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Journal : Jurnal Algoritma

Pendekatan Transfer Learning dengan InceptionResNetV2 dan Augmentasi MixUp untuk Peningkatan Klasifikasi Tumor Otak Mahendra, Randa; Laksana, Eka Angga; Sukenda, Sukenda
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2194

Abstract

Diagnosis of brain tumors such as Glioma, Meningioma, and Pituitary using MRI still faces challenges, including reliance on manual interpretation, long evaluation times, and the potential for human error. To address these issues, deep learning-based approaches offer efficient and accurate solutions. This study aims to develop a brain tumor classification model based on deep learning using the InceptionResNetV2 architecture with MixUp augmentation to improve model accuracy and generalization. The model was trained on 7,023 MRI images (Glioma: 1,621; Meningioma: 1,645; Pituitary: 1,757; No-tumor: 2,000), with MixUp proven effective in reducing overfitting and handling data imbalance. The proposed model achieved a highest accuracy of 99.70%, surpassing other models such as CNN with Image Enhancement (97.84%) \[1], Xception (98.00%) \[2], EfficientNet (98.00%) \[3], and ResNet50 (98.47%) \[4]. Evaluation was conducted using metrics including precision, recall, F1-score, as well as MSE, RMSE, and MAE, showing strong performance. These results support the use of transfer learning for medical image classification with limited datasets. This research demonstrates clinical application potential, particularly in improving diagnostic accuracy, speeding up evaluation processes, and reducing human error. Future recommendations include using more diverse datasets, real-world evaluation, and integration into Clinical Decision Support Systems (CDSS).
Implementasi Named Entity Recognition Untuk Botchat Customer Service PT. Afbe Cahaya Kreatif Badrudin, Deni Ramdani; Sukenda
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2394

Abstract

Perkembangan Kemajuan teknologi kecerdasan buatan, khususnya dalam bidang pemrosesan bahasa alami (Natural Language Processing/NLP), telah membuka peluang baru dalam pengembangan sistem layanan pelanggan yang lebih efisien. Penelitian ini bertujuan untuk mengimplementasikan Named Entity Recognition (NER) berbasis Bidirectional Encoder Representations from Transformers (BERT) dalam perancangan sistem customer service berbasis web di PT. Afbe Cahaya Kreatif. Teknologi ini memungkinkan sistem mengenali entitas penting dalam pertanyaan pelanggan, seperti nama produk, lokasi, dan jenis layanan, serta memahami konteks secara dua arah. Metode penelitian mencakup pengumpulan data percakapan, pelabelan entitas, pelatihan model, dan integrasi model ke dalam antarmuka layanan pelanggan. Hasil yang diharapkan adalah sistem otomatis yang mampu memberikan jawaban secara cepat, akurat, dan real-time. Dengan sistem ini, perusahaan dapat mengurangi beban kerja staf, menyediakan layanan 24/7, serta meningkatkan kepuasan pelanggan secara keseluruhan.