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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi Sistem Informasi dan Aplikasi Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Ilmu Komputer dan Informatika Jurnal Pengabdian Masyarakat Bangsa Teknomatika: Jurnal Informatika dan Komputer Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat
Setyaningsih, Putry Wahyu
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Journal : Building of Informatics, Technology and Science

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Pemanfaatan Deep Learning untuk Klasifikasi Kanker Kulit Menggunakan Few-shot Learning Berbasis Prototypical Networks dan Backbone EfficientNet-B0 Setianingsih, Wahyu; Setyaningsih, Putry Wahyu
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7245

Abstract

The utilization of Artificial Intelligence in the current era of technological development is increasingly popular, especially in the field of health. The increasing number of skin cancer cases globally is of particular concern today. Therefore, a classification model utilizing deep learning was developed to assist in the effective diagnosis process. However, data limitations and imbalances are often an issue in training skin cancer classification models. This research develops a skin cancer classification model using the Few-shot Learning approach with Prototypical Networks architecture and EfficientNet-B0 backbone. The research aims to develop an image-based skin cancer classification model and evaluate how effectively the model performs in classifying various types of skin lesions. Experimental results show that increasing k-shots has a positive impact on model accuracy. The best results were obtained in the 10-shot 15-query scheme with an accuracy value of 86.73% and supported by an ROC AUC value of 94%. This study proves that the few-shot learning approach with Prototypical Networks architecture and EfficientNet-B0 backbone is effective for skin cancer classification under limited dataset conditions. This model also has the potential to be an early diagnosis tool.
Analisis Perbandingan Metode Artificial Neural Network dan XGBoost untuk Prediksi Profit dari Data Transaksi Point of Sale Kurniawan, Panji; Setyaningsih, Putry Wahyu
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7399

Abstract

In the business world, profit is a key indicator of a company’s success, and predicting future profit is essential for strategic decision-making, such as inventory planning, pricing strategies, and marketing efforts. However, market fluctuations and dynamic consumer behavior often make profit prediction a significant challenge. With technological advancements, data mining methods have become increasingly utilized for analyzing such complex datasets, including Artificial Neural Networks (ANN) and XGBoost. This study explicitly aims to compare the performance of ANN and XGBoost in predicting profit based on transactional data from a Point of Sale (POS) system. ANN was selected for its ability to learn intricate and non-linear patterns in data, while XGBoost is known for its efficiency in processing large datasets and preventing overfitting through boosting and regularization techniques. The dataset consists of 44,348 transactions, with 80% used for training and 20% for testing. Results show that the ANN model achieved an R² of 0.9996 and a MAE of 1,359, outperforming the XGBoost model, which obtained an R² of 0.9978 and a MAE of 1,600. This significant difference indicates that ANN delivers more accurate predictions. ANN’s advantage lies in its capacity to develop complex internal representations of data, making it more responsive to subtle patterns in transactional behavior. These findings highlight the importance of choosing the appropriate model for profit prediction and demonstrate that ANN provides superior predictive accuracy, supporting more precise and data-driven strategic decisions for financial and sales management..
Co-Authors Ahsan, Moh Albert Yakobus Chandra Azfa Mutiara Ahmad Pabulo, Azfa Mutiara Ahmad Budianto, Alexius Endy Gani, Muhammad Irfan Abdul Ibrahim, Norshahila Indah Susilawati Mutaqin Akbar Panji Kurniawan Prasetyaningrum, Putri Taqwa Reny Yuniasanti Setianingsih, Wahyu Sitompul, Jessica Sandra Subagyo, Ibnu Rivansyah Suharjo, Imam Tutut Dewi Astuti Wiryanto, Yusuf Aji