolutional Neural Network Kekal, Harming Puja; Saputri, Daniati Uki Eka
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.10

Abstract

Currently, skin cancer is a very dangerous disease for humans. Skin cancer is classified into many types such as Melanoma, Basal and Squamous cell carcinoma. In all types of cancer, melanoma is the most dangerous and unpredictable disease. Detection of melanoma cancer at an early stage is useful for effective treatment and can be used to classify types of melanoma cancer. New innovations in the classification and detection of skin cancer using artificial neural networks continue to develop to assist the medical and medical world in analyzing images precisely and accurately. The method used in this research is Convolutional Neural Network (CNN) with MobileNet model architecture. Skin cancer detection consists of five important stages, namely image database collection, preprocessing methods, augmentation data, model training and model evaluation. This evaluation was carried out using the MobileNet method with an accuracy of 88%.
Enhancing Skin Cancer Classification Using Optimized InceptionV3 Model Daniati Uki Eka Saputri; Nurul Khasanah; Aziz, Faruq; Taopik Hidayat
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.14

Abstract

Skin cancer is a disease that starts in skin cells characterized by uncontrolled growth that can attack skin tissue. Although it has a high cure rate if treated in a timely manner, a delay in diagnosis can have serious consequences. The use of computer technology, especially Artificial Intelligence (AI), has played an important role in improving health services, including in the context of skin cancer. New innovations in the classification and detection of skin cancer using artificial neural networks have led to significant improvements in diagnosis and treatment. One promising approach is using the InceptionV3 algorithm, which has high accuracy and is capable of processing high-resolution images. This study aims to implement InceptionV3 to classify two types of skin cancer, namely malignant and benign, with an emphasis on improving accuracy performance. With the pre-processing process, namely augmentation and the addition of several features, this study aims to provide accurate and efficient results in skin cancer classification. The results of this study can have a positive impact in increasing the accuracy of early detection of skin cancer, especially by future researchers.
Efficient Skin Lesion Detection using YOLOv9 Network Faruq Aziz; Saputri, Daniati Uki Eka
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.30

Abstract

Skin lesion detection plays a crucial role in dermatological diagnosis and treatment. In this study, we propose an efficient approach for skin lesion detection using the YOLOv9 network. Leveraging state-of-the-art deep learning techniques, our model demonstrates robust performance in accurately identifying various skin lesion types, including acne, atopic dermatitis, keratosis pilaris, leprosy, psoriasis, and wart. We conducted comprehensive experiments using a curated dataset comprising 2721 training images, 288 validation images, and 145 test images. The model was trained and evaluated based on standard metrics such as Precision, Recall, and mean Average Precision (mAP). Our results indicate promising detection accuracy, with an overall Precision of 60.5%, Recall of 86.0%, and an mAP of 81.4%. Class-wise analysis reveals varying levels of performance across different disease classes, highlighting the model's proficiency in detecting common dermatological conditions such as acne and wart lesions. Furthermore, we provide insights into potential challenges and limitations, including dataset size and class imbalance, and discuss avenues for future research to address these issues. Our study contributes to the advancement of AI-driven solutions for dermatological diagnosis and underscores the efficacy of the YOLOv9 network in skin lesion detection
Co-Authors Abdul Latif Achmad Baroqah Pohan Ade Suryadi Agus Salim Annida Purnamawati Aziz, Faruq Cahyanti, F. Lia Dwi Dwi, F Lia Dwiza Riana Eko Arif Riyanto, Eko Arif Faruq Aziz Faruq Aziz Firasari, Elly Fitra Septia Nugraha Gata, Windu Hasanah, Riyan Latifahul Juninisvianty, Tri Kekal, Harming Puja Khultsum, Umi Lili Novita Monikka Nur Winnarto Nugraha, Fitra Septia Nurfalah, Ridan Nurul Khasanah Nurul Khasanah Pratiwi, Risca Lusiana Rangga Pebrianto Risca Lusiana Pratiwi - Universitas Nusa Mandiri Rosiyadi, Didi Saelan, M. Rangga Ramadhan Salsabila, Unik Hanifah Sardiarinto Sardiarinto Seimahuira, Syarah Septian, Rendi Siti Masturoh Siti Masturoh siti nurlela Solly Aryza Sri Hadianti Taopik Hidayat Umi Khultsum Veithzal Rivai Zainal YANTO YANTO