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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Media Jurnal Informatika
Toni Arifin
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Adhirajasa Reswara Sanjaya
Computer Science & IT Decision Sciences, Operations Research & Management

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Perbandingan Arsitektur CNN Berbasis Transfer Learning untuk Klasifikasi pada BreastMNIST Sazila Azka Adzkia; Toni Arifin
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 2 (2025): Agustus 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i2.2025.192-200

Abstract

Breast cancer is one of the leading causes of death among women, especially in developing countries like Indonesia. Early detection is very important to increase the cure rate and decrease the mortality rate. This research aims to improve its ability to identify cancer tumors by maximizing the recall value and comparing various transfer learning-based Convolutional Neural Network (CNN) models to find the most optimal model. The CNN architectures studied in this research include MobileNetV2, ResNet50, VGG16, and AlexNet. All models were applied to the BreastMNIST dataset, which consists of ultrasound images with two classes, benign and malignant. Transfer learning was used to overcome the challenge of limited availability of pre-labeled medical image data. The model performance was thoroughly evaluated using accuracy, precision, recall, F1-score and Area Under Curve (AUC) metrics. The results showed that MobileNetV2 provided superior performance with an accuracy of 91.14%, recall of 94%, precision of 93%, F1-score of 94%, and AUC of 0.9607. These findings indicate that the transfer learning-based MobileNetV2 is highly effective in detecting malignant tumors and is the most optimal architecture in this study.
Emotion Detection in Indonesian Text Using the Logistic Regression Method Erfian Junianto; Mila Puspitasari; Salman Ilyas Zakaria; Toni Arifin; Ignatius Wiseto Prasetyo Agung
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5927

Abstract

Emotion detection in Indonesian text has become a crucial topic in the advancement of human–computer interaction and sentiment analysis on digital platforms. Despite its importance, challenges arise from the linguistic complexity and frequent use of slang in Indonesian text. This study aims to evaluate the performance of three classification models—Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—in detecting emotions from Indonesian text. The dataset comprises 1,000 texts categorized into four emotions: happy, sad, angry, and fear. Preprocessing steps included slang normalization, text cleaning, tokenization, stopword removal, and stemming, followed by TF-IDF weighting. Each model was trained and further optimized using ensemble bagging to improve classification performance. The optimized Logistic Regression model achieved the best performance, with an accuracy of 89%, precision of 0.90, recall of 0.89, F1-score of 0.89, and an average ROC-AUC score of 0.98. Both KNN and Naive Bayes models reached 81% accuracy after optimization, but their overall performance remained lower than Logistic Regression. The findings demonstrate that Logistic Regression is the most effective method for detecting emotions in Indonesian text, as it can effectively handle simple grammatical structures and slang variations. This study contributes to the development of emotion analysis models for Indonesian text, supporting applications in social computing and affective computing.
Co-Authors Erfian Junianto Ignatius Wiseto Prasetyo Agung Mila Puspitasari Salman Ilyas Zakaria Sazila Azka Adzkia