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All Journal Jurnal Pepadun
Favorisen Rosyking Lumbanraja
Department of Computer Science, Universitas Lampung
Computer Science & IT

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Two-Stage Convolutional Neural Network (CNN) Architectures for Breast Cancer Image Classification Admi Syarif; Adinda Aulia Sari; Wartariyus Wartariyus; Favorisen Rosyking Lumbanraja; Apri Candra
Jurnal Pepadun Vol. 6 No. 3 (2025): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v6i3.292

Abstract

Breast cancer remains one of the most common and deadly diseases among women globally. Early detection significantly increases the chances of patient recovery. The main objective of this research is to evaluate the performance of three Convolutional Neural Network (CNN) architectures, namely ResNet50, VGG16, and DenseNet201, for breast cancer image classification. In this study, there are two classification stages used: the first is to differentiate between normal and abnormal images, and the second is to distinguish between benign and malignant tumors. The dataset was obtained through the Kaggle website. It was then pre-processed using normalization and augmentation through flipping and rotation. After each CNN model was trained using transfer learning, its performance was evaluated using accuracy, precision, recall, and F1 score. In the Normal and Abnormal classification task, the DenseNet201 model outperformed other models with an accuracy of 91%. Meanwhile, ResNet50 showed the most optimal results in the Benign and Malignant classification with an accuracy of 83%.
Classification of Public Sentiment towards the Performance of the Ministry of Communication and Digital regarding Online Gambling Ika Rahma Alia; Favorisen Rosyking Lumbanraja; Aristoteles Aristoteles; Rico Andrian
Jurnal Pepadun Vol. 6 No. 3 (2025): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v6i3.295

Abstract

Online gambling is a social issue currently in the spotlight in Indonesia. Although the government, particularly the Ministry of Communication and Digital (Kemkomdigi), has taken various measures, such as blocking websites and conducting digital literacy campaigns, online gambling remains rampant and has sparked various public reactions. Social media, particularly Instagram, has become a public space where people express their opinions and sentiments regarding government performance. This study aims to classify public sentiment based on comments directed at the official Kemkomdigi Instagram account regarding the issue of online gambling. This study uses two machine learning algorithms, Random Forest and XGBoost, to compare the effectiveness of the models in classifying positive and negative sentiment. A total of 724 comments were collected and manually labeled by three annotators using a voting method. Preprocessing included cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Feature representation was performed using the TF-IDF method. The data was split with a 70:30 ratio and balanced using Random Oversampling. Model training used 10-fold cross-validation and hyperparameter tuning through GridSearchCV. The evaluation results showed that the tuned Random Forest performed the best, with an accuracy of 0.7082. These findings demonstrate that machine learning approaches, particularly Random Forest, are effective in automatically identifying public sentiment toward emerging public policy issues on social media.
Co-Authors Adinda Aulia Sari Admi Syarif Apri Candra Aristoteles Aristoteles Ika Rahma Alia Rico Andrian Wartariyus Wartariyus